Posts Tagged Opex
A Mature & Emerging Market Profitability Analysis … From Past, through Present & to the Future.
- I dedicate this Blog to David Haszeldine whom has been (and will remain) a true partner when it comes to discussing, thinking and challenging cost structures, corporate excesses and optimizing the Telco profitability.
- Opex growth & declining revenue growth is the biggest exposure to margin decline & profitability risk for emerging growth markets as well as mature mobile markets.
- 48 Major Mobile Market’s Revenue & Opex Growth have been analyzed over the period 2007 to 2013 (for some countries from 2003 to 2013). The results have been provided in an easy to compare overview chart.
- For 23 out of the 48 Mobile Markets, Opex have grown faster than Revenue and poses a substantial risk to Telco profitability in the near & long-term unless Opex will be better managed and controlled.
- Mobile Profitability Risk is a substantial Emerging Growth Market Problem where cost has grown much faster than the corresponding Revenues.
- 11 Major Emerging Growth Markets have had an Opex compounded annual growth rate between 2007 to 2013 that was higher than the Revenue Growth substantially squeezing margin and straining EBITDA.
- On average the compounded annual growth rate of Opex grew 2.2% faster than corresponding Revenue over the period 2007 to 2013. Between 2012 to 2013 Opex grew (on average) 3.7% faster than Revenue.
- A Market Profit Sustainability Risk Index (based on Bayesian inference) is proposed as a way to provide an overview of mobile markets profitability directions based on their Revenue and Opex growth rates.
- Statistical Analysis on available data shows that a Mobile Markets Opex level is driven by (1) Population, (2) Customers, (3) Penetration and (4) ARPU. The GDP & Surface Area have only minor and indirect influence on the various markets Opex levels.
- A profitability framework for understanding individual operators profit dynamics is proposed.
- It is shown that Profitability can be written as withbeing the margin, with ou and ru being the user dependent OpEx and Revenue (i.e., AOPU and ARPU), of the fixed OpEx divided by the Total Subscriber Market andis the subscriber market share.
- The proposed operator profitability framework provides a high degree of descriptive power and understanding of individual operators margin dynamics as a function of subscriber market share as well as other important economical drivers.
I have long & frequently been pondering over the mobile industry’s profitability.In particular, I have spend a lot of my time researching the structure & dynamics of profitability and mapping out factors that contributes in both negative & positive ways? My interest is the underlying cost structures and business models that drives the profitability in both good and bad ways. I have met Executives who felt a similar passion for strategizing, optimizing and managing their companies Telco cost structures and thereby profit and I have also met Executives who mainly cared for the Revenue.
Obviously, both Revenue and Cost are important to optimize. This said it is wise to keep in mind the following Cost- structure & Revenue Heuristics;
- Cost is an almost Certainty once made & Revenues are by nature Uncertain.
- Cost left Unmanaged will by default Increase over time.
- Revenue is more likely to Decrease over time than increase.
- Majority of Cost exist on a different & longer time-scale than Revenue.
In the following I will use EBITDA, which stands for Earnings Before Interest, Taxes, Depreciation and Amortization, as a measure of profitability and EBITDA to Revenue Ratio as a measure of my profit margin or just margin. It should be clear that EBITDA is a proxy of profitability and as such have shortfalls in specific Accounting and P&L Scenarios. Also according with GAAP (General Accepted Accounting Principles) and under IFRS (International Financial Reporting Standards) EBITDA is not a standardized accepted accounting measure. Nevertheless, both EBITDA and EBITDA Margin are widely accepted and used in the mobile industry as a proxy for operational performance and profitability. I am going to assume that for most purposes & examples discussed in this Blog, EBITDA & the corresponding Margin remains sufficiently good measures profitability.
While I am touching upon mobile revenues as an issue for profitability, I am not going to provide much thoughts on how to boost revenues or add new incremental revenues that might compensate from loss of mobile legacy service revenues (i.e., voice, messaging and access). My revenue focus in particular addresses revenue growth on a more generalized level compared to the mobile cost being incurred operating such services in particular and a mobile business in general. For an in-depth and beautiful treatment of mobile revenues past, present and future, I would like to refer to Chetan Sharma’s 2012 paper “Operator’s Dilemma (and Opportunity): The 4th Wave” (note: you can download the paper by following the link in the html article) on mobile revenue dynamics from (1) Voice (1st Revenue or Service Wave), (2) Messaging (2nd Revenue or Service Wave) to todays (3) Access (3rd Revenue Wave) and the commence to what Chetan Sharma defines as the 4th Wave of Revenues (note: think of waves as S-curves describing initial growth spurt, slow down phase, stagnation and eventually decline) which really describes a collection of revenue or service waves (i.e., S-curves) representing a portfolio of Digital Services, such as (a) Connected Home, (b) Connected Car, (c) Health, (d) Payment, (e) Commerce, (f) Advertising, (g) Cloud Services (h) Enterprise solutions, (i) Identity, Profile & Analysis etc.. I feel confident that adding any Digital Service enabled by Internet-of-Things (IoT) and M2M would be important inclusions to the Digital Services Wave. Given the competition (i.e., Facebook, Google, Amazon, Ebay, etc..) that mobile operators will face entering the 4th Wave of Digital Services Space, in combination with having only national or limited international scale, will make this area a tough challenge to return direct profit on. The inherent limited international or national-only scale appears to be one of the biggest barrier to turn many of the proposed Digital Services, particular with those with strong Social Media Touch Points, into meaningful business opportunities for mobile operators.
This said, I do believe (strongly) that Telecom Operators have very good opportunities for winning Digital Services Battles in areas where their physical infrastructure (including Spectrum & IT Architecture) is an asset and essential for delivering secure, private and reliable services. Local regulation and privacy laws may indeed turn out to be a blessing for Telecom Operators and other national-oriented businesses. The current privacy trend and general consumer suspicion of American-based Global Digital Services / Social Media Enterprises may create new revenue opportunities for national-focused mobile operators as well as for other national-oriented digital businesses. In particular if Telco Operators work together creating Digital Services working across operator’s networks, platforms and beyond (e.g., payment, health, private search, …) rather than walled-garden digital services, they might become very credible alternatives to multi-national offerings. It is highly likely that consumers would be more willing to trust national mobile operator entities with her or his personal data & money (in fact they already do that in many areas) than a multinational social-media corporation. In addition to the above Digital Services, I do expect that Mobile/Telecom Operators and Entertainment Networks (e.g., satellite, cable, IP-based) will increasingly firm up partnerships as well as acquire & merge their businesses & business models. In all effect this is already happening.
For emerging growth markets without extensive and reliable fixed broadband infrastructures, high-quality (& likely higher cost compared to today’s networks!) mobile broadband infrastructures would be essential to drive additional Digital Services and respective revenues as well as for new entertainment business models (other than existing Satellite TV). Anyway, Chetan captures these Digital Services (or 4th Wave) revenue streams very nicely and I recommend very much to read his articles in general (i.e., including “Mobile 4th Wave: The Evolution of the Next Trillion Dollars” which is the 2nd “4th Wave” article).
Back to mobile profitability and how to ensure that the mobile business model doesn’t breakdown as revenue growth starts to slow down and decline while the growth of mobile cost overtakes the revenue growth.
A good friend of mine, who also is a great and successful CFO, stated that “Profitability is rarely a problem to achieve (in the short term)”; “I turn down my market invest (i.e., OpEx) and my Profitability (as measured in terms of EBITDA) goes up. All I have done is getting my business profitable in the short term without having created any sustainable value or profit by this. Just engineered my bonus.”
Our aim must be to ensure sustainable and stable profitability. This can only be done by understanding, careful managing and engineering our basic Telco cost structures.
While most Telco’s tend to plan several years ahead for Capital Expenditures (CapEx) and often with a high degree of sophistication, the same Telco’s mainly focus on one (1!) year ahead for OpEx. Further effort channeled into OpEx is frequently highly simplistic and at times in-consistent with the planned CapEx. Obviously, in the growth phase of the business cycle one may take the easy way out on OpEx and focus more on the required CapEx to grow the business. However, as the time-scales for committed OpEx “lives” on a much longer period than Revenue (particular Prepaid Revenue or even CapEx for that matter), any shortfall in Revenue and Profitability will be much more difficult to mitigate by OpEx measures that takes time to become effective. In markets with little or no market investment the penalty can be even harsher as there is no or little OpEx cushion that can be used to soften a disappointing direction in profitability.
How come a telecom business in Asia, or other emerging growth markets around the world, can maintain, by European standards, such incredible high EBITDA Margins. Margin’s that run into 50s or even higher. Is this “just” a matter of different lower-cost & low GDP economies? Does the higher margins simply reflect a different stage in the business cycle (i.e., growth versus super-saturation)?, Should Mature Market really care too much about Emerging Growth Markets? in the sense of whether Mature Markets can learn anything from Emerging Growth Markets and maybe even vice versa? (i.e., certainly mature markets have made many mistakes, particular when shifting gears from growth to what should be sustainability).
Before all those questions have much of a meaning, it might be instructive to look at the differences between a Mature Market and an Emerging Growth Market. I obviously would not have started this Blog, unless I believe that there are important lessons to be had by understanding what is going on in both types of markets. I also should make it clear that I am only using the term Emerging Growth Markets as most of the markets I study is typically defined as such by economists and consultants. However from a mobile technology perspective few of those markets we tend to call Emerging Growth Markets can really be called emerging any longer and growth has slowed down a lot in most of those markets. This said, from a mobile broadband perspective most of the markets defined in this analysis as Emerging Growth Markets are pretty much dead on that definition.
Whether the emerging markets really should be looking forward to mobile broadband data growth might depend a lot on whether you are the consumer or the provider of services.
For most Mature Markets the introduction of 3G and mobile broadband data heralded a massive slow-down and in some cases even decline in revenue. This imposed severe strains on Mobile Margins and their EBITDAs. Today most mature markets mobile operators are facing a negative revenue growth rate and is “forced” continuously keep a razor focus on OpEx, Mitigating the revenue decline keeping Margin and EBITDA reasonably in check.
Emerging Markets should as early as possible focus on their operational expenses and Optimize with a Vengeance.
Well well let ‘s get back to the comparison and see what we can learn!
It doesn’t take to long to make a list of some of the key and maybe at times obvious differentiators (not intended to be exhaustive) between Mature and Emerging Markets are;
- Side Note: it should be clear that by today many of the markets we used to call emerging growth markets are from mobile telephony penetration & business development certainly not emerging any longer and as growing as they were 5 or 10 years ago. This said from a 3G/4G mobile broadband data penetration perspective it might still be fair to characterize those markets as emerging and growing. Though as mature markets have seen that journey is not per se a financial growth story.
Looking at the above table we can assess that Firstly: the straightforward (and possible naïve) explanation of relative profitability differences between Mature and Emerging Markets, might be that emerging markets cost structures are much more favorable compared to what we find in mature market economies. Basically the difference between Low and High GDP economies. However, we should not allow ourselves to be too naïve here as lessons learned from low GDP economies are that some cost structure elements (e.g., real estate, fuel, electricity, etc..) are as costly (some times more so) than what we find back in mature high/higher GDP markets. Secondly: many emerging growth market’s economies are substantially more populous & dense than what we find in mature markets (i.e., although it is hard to beat Netherlands or the Ruhr Area in Germany). Maybe the higher population count & population density leads to better scale than can be achieved in mature markets. However, while maybe true for the urban population, emerging markets tend to have substantially higher ratio of their population living in rural areas compared to what we find in mature markets. Thirdly: maybe the go-to-market approach in emerging markets is different from mature markets (e.g., subsidies, quality including network coverage, marketing,…) offering substantially lower mobile quality overall compared to what is the practice in mature markets. Providing poor mobile network quality certainly have been a recurring theme in the Philippines mobile industry despite the Telco Industry in Philippines enjoys Margins that most mature markets operators can only dream of. It is pretty clear that for 3G-UMTS based mobile broadband, 900 MHz does not have sufficient bandwidth to support the anticipated mobile broadband uptake in emerging markets (e.g., particular as 900MHz is occupied by 2G-GSM as well). IF emerging markets mobile operators will want to offer mobile data at reasonable quality levels (i.e., and the IF is intentional), sustain anticipated customer demand and growth they are likely to require network densification (i.e., extra CapEx and OpEx) at 2100 MHz. Alternative they might choose to wait for APT 700 MHz and drive an affordable low-cost LTE device ecosystem albeit this is some years ahead.
More than likely some of the answers of why emerging markets have a much better margins (at the moment at least) will have to do with cost-structure differences combined with possibly better scale and different go-to-market requirements more than compensating the low revenue per user.
Let us have a look at the usual suspects towards the differences between mature & emerging markets. The EBITDA can be derived as Revenue minus the Operational Expenses (i.e., OpEx) and the corresponding margin is Ebitda divided by the Revenue (ignoring special accounting effects that here);
EBITDA (E) = Revenue (R) – OpEx (O) and Margin (M) = EBITDA / Revenue.
The EBITDA & Margin tells us in absolute and relative terms how much of our Revenue we keep after all our Operational expenses (i.e., OpEx) have been paid (i.e., beside tax, interests, depreciation & amortization charges).
We can write Revenue as a the product of ARPU (Average Number of Users) times Number of Users N and thus the EBITDA can also be written as;
. We see that even if ARPU is low (or very) low, an Emerging Market with lot of users might match the Revenue of a Mature Market with higher ARPU and worse population scale (i.e., lower amount of users). Pretty simple!
But what about the Margin? , in order for an Emerging Market to have substantially better Margin than corresponding Mature Market at the same revenue level, it is clear that the Emerging Market’s OpEx (O) needs to be lower than that of a Mature markets. We also observe that if the Emerging Market Revenue is lower than the Mature Market, the corresponding Opex needs to be even lower than if the Revenues were identical. One would expect that lower GDP countries would have lower Opex (or Cost in general) combined with better population scale is really what makes for a great emerging market mobile Margins! … Or is it ?
A Small but essential de-tour into Cost Structure.
Some of the answers towards the differences in margin between mature and emerging markets obviously lay in the OpEx part or in the Cost-structure differences. Let’s take a look at a mature market’s cost structure (i.e., as you will find in Western & Eastern Europe) which pretty much looks like this;
With the following OpEx or cost-structure elements;
- Usage-related OpEx: typically take up between 10% to 35% of of the total OpEx with an average of ca. 25%. On average this OpEx contribution is approximately 17% of the revenue in mature European markets. Trend wise it is declining. Usage-based OpEx is dominated by interconnect & roaming voice traffic and to a less degree of data interconnect and peering. In a scenario where there is little circuit switched voice left (i.e., the ultimate LTE scenario) this cost element will diminish substantially from the operators cost structure. It should be noted that this also to some extend is being influenced by regulatory forces.
- Market Invest: can be decomposed into Subscriber Acquisition Cost (SAC), i.e., “bribing” the customers to leave your competitor for yourself, Subscriber Retention Cost (SRC), i.e., “bribing” your existing (valuable) customers to not let them be “bribed” by you’re a competitor and leave you (i.e., churn), and lastly Other Marketing spend for advertisement, promotional and so forth. This cost-structure element contribution to OpEx can vary greatly depending on the market composition. In Europe’s mature markets it will vary from 10% to 31% with a mean value of ca. 23% of the total OpEx. On average it will be around 14% of the Revenue. It should be noted that as the mobile penetration increases and enter into heavy saturation (i.e., >100%), SAC tends to reduce and SRC will increase. Further in markets that are very prepaid heavy SAC and SRC will naturally be fairly minor cost structure element (i.e., 10% of OpEx or lower and only a couple of % of Revenue). Profit and Margin can rapidly be influenced by changes in the market invest. SAC and SRC cost-structure elements will in general be small in emerging growth markets (compared to corresponding mature markets).
- Terminal-equipment related OpEx: is the cost associated by procuring terminals equipment (i.e, handsets, smartphones, data cards, etc.). In the past (prior to 2008) it was fairly common that OpEx from procuring and revenues from selling terminals were close to a zero-sum game. In other words the cost made for the operator of procuring terminals was pretty much covered by re-selling them to their customer base. This cost structure element is another heavy weight and vary from 10% to 20% of the OpEx with an average in mature European markets of 17%. Terminal-related cost on average amounts to ca. 11% of the Revenue (in mature markets). Most operators in emerging growth markets don’t massively procure, re-sell and subsidies handsets, as is the case in many mature markets. Typically handsets and devices in emerging markets will be supplied by a substantial 2nd hand gray and black market readily available.
- Personnel Cost: amounts to between 6% to 15% of the Total OpEx with a best-practice share of around the 10%. The ones who believe that this ratio is lower in emerging markets might re-think their impression. In my experience emerging growth markets (including the ones in Eastern & Central Europe) have a lower unit personnel cost but also tends to have much larger organizations. This leads to many emerging growth markets operators having a personnel cost share that is closer to the 15% than to the 10% or lower. On average personnel cost should be below 10% of revenue with best practice between 5% and 8% of the Revenue.
- Technology Cost (Network & IT): includes all technology related OpEx for both Network and Information Technology. Personnel-related technology OpEx (prior to capitalization ) is accounted for in the above Personnel Cost Category and would typically be around 30% of the personnel cost pending on outsourcing level and organizational structure. Emerging markets in Central & Eastern Europe historical have had higher technology related personnel cost than mature markets. In general this is attributed to high-quality relative low-cost technology staff leading to less advantages in outsourcing technology functions. As Technology OpEx is the most frequent “victim” of efficiency initiatives, lets just have a look at how the anatomy of the Technology Cost Structure looks like:
- Technology Cost (Network & IT) – continued: Although, above Chart (i.e., taken from my 2012 Keynote at the Broadband MEA 2012, Dubai “Ultra-efficient network factory: Network sharing and other means to leapfrog operator efficiencies”) emphasizes a Mature Market View, emerging markets cost distribution does not differ that much from the above with a few exceptions. In Emerging Growth Markets with poor electrification rates diesel generators and the associated diesel full will strain the Energy Cost substantially. As the biggest exposure to poor electrical grid (in emerging markets) in general tend to be in Rural and Sub-Urban areas it is a particular OpEx concern as the emerging market operators expands towards Rural Areas to capture the additional subscriber potential present there. Further diesel fuel has on average increased with 10% annually (i..e, over the least 10 years) and as such is a very substantial Margin and Profitability risk if a very large part of the cellular / mobile network requires diesel generators and respective fuel. Obviously, “Rental & Leasing” as well as “Service & Maintenance” & “Personnel Cost” would be positively impacted (i.e., reduced) by Network Sharing initiatives. Best practices Network Sharing can bring around 35% OpEx savings on relevant cost structures. For more details on benefits and disadvantages (often forgotten in the heat of the moment) see my Blog “The ABC of Network Sharing – The Fundamentals”. In my experience one of the greatest opportunities in Emerging Growth Markets for increased efficiency are in the Services part covering Maintenance & Repair (which obviously also incudes field maintenance and spare part services).
- Other Cost: typically covers the rest of OpEx not captured by the above specific items. It can also be viewed as overhead cost. It is also often used to “hide” cost that might be painful for the organization (i.e., in terms of authorization or consequences of mistakes). In general you will find a very large amount of smaller to medium cost items here rather than larger ones. Best practices should keep this below 10% of total OpEx and ca. 5% of Revenues. Much above this either means mis-categorization, ad-hoc projects, or something else that needs further clarification.
So how does this help us compare a Mature Mobile Market with an Emerging Growth Market?
As already mentioned in the description of the above cost structure categories particular Market Invest and Terminal-equipment Cost are items that tend to be substantially lower for emerging market operators or entirely absent from their cost structures.
Lets assume our average mobile operator in an average mature mobile market (in Western Europe) have a Margin of 36%. In its existing (OpEx) cost structure they spend 15% of Revenue on Market Invest of which ca. 53% goes to subscriber acquisition (i.e., SAC cost category), 40% on subscriber retention (SRC) and another 7% for other marketing expenses. Further, this operator has been subsidizing their handset portfolio (i.e., Terminal Cost) which make up another 10% of the Revenue.
Our Average Operator comes up with the disruptive strategy to remove all SAC and SRC from their cost structure and stop procuring terminal equipment. Assuming (and that is a very big one in a typical western European mature market) that revenue remains at the same level, how would this average operator fare?
Removing SAC and SRC, which was 14% of the Revenue will improve the Margin adding another 14 percentage points. Removing terminal procurement from its cost structure leads to an additional Margin jump of 10 percentage points. The final result is a Margin of 60% which is fairly close to some of the highest margins we find in emerging growth markets. Obviously, completely annihilating Market Invest might not be the most market efficient move unless it is a market-wide initiative.
Albeit the example might be perceived as a wee bit academic, it serves to illustrate that some of the larger margin differences we observe between mobile operators in mature and emerging growth markets can be largely explain by differences in the basic cost structure, i..e, the lack of substantial subscriber acquisition and retention costs as well as not procuring terminals does offer advantages to the emerging market business model.
However, it also means that many operators in emerging markets have little OpEx flexibility, in the sense of faster OpEx reduction opportunities once mobile margin reduces due to for example slowing revenue growth. This typical becomes a challenge as mobile penetration starts reaching saturation and as ARPU reduces due to diminishing return on incremental customer acquisition.
There is not much substantial OpEx flexibility (i..e, market invest & terminal procurement) in Emerging Growth Markets mobile accounts. This adds to the challenge of avoiding profitability squeeze and margin exposure by quickly scaling back OpEx.
This is to some extend different from mature markets that historically had quiet a few low hanging fruits to address before OpEx efficiency and reduction became a real challenge. Though ultimately it does become a challenge.
Back to Profitability with a Vengeance.
So it is all pretty simple! … leave out Market Invest and Terminal Procurement … Then add that we typically have to do with Lower GDP countries which conventional wisdom would expect also to have lower Opex (or Cost in general) combined with better population scale .. isn’t that really what makes for a great emerging growth market Mobile Margin?
Hmmm … Albeit Compelling ! ? … For the ones (of us) who would think that the cost would scale nicely with GDP and therefor a Low GDP Country would have a relative Lower Cost Base, well …
- In the Chart above the Y-axis is depicted with logarithmic scaling in order to provide a better impression of the data points across the different economies. It should be noted that throughout the years 2007 to 2013 (note: 2013 data is shown above) there is no correlation between a countries mobile Opex, as estimated by Revenue – EBITDA, and the GDP.
Well … GDP really doesn’t provide the best explanation (to say the least)! … So what does then?
I have carried out multi-linear regression analysis on the available data from the “Bank of America Merrill Lynch (BoAML) Global Wireless Matrix Q1, 2014” datasets between the years 2007 to 2013. The multi-linear regression approach is based on year-by-year analysis of the data with many different subsets & combination of data chosen including adding random data.
I find that the best description (R-square 0.73, F-Ratio of 30 and p-value(s) <0.0001) of the 48 country’s data on Opex. The amount of data points used in the multi-regression is at least 48 for each parameter and that for each of the 7 years analyzed. The result of the (preliminary) analysis is given by the following statistically significant parameters explaining the Mobile Market OpEx:
- Population – The higher the size of the population, proportional less Mobile Market Opex is spend (i.e., scale advantage).
- Penetration – The higher the mobile penetration, proportionally less Mobile Market Opex is being spend (i.e., scale advantage and the incremental penetration at an already high penetration would have less value thus less Opex should be spend).
- Users (i..e., as measured by subscriptions) – The more Users the higher the Mobile Market Opex (note: prepaid ratio has not been found to add statistical significance).
- ARPU (Average Revenue Per User) – The higher the ARPU, the higher will the Mobile Market Opex be.
If I leave out ARPU, GDP does enter as a possible descriptive candidate although the overall quality of the regression analysis suffers. However, it appears that the GDP and ARPU cannot co-exist in the analysis. When Mobile Market ARPU data are included, GDP becomes non-significant. Furthermore, a countries Surface Area, which I previously believed would have a sizable impact on a Mobile Market’s OpEx, also does not enter as a significant descriptive parameter in this analysis. In general the Technology related OpEx is between 15% to 25% (maximum) of the Total OpEx and out that possible 40% to 60% would be related to sites that would be needed to cover a given surface area. This might no be significant enough in comparison to the other parameters or simply not a significant factor in the overall country related mobile OpEx.
I had also expected 3G-UMTS to have had a significant contribution to the Opex. However this was not very clear from the analysis either. Although in the some of the earlier years (2005 – 2007), 3G does enter albeit not with a lot of weight. In Western Europe most incremental OpEx related to 3G has been absorb in the existing cost structure and very little (if any) incremental OpEx would be visible particular after 2007. This might not be the case in most Emerging Markets unless they can rely on UMTS deployments at 900 MHz (i.e., the traditional GSM band). Also the UMTS 900 solution would only last until capacity demand require the operators to deploy UMTS 2100 (or let their customers suffer with less mobile data quality and keep the OpEx at existing levels). In rural areas (already covered by GSM at 900 MHz) the 900 MHz UMTS deployment option may mitigate incremental OpEx of new site deployment and further encourage rural active network sharing to allow for lower cost deployment and providing rural populations with mobile data and internet access.
The Population Size of a Country, the Mobile Penetration and the number of Users and their ARPU (note last two basically multiplies up to the revenue) are most clearly driving a mobile markets Opex.
Philippines versus Germany – Revenue, Cost & Profitability.
Philippines in 2013 is estimated to have a population of ca. 100 Million compared to Germany’s ca. 80 Million. The Urban population in Germany is 75% taking up ca. 17% of the German surface area (ca. 61,000 km2 or a bit more than Croatia). Comparison this to Philippines 50% urbanization that takes up up only 3% (ca. 9,000 km2 or equivalent to the surface area of Cyprus). Germany surface area is about 20% larger than Philippines (although the geographies are widely .. wildly may be a better word … different, with the Philippines archipelago comprising 7,107 islands of which ca. 2,000 are inhabited, making the German geography slightly boring in comparison).
In principle if all I care about is to cover and offer services to the urban population (supposedly the ones with the money?) I only need to cover 9 – 10 thousand square kilometer in the Philippines to capture ca. 50 Million potential mobile users (or 5,000 pop per km2), while I would need to cover about 6 times that amount of surface area to capture 60 million urban users in Germany (or 1,000 pop per km2). Even when taking capacity and quality into account, my Philippine cellular network should be a lot smaller and more efficient than my German mobile network. If everything would be equal, I basically would need 6 times more sites in Germany compared to Philippines. Particular if I don’t care too much about good quality but just want to provide best effort services (that would never work in Germany by the way). Philippines would win any day over Germany in terms of OpEx and obviously also in terms of capital investments or CapEx. It does help the German Network Economics that the ARPU level in Germany is between 4 times (in 2003) to 6 times (in 2013) higher than in Philippines. Do note that the two major Germany mobile operators covers almost 100% of the population as well as most of the German surface area and that with a superior quality of voice as well as mobile broadband data. This does not true hold true for Philippines.
In 2003 a mobile consumer in Philippines would spend on average almost 8 US$ per month for mobile services. This was ca. 4x lower than a German customer for that year. The 2003 ARPU of the Philippines roughly corresponded to 10% of the GDP per Capita versus 1.2% of the German equivalent. Over the 10 Years from 2003 to 2013, ARPU dropped 60% in Philippine and by 2013 corresponded to ca. 1.5% of GDP per Capita (i.e., a lot more affordable propositions). The German 2013 ARPU to GDP per Capita ratio was 0.5% and its ARPU was ca. 40% lower than in 2003.
The Philippines ARPU decline and Opex increase over the 10 year period led to a Margin drop from 64% to 45% (19% drop!) and their Margin is still highly likely to fall further in the near to medium-term. Despite the Margin drop Philippines still made a PHP26 Billion more EBITDA in 2013 than compared to 2003 (ca. 45% more or equivalent compounded annual growth rate of 3.8%).
- Germany had ca. 3x more mobile subscribers compared to Philippines.
- German Mobile Revenue was 14x higher than Philippines.
- German EBITDA was 9x higher than that of Philippines.
- German OpEx was 23x higher than that of Philippines Mobile Industry.
- Mobile Margin of the Philippines was 64% versus 42% of Germany.
- Germany’s GPD per Capita (in US$) was 35 times larger than that of Philippines.
- Germany’s mobile ARPU was 4 times higher than that of Philippines.
in 2013 (+ 10 Years)
- Philippines & Germany have almost the same amount of mobile subscriptions.
- Germany Mobile Revenue was 6x higher than Philippines.
- German EBITDA was only 5x higher than that of Philippines.
- German OpEx was 6x higher than Mobile OpEx in Philippines (and German OpEx was at level with 2003).
- Mobile Margin of the Philippines dropped 19% to 45% compared to 42% of Germany (essential similar to 2003).
- In local currencies, Philippines increased their EBITDA with ca. 45%, Germany remain constant.
- Both Philippines and Germany has lost 11% in absolute EBITDA between the 10 Year periods maximum and 2013.
- Germany’s GDP per Capita (in US$) was 14 times larger than that of the Philippines.
- Germany’s ARPU was 6 times higher than that of Philippines.
In the Philippines, mobile revenues have grown with 7.4% per anno (between 2003 and 2013) while the corresponding mobile OpEx grew with 12% and thus eroding margin massively over the period as increasingly more mobile customers were addressed. In Philippines, the 2013 OpEx level was 3 times that of 2003 (despite one major network consolidation and being an essential duopoly after the consolidation). In Philippines over this period the annual growth rate of mobile users were 17% (versus Germany’s 6%). In absolute terms the number of users in Germany and Philippines were almost the same in 2013, ca. 115 Million versus 109 Million. In Germany over the same period Financial growth was hardly present although more than 50 Million subscriptions were added.
When OpEx grows faster than Revenue, Profitability will suffer today & even more so tomorrow.
Mobile capital investments (i.e., CapEx) over the period 2003 to 2013 was for Germany 5 times higher than that of Philippines (i.e., remember that Germany also needs at least 5 – 6 times more sites to cover the Urban population) and tracks at a 13% Capex to Revenue ratio versus Philippines 20%.
The stories of Mobile Philippines and of Mobile Germany are not unique. Likewise examples can be found in Emerging Growth Markets as well as Mature Markets.
Can Mature Markets learn or even match (keep on dreaming?) from Emerging Markets in terms of efficiency? Assuming such markets really are efficient of course!
As logic (true or false) would dictate given the relative low ARPUs in emerging growth markets and their correspondingly high margins, one should think that such emerging markets are forced to run their business much more efficient than in Mature Markets. While compelling to believe this, the economical data would indicate that most emerging growth markets have been riding the subscriber & revenue growth band wagon without too much thoughts to the OpEx part … and Frankly why should you care about OpEx when your business generates margins much excess of 40%? Well … it is (much) easier to manage & control OpEx year by year than to abruptly “one day” having to cut cost in panic mode when growth slows down the really ugly way and OpEx keeps increasing without a care in the world. Many mature market operators have been in this situation in the past (e.g., 2004 – 2008) and still today works hard to keep their margins stable and profitability from declining.
Most Companies will report both Revenues and EBITDA on quarterly and annual basis as both are key financial & operational indicators for growth. They tend not report Opex but as seen from above that’s really not a problem to estimate when you have Revenue and EBITDA (i.e., OpEx = Revenue – EBITDA).
Thus, had you left the European Telco scene (assuming you were there in the first place) for the last 10 years and then came back you might have concluded that not much have happened in your absence … at least from a profitability perspective. Germany was in 2013 almost at its Ebitda margin level of 2003. Of course as the ones who did not take a long holiday knows those last 10 years were far from blissful financial & operational harmony in the mature markets where one efficiency program after the other struggled to manage, control and reduce Operators Operational Expenses.
However, over that 10-year period Germany added 50+ Million mobile subscriptions and invested more than 37 Billion US$ into the mobile networks from T-Deutschland, Vodafone, E-plus and Telefonica-O2. The mobile country margin over the 10-year period has been ca. 43% and the Capex to Revenue ratio ca. 13%. By 2013 the total amount of mobile subscription was in the order of 115 Million out of a population of 81 Million (i.e., 54 Million of the German population is between 15 and 64 years of age). The observant numerologist would have realized that there are many more subscriptions than population … this is not surprising as it reflects that many subscribers are having multiple different SIM cards (as opposed to cloned SIMs) or subscription types based on their device portfolio and a host of other reasons.
All Wunderbar! … or? .. well not really … Take a look at the revenue and profitability over the 10 year period and you will find that no (or very very little) revenue and incremental profitability has been gained over the period from 2003 to 2013. AND we did add 80+% more subscriptions to the base!
Here is the Germany Mobile development over the period;
Apart from adding subscribers, having modernized the mobile networks at least twice over the period (i.e, CapEx with little OpEx impact) and introduced LTE into the German market (i.e., with little additional revenue to show for it) not much additional value has been added. It is however no small treat what has happen in Germany (and in many other mature markets for that matter). Not only did Germany almost double the mobile customers (in terms of subscriptions), over the period 3G Nodes-B’s were over-layed across the existing 2G network. Many additional sites were added in Germany as the fundamental 2G cellular grid was primarily based on 900 MHz and to accommodate the higher UMTS frequency (i.e., 2100 MHz) more new locations were added to provide a superior 3G coverage (and capacity/quality). Still Germany managed all this without increasing the Mobile Country OpEx across the period (apart from some minor swings). This has been achieved by a tremendous attention to OpEx efficiency with every part of the Industry having razor sharp attention to cost reduction and operating at increasingly efficiency.
Philippines story is a Fabulous Story of Growth (as summarized above) … and of Profitability & Margin Decline.
Philippines today is in effect a duopoly with PLDT having approx. 2/3 of the mobile market and Globe the remaining 1/3. During the period the Philippine Market saw Sun Cellular being acquired and merged by PLDT. Further, 3G was deployed and mobile data launched in major urban areas. SMS revenues remained the largest share of non-voice revenue to the two remaining mobile operators PLDT and Globe. Over the period 2003 to 2013, the mobile subscriber base (in terms of subscriptions) grew with 16% per anno and the ARPU fell accordingly with 10% per anno (all measured in local currency). All-in-all safe guarding a “healthy” revenue increase over the period from ca. 93 Billion PHP in 2003 to 190 Billion PHP in 2013 (i.e., a 65% increase over the period corresponding to a 5% annual growth rate).
However, the Philippine market could not maintain their relative profitability & initial efficiency as the mobile market grew.
So we observe (at least) two effects (1) Reduction in ARPU as market is growing & (2) Increasing Opex cost to sustain the growth in the market. As more customers are added to a mobile network the return on thus customers increasingly diminishes as network needs to be massively extended capturing the full market potential versus “just” the major urban potential.
Mobile Philippines did become less economical efficient as its scale increases and ARPU dropped (i.e., by almost 70%). This is not an unusual finding across Emerging Growth Markets.
As I have described in my previous Blog “SMS – Assimilation is inevitable, Resistance is Futile!”, Philippines mobile market has an extreme exposure to SMS Revenues which amounts to more than 35% of Total Revenues. Particular as mobile data and smartphones penetrate the Philippine markets. As described in my previous Blog, SMS Services enjoy the highest profitability across the whole range of mobile services we offer the mobile customer including voice. As SMS is being cannibalized by IP-based messaging, the revenue will decline dramatically and the mobile data revenue is not likely to catch up with this decline. Furthermore, profitability will suffer as the the most profitable service (i.e., SMS) is replaced by mobile data that by nature has a different profitability impact compared to simple SMS services.
Philippines do not only have a substantial Margin & EBITDA risk from un-managed OpEx but also from SMS revenue cannibalization (a la KPN in the Netherlands and then some).
Let us compare the ARPU & Opex development for Philippines (above Chart) with that of Germany over the same period 2003 to 2013 (please note that the scale of Opex is very narrow)
Mobile Germany managed their Cost Structure despite 40+% decrease in ARPU and as another 60% in mobile penetration was added to the mobile business. Again similar trend will be found in most Mature Markets in Western Europe.
One may argue (and not being too wrong) that Germany (and most mature mobile markets) in 2003 already had most of its OpEx bearing organization, processes, logistics and infrastructure in place to continue acquiring subscribers (i.e., as measured in subscriptions). Therefor it have been much easier for the mature market operators to maintain their OpEx as they continued to grow. Also true that many emerging mobile markets did not have the same (high) deployment and quality criteria, as in western mature markets, in their initial network and service deployment (i.e., certainly true for the Philippines as is evident from the many Regulatory warnings both PLDT and Globe received over the years) providing basic voice coverage in populated areas but little service in sub-urban and rural areas.
Most of the initial emerging market networks has been based on coarse (by mature market standards) GSM 900 MHz (or CDMA 850 MHz) grids with relative little available capacity and indoor coverage in comparison to population and clutter types (i.e., geographical topologies characterized by their cellular radio interference patterns). The challenge is, as an operator wants to capture more customers, it will need to build out / extend its mobile network in the areas those potential or prospective new customers live and work in. From a cost perspective sub-urban and rural areas in emerging markets are not per se lower cost areas despite such areas in general being lower revenue areas than their urban equivalents. Thus, as more customers are added (i.e., increased mobile penetration) proportionally more cost are generated than revenue being capture and the relative margin will decline. … and this is how the Ugly-cost (or profitability tail) is created.
- I just cannot write about profitability and cost structure without throwing the Ugly-(cost)-Tail on the page.I strongly encourage all mobile operators to make their own Ugly-Tail analysis. You will find more details of how to remedy this Ugliness from your cost structure in “The ABC of Network Sharing – The Fundamentals”.
In Western Europe’s mature mobile markets we find that more than 50% of our mobile cellular sites captures no more than 10% of the Revenues (but we do tend to cover almost all surface area several times unless the mobile operators have managed to see the logic of rural network sharing and consolidated those rural & sub-urban networks). Given emerging mobile markets have “gone less over board” in terms of lowest revenue un-profitable network deployments in rural areas you will find that the number of sites carrying 10% of less of the revenue is around 40%. It should be remembered that the rural populations in emerging growth markets tend to be a lot larger than in of that in mature markets and as such revenue is in principle spread out more than what would be the case in mature markets.
Population & Mobile Statistics and Opex Trends.
The following provides a 2013 Summary of Mobile Penetration, 3G Penetration (measured in subscriptions), Urban Population and the corresponding share of surface area under urban settlement. Further to guide the eye the 100% line has been inserted (red solid line), a red dotted line that represents the share of the population that is between 15 and 64 years of age (i.e., who are more likely to afford a mobile service) and a dashed red line providing the average across all the 43 countries analyzed in this Blog.
- Sources: United Nations, Department of Economic & Social Affairs, Population Division. The UN data is somewhat outdated though for most data points across emerging and mature markets changes have been minor. Mobile Penetration is based on Pyramid Research and Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014. Index Mundi is the source for the Country Age structure and data for %tage of population between 15 and 64 years of age and shown as a red dotted line which swings between 53.2% (Nigeria) to 78.2% (Singapore), with an average of 66.5% (red dashed line).
There is a couple of points (out of many) that can be made on the above data;
- There are no real emerging markets any longer in the sense of providing basic mobile telephone services such as voice and messaging.
- For mobile broadband data via 3G-UMTS (or LTE for that matter), what we tend to characterize as emerging markets are truly emerging or in some case nascent (e.g., Algeria, Iraq, India, Pakistan, etc..).
- All mature markets have mobile penetration rates way above 100% with exception of Canada, i.e., 80% (i.e., though getting to 100% in Canada might be a real challenge due to a very dispersed remaining 20+% of the population).
- Most emerging markets are by now covering all urban areas and corresponding urban population. Many have also reach 100% mobile penetration rates.
- Most Emerging Markets are lagging Western Mature Markets in 3G penetration. Even providing urban population & urban areas with high bandwidth mobile data is behind that of mature markets.
Size & density does matter … in all kind of ways when it comes to the economics of mobile networks and the business itself.
In Australia I only need to cover ca. 40 thousand km2 (i.e., 0.5% of the total surface area and a bit less than the size of Denmark) to have captured almost 90% of the Australian population (e.g., Australia’s total size is 180+ times that of Denmark excluding Greenland). I frequently hear my Australian friends telling me how Australia covers almost 100% of the population (and I am sure that they cover more area than is equivalent to Denmark too) … but without being (too) disrespectful that record is not for Guinness Book of Records anytime soon. in US (e.g., 20% more surface area than Australia) I need to cover in almost 800 thousand km2 (8.2% of surface area or equivalent to a bit more than Turkey) to capture more than 80% of the population. In Thailand I can only capture 35% of the population by covering ca. 5% of the surface area or a little less than 30 thousand km2 (approx. the equivalent of Belgium). The remaining of 65% of the Thai population is rural-based and spread across a much larger surface area requiring extensive mobile network to provide coverage to and capture additional market share outside the urban population.
So in Thailand I might need a bit less cell sites to cover 35% of my population (i.e., 22M) than in Australia to cover almost 90% of the population (i.e., ca. 21M). That’s pretty cool economics for Australia which is also reflected in a very low profitability risk score. For Thailand (and other countries with similar urban demographics) it is tough luck if they want to reach out and get the remaining 65% of their population. The geographical dispersion of the population outside urban areas is very wide and increasing geographical area is required to be covered in order to catch this population group. UMTS at 900 MHz will help to deploy economical mobile broadband, as will LTE in the APT 700 MHz band (being it either FDD Band 28 or TDD Band 44) as the terminal portfolio becomes affordable for rural and sub-urban populations in emerging growth markets.
In Western Europe on average I can capture 77% of my population (i..e, the urban pop) covering 14.2% of the surface area (i.e., average over markets in this analysis), This is all very agreeable and almost all Western European countries cover their surface areas to at least 80% and in most cases beyond that (i.e., it’s just less & easier land to cover though not per see less costly). In most cases rural coverage is encourage (or required) by the mature market license regime and not always a choice of the mobile operators.
Before we look in depth to the growth (incl. positive as well as negative growth), lets first have a peek at what has happened to the mobile revenue in terms of ARPU and Number of Mobile User and the corresponding mobile penetration over the period 2007 to 2013.
- Source: Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014 and Pyramid Research Data data were used to calculated the growth of ARPU as compounded annual growth rate between 2007 to 2013 and the annual growth rate between 2012 and 2013. Since 2007 the mobile ARPUs have been in decline and to make matters worse the decline has even accelerated rather than slowed down as markets mobile penetration saturated.
- Source: Mobile Penetrations taken from Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014 and Pyramid Research Data data .Index Mundi is the source for the Country Age structure and data for %tage of population between 15 and 64 years of age and shown as a red dotted line which swings between 53.2% (Nigeria) to 78.2% (Singapore), with an average of 66.5% (red dashed line). It s interesting to observe that most emerging growth markets are now where the mature markets were in 2007 in terms of mobile penetration.
Apart from a very few markets, ARPU has been in a steady decline since 2007. Further in many countries the ARPU decline has even accelerated rather than slowed down. From most mature markets the conclusion that we can draw is that there are no evidence that mobile broadband data (via 3G-UMTS or LTE) has had any positive effect on ARPU. Although some of the ARPU decline over the period in mature markets (particular European Union countries) can be attributed to regulatory actions. In general as soon a country mobile penetration reaches 100% (in all effect reaches the part of the population 15-64 years of age) ARPU tends to decline faster rather than slowing down. Of course one may correctly argue that this is not a big issue as long as the ARPU times the Users (i.e., total revenue) remain growing healthily. However, as we will see that is yet another challenge for the mobile industry as also the total revenue in mature markets also are in decline on a year by year basis. Given the market, revenue & cost structures of emerging growth markets, it is not unlikely that they will face similar challenges to their mobile revenues (and thus profitability). This could have a much more dramatic effect on their overall mobile economics & business models than what has been experienced in the mature markets which have had a lot more “cushion” on the P&Ls to defend and even grow (albeit weakly) their profitability. It is instructive to see that the most emerging growth markets mobile penetrations have reached the levels of Mature Markets in 2007. Combined with the introduction and uptake of mobile broadband data this marks a more troublesome business model phase than what these markets have experienced in the past.Some of the emerging growth market have yet to introduce 3G-UMTS, and some to leapfrog mobile broadband by launching LTE. Both events, based on lessons learned from mature markets, heralds a more difficult business model period of managing cost structures while defending revenues from decline and satisfy customers appetite for mobile broadband internet that cannot be supported by such countries fixed telecommunications infrastructures.
For us to understand more profoundly where our mobile profitability is heading it is obviously a good idea to understand how our Revenue and OpEx is trending. In this Section I am only concerned about the Mobile Market in Country and not the individual mobile operators in the country. For that latter (i.e., Operator Profitability) you will find a really cool and exiting analytic framework in the Section after this. I am also not interested (in this article) in modeling the mobile business bottom up (been there & done that … but that is an entirely different story line). However, I am interested and I am hunting for some higher level understanding and a more holistic approach that will allow me to probabilistically (by way of Bayesian analysis & ultimately inference) to predict in which direction a given market is heading when it comes to Revenue, OpEx and of course the resulting EBITDA and Margin. The analysis I am presenting in this Section is preliminary and only includes compounded annual growth rates as well as the Year-by-Year growth rates of Revenue and OpEx. Further developments will include specific market & regulatory developments as well to further improve on the Bayesian approach. Given the wealth of data accumulated over the years from the Bank of America Merrill Lynch (BoAML) Global Wireless Matrix datasets it is fairly easy to construct & train statistical models as well as testing those consistent with best practices.
The Chart below comprises 48 countries Revenue & OpEx growth rates as derived from the “Bank of America Merrill Lynch (BoAML) Global Wireless Matrix Q1, 2014” dataset (note: BoAML data available in this analysis goes back to 2003). Out of the 48 Countries, 23 countries have an Opex compounded annual growth rate higher than the corresponding Revenue growth rate. Thus, it is clear that those 23 countries are having a higher risk of reduced margin and strained profitability due to over-proportionate growth of OpEx. Out of the 23 countries with high or very high profitability risk, 11 countries have been characterized in macro-economical terms as emerging growth markets (i.e., China, India, Indonesia, Philippines, Egypt, Morocco, Nigeria, Russia, Turkey, Chile, Mexico) the remaining 12 countries can be characterized as mature markets in macro-economical terms (i.e., New Zealand, Singapore, Austria, Belgium, France, Greece, Spain, Canada, South Korea, Malaysia, Taiwan, Israel). Furthermore, 26 countries had a higher Opex growth between 2012 and 2013 than their revenues and is likely to be trending towards dangerous territory in terms of Profitability Risk.
- Source: Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014. Revenue depicted here is Service Revenues and the OPEX has been calculated as Service REVENUE minus EBITDA. The Compounded Annual Growth Rate (CAGR) is calculated with X being Revenue and Opex. Y-axis scale is from -25% to +25% (i.e., similar to the scale chosen in the Year- by-Year growth rate shown in the Chart below).
With few exceptions one does not need to read the countries names on the Chart above to immediately see where we have the Mature Markets with little or negative growth and where what we typically call emerging growth markets are located.
As the above Chart clearly illustrate the mobile industry across different types of markets have an increasing challenge to deliver profitable growth and if the trend continues to keep their profitability period!
Opex grows faster than Mobile Operator’s can capture Revenue … That’s a problem!
In order gauge whether the growth dynamics of the last 7 years is something to be concerned about (it is! … it most definitely is! but humor me!) … it is worthwhile to take a look at the year by year growth rate trends (i.e. as CAGR only measures the starting point and the end point and “doesn’t really care” about what happens in the in-between years).
- Source: Bank of America Merrill Lynch Global Wireless Matrix Q1, 2014. Revenue depicted here is Service Revenues and the OPEX has been calculated as Service REVENUE minus EBITDA. Year on Year growth is calculated and is depicted in the Chart above. Y-axis scale is from -25% to +25%. Note that the Y-scales in the Year-on-Year Growth Chart and the above 7-Year CAGR Growth Chart are the same and thus directly comparable.
From the Year on Year Growth dynamics compared to the compounded 7-year annual growth rate, we find that Mature Markets Mobile Revenues decline has accelerated. However, in most cases the Mature Market OpEx is declining as well and the Control & Management of the cost structure has improved markedly over the last 7 years. Despite the cost structure management most Mature Markets Revenue have been declining faster than the OpEx. As a result Profitability Squeeze remains a substantial risk in Mature Markets in general.
In almost all Emerging Growth Markets the 2013 to 2012 revenue growth rate has declined in comparison with the compounded annual growth rate. Not surprising as most of those markets are heading towards 100% mobile penetration (as measured in subscriptions). OpEx growth remains a dire concern for most of the emerging growth markets and will continue to squeeze emerging markets profitability and respective margins. There is no indication (in the dataset analyzed) that OpEx is really under control in Emerging Growth Markets, at least to the same degree as what is observed in the Mature Markets (i.e., particular Western Europe). What further adds to the emerging markets profitability risk is that mobile data networks (i.e., 3G-UMTS, HSPA+,..) and corresponding mobile data uptakes are just in its infancy in most of the Emerging Growth Markets in this analysis. The networks required to sustain demand (at a reasonable quality) are more extensive than what was required to provide okay-voice and SMS. Most of the emerging growth markets have no significant fixed (broadband data) infrastructure and in addition poor media distribution infrastructure which can relieve the mobile data networks being built. Huge rural populations with little available ARPU potential but a huge appetite to get connected to internet and media will further stress the mobile business models cost structure and sustainable profitability.
This argument is best illustrated by comparing the household digital ecosystem evolution (or revolution) in Western Europe with the projected evolution of Emerging Growth Markets.
- Above Chart illustrates the likely evolution in Home and Personal Digital Infrastructure Ecosystem of an emerging market’s Household (HH). Particular note that the amount of TV Displays are very low and much of the media distribution is expected to happen over cellular and wireless networks. An additional challenge is that the fixed broadband infrastructure is widely lagging in many emerging markets (in particular in sub-urban and rural areas) increasing the requirements of the mobile network in those markets. It is compelling to believe that we will witness a completely different use case scenarios of digital media consumption than experienced in the Western Mature Markets. The emerging market is not likely to have the same degree of mobile/cellular data off-load as experienced in mature markets and as such will strain mobile networks air-interface, backhaul and backbone substantially more than is the case in mature markets. Source: Dr. Kim K Larsen Broadband MEA 2013 keynote on “Growth Pains: How networks will supply data capacity for 2020”
- Same as above but projection for Western Europe. In comparison with Emerging Markets a Mature Market Household (HH) has many more TV as wells as a substantially higher fixed broadband penetration offering high-bandwidth digital media distribution as well as off-load optionality for mobile devices via WiFi. Source: Dr. Kim K Larsen Broadband MEA 2013 keynote on “Growth Pains: How networks will supply data capacity for 2020”
Mobile Market Profit Sustainability Risk Index
The comprehensive dataset from Bank of America Merrill Lynch Global Wireless Matrix allows us to estimate what I have chosen to call a Market Profit Sustainability Risk Index. This Index provides a measure for the direction (i.e., growth rates) of Revenue & Opex and thus for the Profitability.
The Chart below is the preliminary result of such an analysis limited to the BoAML Global Wireless Matrix Quarter 1 of 2014. I am currently extending the Bayesian Analysis to include additional data rather than relying only on growth rates of Revenue & Opex, e.g., (1) market consolidation should improve the cost structure of the mobile business, (2) introducing 3G usually introduces a negative jump in the mobile operator cost structure, (3) mobile revenue growth rate reduces as mobile penetration increases, (4) regulatory actions & forces will reduce revenues and might have both positive and negative effects on the relevant cost structure, etc.…
So here it is! Preliminary but nevertheless directionally reasonable based on Revenue & Opex growth rates, the Market Profit Sustainability Risk Index over for 48 Mature & Emerging Growth Markets worldwide:
The above Market Profit Sustainability Risk Index is using the following risk profiles
- Very High Risk (index –5): (i.e., for margin decline): (i) Compounded Annual Growth Rate (CAGR) between 2007 and 2013 of Opex was higher than equivalent for Revenue AND (ii) Year-on-Year (YoY) Growth Rate 2012 to 2013 of Opex higher than that of Revenue AND (iii) Opex Year-on-Year 2012 to 2013 Growth Rate is higher than the Opex CAGR over the period 2007 to 2013.
- High Risk (index –3): Same as above Very High Risk with condition (iii) removed OR YoY Revenue Growth 2012 to 2013 lower than the corresponding Opex Growth.
- Medium Risk (index –2): CAGR of Revenue lower than CAGR of Opex but last year (i.e., 2012 t0 2013) growth rate of Revenue higher than that of Opex.
- Low Risk (index 1): (i) CAGR of Revenue higher than CAGR of Opex AND (ii) YoY Revenue Growth higher than Opex Growth but lower than the inflation of the previous year.
- Very Low Risk (index 3): Same as above Low Risk with YoY Revenue Growth Rate required to be higher than the Opex Growth with at least the previous year’s inflation rate.
The Outlook for Mature Markets are fairly positive as most of those Market have engaged in structural cost control and management for the last 7 to 8 years. Emerging Growth Markets Profit Sustainability Risk Index are cause for concern. As the mobile markets are saturating it usually results in lower ARPU and higher cost to reach the remaining parts of the population (often “encouraged” by regulation). Most Emerging Growth markets have started to introduce mobile data, which is likely to result in higher cost-structure pressure & with traditional revenue streams under pressure (if history of Mature Markets are to repeat itself in emerging growth markets). The Emerging Growth Markets have had little incentive (in the past) to focus on cost structure control and management, due to the exceedingly high margins that they historically could present with their legacy mobile services (i.e., Voice & SMS) and relative light networks (as always in comparison to Mature Markets).
Cautionary note is appropriate. All the above are based on the Mobile Market across the world. There are causes and effects that can move a market from having a high risk profile to a lower. Even if I feel that the dataset supports the categorization it remains preliminary as more effects should be included in the current risk model to add even more confidence in its predictive power. Furthermore, the analysis is probabilistic in nature and as such does not claim to carve in stone the future. All the Index claims to do is to indicate a probable direction of the profitability (as well as Revenue & OpEx). There are several ways that Operators and Regulatory Authorities might influence the direction of the profitability changing Risk Exposure (in the Wrong as well as in the Right Direction)
Furthermore, it would be wrong to apply the Market Profit Sustainability Risk Index to individual mobile operators in the relevant markets analyzed here. The profitability dynamics of individual mobile operators are a wee bit more complicated, albeit some guidelines and predictive trends for their profitability dynamics in terms of Revenue and Opex can be defined. This will all be revealed in the following Section.
Operator Profitability – the Profitability Math.
We have seen that the Margin M an be written as
with E, R and O being EBITDA, REVENUE and OPEX respectively.
However, much more interesting is that it can also be written as a function of subscriber share
being valid forwith being the margin and the subscriber market share can be found between 0% to 100%. The rest will follow in more details below, suffice to say that as the subscriber market share increases the Margin (or relative profitability) increases as well although not linearly (if anyone would have expected that ).
Before we get down and dirty on the math lets discuss Operator Profitability from a higher level and in terms of such an operators subscriber market share (i.e., typically measured in subscriptions rather than individual users).
In the following I will show some Individual Operator examples of EBITDA Margin dynamics from Mature Markets limited to Western Europe. Obviously the analysis and approach is not limited emerging markets and can (have been) directly extended to Emerging Growth Markets or any mobile market for that matter. Again BoAML Global Matrix provides a very rich data set for applying the approach described in this Blog.
It has been well established (i.e., by un-accountable and/or un-countable Consultants & Advisors) that an Operator’s Margin correlates reasonably well with its Subscriber Market Share as the Chart below illustrates very well. In addition the Chart below also includes the T-Mobile Netherlands profitability journey from 2002 to 2006 up to the point where Deutsche Telekom looked into acquiring Orange Netherlands. An event that took place in the Summer of 2007.
I do love the above Chart (i.e., must be the physicist in me?) as it shows that such a richness in business dynamics all boiled down to two main driver, i.e., Margin & Subscriber Market Shared.
So how can an Operator strategize to improve its profitability?
Let us take an Example
Here is how we can think about it in terms of Subscriber Market Share and EBITDA as depicted by the above Chart. In simple terms an Operator have a combination of two choices (Bullet 1 in above Chart) Improve its profitability through Opex reductions and making its operation more efficient without much additional growth (i.e., also resulting in little subscriber acquisition cost), it can improve its ARPU profile by increasing its revenue per subscriber (smiling a bit cynical here while writing this) again without adding much in additional market share. The first part of Bullet 1 has been pretty much business as usual in Western Europe since 2004 at least (unfortunately very few examples of the 2nd part of Bullet 1) and (Bullet 2 in above Chart) The above “Margin vs. Subscriber Market Share” Chart indicates that if you can acquire the customers of another company (i.e., via Acquisition & Merger) it should be possible to quantum leap your market share while increasing the efficiencies of the operation by scale effects. In the above Example Chart our Hero has ca. 15% Customer Market Share and the Hero’s target ca. 10%. Thus after an acquisition our Hero would expect to get ca. 25% (if they play it well enough). Similarly we would expect a boost in profitability and hope for at least 38% if our Hero has 18% margin and our Target has 20%. Maybe even better as the scale should improve this further. Obviously, this kind of “math” assumes that our Hero and Target can work in isolation from the rest of the market and that no competitive forces would be at play to disrupt the well thought through plan (or that nothing otherwise disruptive happens in parallel with the merger of the two businesses). Of course such a venture comes with a price tag (i.e., the acquisition price) that needs to be factored into the overall economics of acquiring customers. As said most (Western) Operators are in a perpetual state of managing & controlling cost to maintain their Margin, protect and/or improve their EBITDA.
So one thing is theory! Let us see how the Dutch Mobile Markets Profitability Dynamics evolved over the 10 year period from 2003 to 2013;
From both KPN’s acquisition of Telfort as well as the acquisition & merger of Orange by T-Mobile above Margin vs. Subscriber Market Share Chart, we see that in general, the Market Share logic works. On the other hand the management of the integration of the business would have been fairly unlucky for that to be right. When it comes to the EBITDA logic it does look a little less obvious. KPN clearly got unlucky (if un-luck has something to do with it?) as their margin decline with a small uplift albeit still lower than where they started pre-acquisition. KPN should have expected a margin lift to 50+%. That did not happen to KPN – Telfort. T-Mobile did fare better although we do observe a margin uplift to around 30% that can be attributed to Opex synergies resulting from the integration of the two businesses. However, it has taken many Opex efficiency rounds to get the Margin up to 38% that was the original target for the T-Mobile – Orange transaction.
In the past it was customary to take lots of operators from many countries, plot their margin versus subscriber markets share, draw a straight line through the data points and conclude that the margin potential is directly related to the Subscriber Market Share. This idea is depicted by the Left Side Chart and the Straight line “Best” Fit to data.
Lets just terminate that idea … it is wrong and does not reflect the right margin dynamics as a function of the subscriber markets share. Furthermore, the margin dynamics is not a straight-line function of the subscriber market share but rather asymptotic falling off towards minus infinity, i.e., when the company have no subscribers and no revenue but non-zero cost. We also observed a diminishing return on additional market share in the sense that as more market share is gained smaller and smaller incremental margins are gained. The magenta dashed line in the Left Chart below illustrates how one should expect the Margin to behave as a function of Subscriber market share.
The Right Chart above shows has broken down the data points in country by country. It is obvious that different countries have different margin versus market share behavior and that drawing a curve through all of those might be a bit naïve.
So how can we understand this behavior? Let us start with making a very simple formula a lot more complex :–)
We can write the Marginas the ratio of Earning before Interest Tax Depreciation & Amortization (EBITDA)and Revenue R:, EBITDA is defined as Revenue minus Opex. Both Opex and Revenue I can de-compose into a fixed and a variable part: O = Of + AOPU x U and R = Rf + ARPU x U with AOPU being the Average Opex per User, ARPU the Average (blended) Revenue per User and U the number of users. For the moment I will be ignoring the fixed part of the revenue and write R = ARPU x U. Further, the number of users can be written as with being the market share and M being the market size. So we can now write the margin as
with and .
being valid for
The Margin is not a linear function of the Subscriber Market Share (if anybody would have expected that) but relates to the Inverse of Market Share.
Still the Margin becomes larger as the market share grows with maximum achievable margin of as the market share equals 1 (i.e., Monopoly). We observe that even in a Monopoly there is a limit to how profitable such a business can be. It should be noted that this is not a constant but a function of how operationally efficient a given operator is as well as its market conditions. Furthermore, as the market share reduces towards zero .
Fixed Opex (of) per total subscriber market: This cost element is in principle related to cost structure that is independent on the amount of customers that a given mobile operator have. For example a big country with a relative low population (or mobile penetration) will have higher fixed cost per total amount of subscribers than a smaller country with a larger population (or mobile penetration). Fixed cost is difficult to change as it depends on the network and be country specific in nature. For an individual Operator the fixed cost (per total market subscribers) will be influenced by;
- Coverage strategy, i.e., to what extend the country’s surface area will be covered, network sharing, national roaming vs. rural coverage, leased bandwidth, etc..
- Spectrum portfolio, i.e, lower frequencies are more economical than higher frequencies for surface area coverage but will in general have less bandwidth available (i.e., driving up the number of sites in capacity limited scenarios). Only real exception to bandwidth limitations of low frequency spectrum would be the APT700 band (though would “force” an operator to deploy LTE which might not be timed right given specifics of the market).
- General economical trends, lease/rental cost, inflation, salary levels, etc..
Average Variable Opex per User (ou): This cost structure element capture cost that is directly related to the subscriber, such as
- Market Invest (i.e., Subscriber Acquisition Cost SAC, Subscriber Retention Cost SRC), handset subsidies, usage-related cost, etc..
- Any other variable cost directly associated with the customer (e.g., customer facing functions in the operator organization).
This behavior is exactly what we observe in the presented Margin vs. Subscriber Market Share data and also explains why the data needs to be treated on a country by country basis. It is worthwhile to note that after the higher the market share the less incremental margin gain should be expected for additional market share.
The above presented profitability framework can be used to test whether a given mobile operator is market & operationally efficient compared to its peers.
The overall Margin dynamics is shown above Chart for the various settings of fixed and variable Opex as well as a given operators ARPU. We see that as the fixed Opex (in relation to the total subscriber market) increasing it will get more difficult to get EBITDA positive and increasingly more market share is required to reach a reasonable profitability targets. The following maps a 3 player market according with the profitability logic derived here:
What we first notice is that operators in the initial phase of what you might define as the “Market-share Capture Phase” are extremely sensitive to setbacks. A small loss of subscriber market share (i.e. 2%) can tumble the operator back into the abyss (i.e, 15% Margin setback) and wreck havoc to the business model. The profitability logic also illustrates that once an operator has reached Market-share maturity adding new subscribers is less valuable than to keep them. Even big market share addition will only result in little additional profitability (i.e., the law of diminishing returns).
The derived Profitability framework can be used also to illustrate what happens to the Margin in a market-wise steady situation (i.e., only minor changes to an operators market share) or what the Market Share needs to be to keep a given Margin or how cost needs to be controlled in the event that ARPU drops and we want to keep our margin and cannot grow market share (or any other market, profitability or cost-structure exercise for that matter);
- Above chart illustrates Margin as a function of ARPU & Cost (fixed & variable) Development at a fixed market share here chosen to be 33%. The starting point is an ARPU ru of EUR25.8 per month, a variable cost per user ou assumed to be EUR15 and a fixed cost per total mobile user market (of) of EUR0.5. The first scenario (a Orange Solid Line) with an end of period margin of 32.7% assumes that ARPU reduces with 2% per anno, that the variable cost can be controlled and likewise will reduce with 2% pa. Variable cost is here assumed to increase with 3% on an annual basis. During the 10 year period it is assumed that the Operators market share remains at 33%. The second scenario (b Red Dashed Line) is essential same as (a) with the only difference that the variable cost remains at the initial level of EUR15 and will not change over time. This scenario ends at 21.1% after 10 Years. In principle it shows that Mobile Operators will not have a choice on reducing their variable cost as ARPU declines (again the trade-off between certainty of cost and risk/uncertainty of revenue). In fact the most successful mature mobile operators are spending a lot of efforts to manage & control their cost to keep their margin even if ARPU & Revenues decline.
- The above chart illustrates what market share is required to keep the margin at 36% when ARPU reduces with 2% pa, fixed cost increases with 3% pa and the variable cost either (a Orange Solid Line) can be reduced with 2% in line with the ARPU decline or (b Red Solid Line) remains fixed at the initial level. In scenario (a) the mobile operator would need to grow its market share to 52% to main its margin at 36%. This will obviously be very challenging as this would be on the expense of other operators in this market (here assume to be 3). Scenario (b) is extremely dramatic and in my opinion mission impossible as it requires a complete 100% market dominance.
- Above Chart illustrates how we need to manage & control my variable cost compared to the –2% decline pa in order to keep the Margin constant at 36% assuming that the Operator Subscriber Market Share remains at 33% over the period. The Orange Solid Line in the Chart shows the –2% variable cost decline pa and the Red Dashed Line the variable cost requirement to keep the margin at 36%.
The following illustrates the Profitability Framework as described above applied to a few Western European Markets. As this only serves as an illustration I have chosen to show older data (i..e, 2006). It is however very easy to apply the methodology to any country and the BoAML Global Wireless Matrix with its richness in data can serve as an excellent source for such analysis. Needless to say the methodology can be extended to assess an operators profitability sensitivity to market share and market dynamics in general.
The Charts below shows the Equalized Market Share which simply means the fair market share of operators, i.e., if I have 3 operators the fair or equalized market share would 1/3 (33.3%), in case of 4 operators it should be 25% and so forth, I am also depicting what I call the Max Margin Potential this is simply the Margin potential at 100% Market Share at a given set of ARPU (ru), AOPU (ou) and Fixed Cost (of) Level in relation to the total market.
- Netherlands Chart: Equalized Market Share assumes Orange has been consolidated with T-Mobile Netherlands. The analysis would indicate that no more than ca. 40% Margin should be expected in The Netherlands for any of the 4 Mobile Operators. Note that for T-Mobile and Orange small increases in market share should in theory lead to larger margins, while KPN’s margin would be pretty much un-affected by additional market share.
- Germany Chart: Shows Vodafone to slightly higher and T-Mobile Deutschland slight lower in Margin than the idealized Margin versus Subscriber Market share. At the time T-Mobile had almost exclusive leased lines and outsourced their site infrastructure while Vodafone had almost exclusively Microwaves and owned its own site infrastructure. The two new comers to the German market (E-Plus and Telefonica-O2) is trailing on the left side of the Equalized Market Share. At this point in time should Telefonica and E-Plus have merged one would have expected them eventually (post-integration) to exceed a margin of 40%. Such a scenario would lead to an almost equilibrium market situation with remaining 3 operators having similar market shares and margins.
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog. I certainly have not always been very present during the analysis and writing.
- Up-to 50% of Sites in Mobile Networks captures no more than 10% of Mobile Service Revenues.
- The “Ugly” (cost) Tail of Cellular Networks can only be remedied by either removing sites (and thus low- or –no-profitable service) or by aggressive site sharing.
- With Network Sharing expect up-to 35% saving on Technology Opex as well as future Opex avoidance.
- The resulting Technology Opex savings easily translates into a Corporate Opex saving of up-to 5% as well as future Opex avoidance.
- Active as well as Passive Network Sharing brings substantial Capex avoidance and improved sourcing economics by improved scale.
- National Roaming can be an alternative to Network Sharing in low traffic and less attractive areas. Capex attractive but a likely Ebitda-pressure point over time.
- “Sharing by Towerco” can be an alternative to Real Network Sharing. It is an attractive mean to Capex avoidance but is not Ebitda-friendly. Long-term commitments combined with Ebitda-risks makes it a strategy that should to be considered very carefully.
- Network Sharing frees up cash to be spend in other areas (e.g., customer acquisition).
- Network Sharing structured correctly can result in faster network deployment –> substantial time to market gains.
- Network Sharing provides substantially better network quality and capacity for a lot less cash (compared to standalone).
- Instant cell split option easy to realize by Network Sharing –> cost-efficient provision of network capacity.
- Network Sharing offers enhanced customer experience by improved coverage at less economics.
- Network Sharing can bring spectral efficiency gains of 10% or higher.
The purpose of this story is to provide decision makers, analysts and general public with some simple rules that will allow them to understand Network Sharing and assess whether it is likely to be worthwhile to implement and of course successful in delivering the promise of higher financial and operational efficiency.
Today’s Technology supports almost any network sharing scenario that can be thought of (or not). Financially & not to forget Strategically this is far from so obvious.
Network Sharing is not only about Gains, its evil twin Loss is always present.
Network Sharing is a great pre-cursor to consolidation.
Network sharing has been the new and old black for many years. It is a fashion that that seems to stay and grow with and within the telecommunications industry. Not surprising as we shall see that one of the biggest financial efficiency levers are in the Technology Cost Structure. Technology wise there is no real stumbling blocks for even very aggressive network sharing maximizing the amount of system resources being shared, passive as well as active. The huge quantum-leap in availability of very high quality and affordable fiber optic connectivity in most mature markets, as well between many countries, have pushed the sharing boundaries into Core Network, Service Platforms and easily reaching into Billing & Policy Platforms with regulatory and law being the biggest blocking factor of Network-as-a-Service offerings. Below figure provides the anatomy of network sharing. It should of course be noted that also within each category several flavors of sharing is possible pending operator taste and regulatory possibilities.
Network Sharing comes in many different flavors. To only consider one sharing model is foolish and likely will result in wrong benefit assessment. Setting a sharing deal up for failure down the road (if it ever gets started). It is particular important to understand that while active sharing provides the most comprehensive synergy potential, it tends to be a poor strategy in areas of high traffic potential. Passive sharing is a much more straightforward strategy in such areas. In rural areas, where traffic is less of an issue and profitability is a huge challenge, aggressive active sharing is much more interesting. One should even consider frequency sharing if permitted by regulatory authority. The way I tend to look at the Network Sharing Flavors are (as also depicted in the Figure below);
- Capacity Limited Areas (dense urban and urban) – Site Sharing or Passive Sharing most attractive and sustainable.
- Coverage Limited Areas (i.e., some urban environments, mainly sub-urban and rural) – Minimum Passive Sharing should be pursued with RAN (Active) Sharing providing an additional economical advantage.
- Rural Areas – National Roaming or Full RAN sharing including frequency sharing (if regulatory permissible).
One of the first network sharing deals I got involved in was back in mid-2001 in The Netherlands. This was at the time of the Mobile Industry’s first real cash crises. Just as we were about to launch this new exiting mobile standard (i.e., UMTS) that would bring Internet to the pockets of the masses. After having spend billions & billions of dollars (i.e., way too much of course) on high-frequency 2100MHz UMTS spectrum, all justified by an incredible optimistic (i.e., said in hindsight!) belief in the mobile internet business case, the industry could not afford to deploy the networks required to make our wishful thinking come true.
T-Mobile (i.e., aka Ben BV) engaged with Orange (i.e., aka Dutchtone) in The Netherlands on what should have been a textbook example of the perfect network sharing arrangement. We made a great business case for a comprehensive network sharing. It made good financial and operational sense at the setup. At the time the sharing game was about Capex avoidance and trying to get the UMTS network rolled out as quickly as possible within very tight budgets imposed by our mother companies (i.e., Deutsche Telekom and France Telecom respectively). Two years down the road we revised our strategic thoughts on network sharing. We made another business case for why deploying on standalone made more sense than sharing. At that time the only thing T-we (Mobile NL) really could agree with Orange NL about was ancillary cabinet sharing and of course the underlying site sharing. Except for agreeing not to like the Joint Venture we created (i.e., RANN BV), all else were at odds, e.g., supplier strategy, degree of sharing, network vision, deployment pace, etc… Our respective deployment strategies had diverged so substantially from each other that sharing no longer was an option. Further, T-Mobile decided to rely on the ancillary cabinet we had in place for GSM –> so also no ancillary sharing. This was also at a time where cabinets and equipment took up a lot of space (i.e., do you still remember the first & 2nd generation 3G cabinets?). Many site locations simply could not sustain 2 GSM and 2 UMTS solutions. Our site demand went through the roof and pretty much killed the sharing case.
- Starting point: Site Sharing, Shared Built, Active RAN and transport sharing.
- Just before breakup I: Site Sharing, cabinet sharing if required, shared built where deployment plans overlapped.
- Just before breakup II:Crisis over and almost out. Cash and Capex was no longer as critical as it was at startup.
It did not help that the Joint Venture RANN BV created to realize T-Mobile & Orange NL shared UMTS network plans frequently were at odds with both founding companies. Both entities still had their full engineering & planning departments including rollout departments (i.e., in effect we tried to coordinate across 3 rollout departments & 3 planning departments, 1 from T-Mobile, 1 from Orange and 1 from RANN BV … pretty silly! Right!). Eventually RANN BV was dissolved. The rest is history. Later T-Mobile NL acquired Orange NL and engaged in a very successful network consolidation (within time and money).
The economical benefits of Sharing and Network Consolidation are pretty similar and follows pretty much the same recipe.
Luckily (if Luck has anything to do with it?) since then there have been more successful sharing projects although the verdict is still out whether these constructs are long-lived or not and maybe also by what definition success is measured.
Judging from the more than 34 Thousand views on my various public network sharing presentations, I have delivered around the world since 2008, there certainly seem to be a strong and persistent interest in the topic.
- Fundamentals of Mobile Network Sharing.(2012).
- Ultra-Efficient Network Factory: Network Sharing & other means to leapfrog operator efficiencies. (2012).
- Economics of Network Sharing. (2008).
- Technology Cost Optimization Strategies. (2009).
- Analyzing Business Models for Network Sharing Success. (2009).
I have worked on Network Sharing and Cost Structure Engineering since the early days of 2001. Very initially focus was on UMTS deployments, the need and requirements to deploy much more cash efficient. Cash was a very scarce resource after the dot-com crash between 2000 & 2003. After 2004 the game changed to be an Opex Saving & Avoidance game to mitigate stagnating customer growth and revenue growth slow down.
I have in detail studied many Network Sharing strategies, concepts and deals. A few have turned out successful (at least still alive & kicking) and many more un-successful (never made it beyond talk and analysis). One of the most substantial Network Sharing deals (arguable closer to network consolidation), I work on several years ago is still very much alive and kicking. That particular setup has been heralded as successful and a poster-boy example of the best of Network Sharing (or consolidation). However, by 2014 there has hardly been any sites taken out of operation (certainly no where close to the numbers we assumed and based our synergy savings on).
More than 50% of all network related TCO comes from site-related operational and capital expenses.
Despite the great economical promises and operational efficiencies that can be gained by two mobile operations (fixed for that matter as well) agreeing to share their networks, it is important to note that
It is NOT enough to have a great network sharing plan. A very high degree of discipline and razor-sharp focus in project execution is crucial for delivering network sharing within money and time.
With introduction of UMTS & Mobile Broadband the mobile operator’s margin & cash have come under increasing pressure (not helped by voice revenue decline & saturated markets).
Technology addresses up-to 25% of a Mobile Operators Total Opex & more than 90% of the Capital Expenses.
Radio Access Networks accounts easily for more than 50% of all Network Opex and Capex.
For a reasonable efficient Telco Operation, Technology Cost is the most important lever to slow the business decline, improve financial results and return on investments.
Above Profit & Loss Figure serves as an illustration that Technology Cost (Opex & Capex) optimization and is pivotal to achieve a more efficient operation and a lot more certain that relying on new business (and revenue) additions
It is not by chance that RAN Sharing is such a hot topic. The Radio Access Network takes up more than half of Network Cost including Capex.
Of course there are many other general cost levers to consider that might be less complex than Network Sharing to implement. Another Black (or Dark Grey) is outsourcing of (key) operational functions to a 3rd party. Think here about some of the main ticks
- Site acquisition (SA) & landlord relations (LR) – Standard practice for SA, not recommended for landlord relations. Usually better done by operator self (at least while important during deployment)..
- Site Build – Standard practice with sub-contractors..
- Network operations & Maintenance – Cyclic between in-source and outsource pending business cycle.
- Field services – standard practice particular in network sharing scenarios.
- Power management – particular interesting for network sharing scenarios with heavy reliance of diesel generators and fuel logistics (also synergetic with field services).
- Operational Planning – particular for comprehensive managed network services. Network Sharing could outsource RAN & TX Planning.
- Site leases – Have a site management company deal with site leases with a target to get them down with x% (they usually take a share of the reduced amount). Care should be taken not to jeopardize network sharing possibilities. Will impact landlord relations.
- IT operations – Cyclic between in-source and outsource pending business cycle.
- IT Development – Cyclic between in-source and outsource pending business cycle.
- Tower Infrastructure – Typical Cash for infrastructure swap with log-term Opex commitments. Care must be taken to allow for Network Sharing and infrastructure termination.
In general many of the above (with exception of IT or at least in a different context than RAN Sharing) potential outsourcing options can be highly synergetic with Network Sharing and should always be considered when negotiating a deal.
Looking at the economics of managed services versus network sharing we find in general the following picture;
and remember that any managed services that is assumed to be applicable in the Network Sharing strategy column will enable the upper end of the possible synergy potential estimated. Having a deeper look at the original T-Mobile UK and Hutchinson UK 3G RAN Sharing deal is very instructive as it provides a view on what can be achieved when combining both best practices of network sharing and shared managed services (i.e., this is the story for The ABC of Network Sharing – Part II).
Seriously consider Managed Services when it can be proven to provide at least 20% Opex synergies will be gained for apples to apples SLAs and KPIs (as compared to your insourced model).
Do your Homework! It is bad Karma to implement Managed Services on an in-efficient organizational function or area that has not been optimized prior to outsourcing.
Do your Homework (Part II)! Measure, Analyze and Understand your own relevant cost structure 100% before outsourcing!
It is not by chance that Deutsche Telekom AG (DTAG) has been leading the Telco Operational Efficiency movement and have some of the most successful network sharing operations around. Since 2004 DTAG have had several (very) deep dives programs into their cost structure and defining detailed initiatives across every single operation as well as on its Group level. This has led to one of the most efficient Telco operations around in Western Europe & the US and with lots to learn from when it comes to managing your cost structure when faced with stagnating revenue growth and increasing cost pressure.
In 2006, prior to another very big efficiency program was kicked off within DTAG, I was asked to take a very fundamental and extreme (but nevertheless realistic) look at all the European mobile operations technology cost structures and come back with how much Technology Opex could be pulled out of them (without hurting the business) within 3-4 years (or 2010).
Below (historical) Figure illustrates my findings from 2006 (disguised but nevertheless the real deal);
This analysis (7-8 years old by now) directly resulted in a lot of Network Sharing discussions across DTAGs operations in Europe. Ultimately this work led to a couple of successful Network Sharing engagements within the DTAG (i.e., T-Mobile) Western European footprint. It enabled some of the more in-efficient mobile operations to do a lot more than they could have done standalone and at least one today went from a number last to number 1. So YES … Network Sharing & Cost Structure Engineering can be used to leapfrog an in-efficient business and by that transforming an ugly duckling into what might be regarded as an approximation of a swan. (in this particular example I have in mind, I will refrain from calling it a beautiful swan … because it really isn’t … although the potential is certainly remain even more today).
The observant reader till see that the order of things (or cost structure engineering) matters. As already said above, the golden rule of outsourcing and managed services is to first ensure you have optimized what can be done internally and then consider outsourcing. We found that first outsourcing network operations or establish a managed service relationship prior to a network sharing relationship was sub-optimal and actually might be hindering reaching the most optimal network sharing outcome (i.e., full RAN sharing or active sharing with joint planning & operations).
Revenue Growth will eventually slow down and might even decline due to competitive climate, poor pricing management and regulatory pressures, A Truism for all markets … its just a matter of time. The Opex Growth is rarely in synch with the revenue slow down. This will result in margin or Ebitda pressure and eventually profitability decline.
Revenue will eventually stagnate and likely even enter decline. Cost is entropy-like and will keep increasing.
The technology refreshment cycles are not only getting shorter. These cycles imposes additional pressure on cash. Longer return on investment cycles results compared to the past. Paradoxical as the life-time of the Mobile Telecom Infrastructure is shorter than in the past. This vicious cycle requires the industry to leapfrog technology efficiency, driving demand for infrastructure sharing and business consolidation as well as new innovative business models (i.e., a topic for another Blog).
The time Telco’s have to return on new technology investments is getting increasingly shorter.
Cost saving measures are certain by nature. New Business & New (even Old) Revenue is by nature uncertain.
Back to NETWORK SHARING WITH A VENGENCE!
I have probably learned more from the network sharing deals that failed than the few ones that succeeded (in the sense of actually sharing something). I have work on sharing deals & concepts across across the world; in Western Europe, Central Eastern Europe, Asia and The USA under very different socio-economical conditions, financial expectations, strategic incentives, and very diverse business cycles.
It is fair to say that over the time I have been engaged in Network Sharing Strategies and Operational Realities, I have come to the conclusion that the best or most efficient sharing strategy depends very much on where an operator’s business cycle is and the network’s infrastructure age.
The benefits that potentially can be gained from sharing will depend very much on whether you
- Greenfield: Initial phase of deployment with more than 80% of sites to be deployed.
- Young: Steady state with more than 80% of your sites already deployed.
- Mature: Just in front of major modernization of your infrastructure.
The below Figure describes the three main cycles of network sharing.
It should be noted that I have omitted the timing benefit aspects from the Rollout Phase (i.e., Greenfield) in the Figure above. The omission is on purpose. I believe (based on experience) that there are more likelihood of delay in deployment than obvious faster time-to-market. This is inherent in getting everything agreed as need to be agreed in a Greenfield Network Sharing Scenario. If time-to-market matters more than initial cost efficiency, then network sharing might not a very effective remedy. Once launch have been achieved and market entry secured, network sharing is an extremely good remedy in securing better economics in less attractive areas (i.e., typical rural and outer sub-urban areas). There are some obvious and very interesting games that can be played out with your competitor particular in the Rollout Phase … not all of them of the Altruistic Nature (to be kind).
There can be a very good strategic arguments of not sharing economical attractive site locations depending on the particular business cycle and competitive climate of a given market. The value certain sites market potential could justify to not give them up for sharing. Particular if competitor time-to-market in those highly attractive areas gets delayed. This said there is hardly any reason for not sharing rural sites where the Ugly (Cost) Tail of low or no profitable sites are situated. Being able to share such low-no-profitability sites simply allow operators to re-focus cash on areas where it really matters. Sharing allows services can be offered in rural and under-develop areas at the lowest cost possible. Particular in emerging markets rural areas, where a fairly large part of the population will be living, the cost of deploying and operating sites will be a lot more expensive than in urban areas. Combined with rural areas substantially lower population density it follows that sites will be a lot harder to make positively return on investment within their useful lifetime.
Total Cost of Ownership of rural sites are in many countries substantially higher than their urban equivalents. Low or No site profitability follows.
In general it can be shown that between 40% to 50% of mature operators sites generates less than 10% of the revenue and are substantially more expensive to deploy and operate than urban sites.
The ugly (cost) tail is a bit more “ugly” in mature western markets (i.e., 50+% of sites) than in emerging markets, as the customers in mature markets have higher coverage expectations in general.
(Source: Western European market. Similar Ugly-tail curves observed in many emerging markets as well although the 10% breakpoint tend to be close to 40%).
It is always recommend to analyze the most obvious strategic games that can be played out. Not only from your own perspective. More importantly, you need to have a comprehensive understanding of your competitors (and sharing partners) games and their most efficient path (which is not always synergetic or matching your own). Cost Structure Engineering should not only consider our own cost structure but also those of your competitors and partners.
Sharing is something that is very fundamental to the human nature. Sharing is on the fundamental level the common use of a given resource, tangible as well as intangible.
Sounds pretty nice! However, Sharing is rarely altruistic in nature i.e., lets be honest … why would you help a competitor to get stronger financially and have him spend his savings for customer acquisition … unless of course you achieve similar or preferably better benefits. It is a given that all sharing stakeholders should stand to benefit from the act of sharing. The more asymmetric perceived or tangible sharing benefits are the less stable will a sharing relationship be (or become over time if the benefit distribution should change significantly).
Recipe for a successful sharing partnership is that the sharing partners both have a perception of a deal that offers reasonable symmetric benefits.
It should be noted that perception of symmetric benefits does not mean per see that every saving or avoidance dollar of benefit is exactly the same for both partners. One stakeholder might get access to more coverage or capacity faster than in standalone. The other stakeholder might be able to more driven by budgetary concerns and sharing allows more extensive deployment than otherwise would have been possible within allocated budgets.
Historical most network sharing deals have focused on RAN Sharing, comprising radio access network (RAN) site locations, related passive infrastructure (e.g., such as tower, cabinets, etc..) and various degrees of active sharing. Recent technology development such as software definable network (SDN), virtualization concepts (e.g., Network Function Virtualization, NFV) have made sharing of core network and value-add service platforms interesting as well (or at least more feasible). Another financially interesting industry trend is to spin-off an operators tower assets to 3rd party Tower Management Companies (TMC). The TMC pays upfront a cash equivalent of the value of the passive tower infrastructure to the Mobile Network Operator (MNO). The MNO then lease (i.e., Opex) back the tower assets from the TMC. Such tower asset deals provide the MNO with upfront cash and the TMC a long-term lease income from the MNO. In my opinion such Tower deals tend to be driven by MNOs short-term cash needs without much regard for longer term profitability and Ebitda (i.e., Revenue minus Opex) developments.
With ever increasing demand for more and more bandwidth feeding our customers mobile internet consumption, fiber optical infrastructures have become a must have. Legacy copper-based fixed transport networks can no longer support such bandwidth demands. Over the next 10 years all Telco’s will face massive investments into fiber-optic networks to sustain the ever growing demand for bandwidth. Sharing such investments should be obvious and straightforward. In this area we also are faced with the choice of passive (Dark Fiber itself) as well as active (i.e., DWDM) infrastructure sharing.
NETWORK SHARING SUCCESS FACTORS
There are many consultants out there who evangelize network sharing as the only real cost reduction / saving measure left to the telecom industry. In Theory they are not wrong. The stories that will be told are almost too good to be true. Are you “desperate” for economical efficiency? You might then get very exited by the network sharing promise and forget that network sharing also has a cost side to it (i.e., usually forget and denial are fairly interchangeable here).
In my experience Network Sharing boils down to the following 4 points:
- Who to share with? (your equal, your better or your worse).
- What to share? (sites, passives, active, frequencies, new sites, old sites, towers, rooftops, organization, ,…).
- Where to share? (rural, sub-urban, urban, regional, all, etc..).
- How to share? (“the legal stuff”).
In my more than 14 years of thinking about and working on Network Sharing I have come to the following heuristics of the pre-requisites a successful network sharing:
- CEOs agree with & endorse Network Sharing.
- Sharing Partners have similar perceived benefits (win-win feel).
- Focus on creating a better network for less and with better time-to-market..
- Both parties share a similar end-goal and have a similar strategic outlook.
While it seems obvious it is often forgotten that Network Sharing is a very-long term engagement (“for Life!”) and like in any other relationship (particular the JV kind) Do consider that a break-up can happen … so be prepared (i.e., “legal stuff”).
Compared to 14 – 15 years ago, Technology pretty much support Network Sharing in all its flavors and is no longer a real show-stopper for engaging with another operator to share network and ripe of (eventually) the financial benefits of such a relationship. References on the technical options for network sharing can be found in the 3GPP TR 3GPP TS 22.951 (“Service Aspects and Requirements for network sharing”) and 123.251 (“Network Sharing; Architecture and Functional Description”). Obviously, today 3GPP support for network sharing runs through most of the 3GPP technical requirements and specification documents.
Technology is not a show-stopper for Network Sharing. The Economics might be!
COST STRUCTURE CONSIDERATIONS.
Before committing man power to a network sharing deal, there are a couple of pretty basic “litmus tests” to be done to see whether the economic savings being promised make sense.
First understand your own cost structure (i.e., Capex, Opex, Cash and Revenues) and in particular where Network Sharing will make an impact – positive as well as negative. I am more often that not, surprised how few Executives and Senior Managers really understand their own company’s cost structure. Thus they are not able to quickly spot un-realistic financial & operational promises made.
Seek answers to the following questions:
- What is the Total Technology Opex (Network & IT) share out of the Total Corporate Opex?
- What is the Total Network Opex out of Total Technology Opex?
- What is the Total Radio Access Network (RAN) Opex out of the Total Network Opex?
- Out of the Total RAN Opex how much relates to sites including Operations & Maintenance?
In general, I would expect the following answers to the above questions based on many of mobile operator cost structure analysis across many different markets (from mature to very emerging, from Western Europe, Central Eastern & Southern Europe, to US and Asia-Pacific).
- Technology Opex is 20% to 25% of Total Corporate Opex defined as “Revenue-minus-Ebitda”(depends a little on degree of leased lines & diesel generator dependence).
- Network Opex should be between 70% to 80% of the Technology Opex.,
- RAN related Opex should be between 50% to 80% of the Network Opex. Of course here it is important to understand that not all of this Opex might be impacted by Network Sharing or at least the impact would depend on the Network Sharing model chosen (e.g., active versus passive).
Lets assume that a given RAN network sharing scenario provides a 35% saving on Total RAN Opex, that would be 35% (RAN Saving) x 60% (RAN Opex) x 75% (Network Opex) x 25% (Technology Opex) which yields a total network sharing saving of 4% on the Corporate Opex.
A saving on Opex obviously should translate into a proportional saving on Ebitda (i.e., Earnings before interest tax depreciation & amortization). The margin saving is given as follows
(with E1 and E2 represents Ebitda before and after the relative Opex saving x, m1 is the margin before the Opex saving, assuming that Revenue remains unchanged after Opex saving has been realized).
From the above we see that when the margin is exactly 50% (i.e., fairly un-usual phenomenon for most mature markets), a saving in Opex corresponds directly to an identical relative saving in Ebitda. When the margin is below 50% the relative impact on Ebitda is higher than the relative saving on Opex. If your margin was 40% prior to a realized Opex saving of 5%, one would expect the margin (or Ebitda) saving to be 1.5x that saving or 7.5%.
In general I would expect up-to 35% Opex saving on relevant technology cost structure from network sharing on established networks. If much more saving is claimed, we should get skeptical of the analysis and certainly not take it on face value. It is not un-usual to see Network Sharing contributing as much as 20% saving (and avoidance on run-rate) on the overall Network Opex (ignoring IT Opex here!).
Why not 50% saving (or avoidance)? You may ask! But only once please!
After all we are taking 2 RAN networks and migrating them into 1 network … surely that should result in at 50% saving (i.e., always on relevant cost structure).
First of all, not all relevant (to cellular sites) cost structure is in general relevant to network sharing. Think here about energy consumption and transport solutions as the most obvious examples. Further, landlords are not likely to allow you to directly share existing site locations, and thus site lease cost with another operator without asking for an increased lease (i.e., 20% to 40% is not un-heard of). Existing lease contracts might need to be opened up to allow sharing, terms & conditions will likely need to be re-negotiated, etc.. in the end site lease savings are achievable but these will not translate into a 50% saving.
WARNING! 50% saving claims as a result of Network Sharing are not to be taken at face value!
Another interesting effect is that more shared sites will eventually result compared to the standalone number of sites. In other words, the shared network will have sites than either of the two networks standalone (and hopefully less than the combined amount of sites prior to sharing & consolidation). The reason for this is that the two sharing parties networks rarely are completely symmetric when it comes to coverage. Thus the shared network that will be somewhat bigger than compared to the standalone networks and thus safeguard the customer experience and hopefully the revenue in a post-merged network scenario. If the ultimate shared network has been planned & optimized properly, both parties customers will experience an increased network quality in terms of coverage and capacity (i.e., speed).
#SitesA , #SitesB < #SitesA+B < #SitesA + #SitesB
The Shared Network should always provide a better network customer experience than each standalone networks.
I have experienced Executives argue (usually post-deal obviously!) that it is not possible to remove sites, as any site removed will destroy customer experience. Let me be clear, If the shared network is planned & optimized according with best practices the shared network will deliver a substantial better network experience to the combined customer base than the respective standalone networks.
Lets dive deeper into the Technology Cost Structure. As the Figure below shows (i.e., typical for mature western markets) we have the following high level cost distribution for the Technology Opex
- 10% to 15% for Core Network
- 20% to 40% for IT & Platforms and finally
- 45% to 70% for RAN.
The RAN Opex for markets without energy distribution challenges, i.e., mature & reliable energy delivery grid) is split in (a) ca. 40% (i.e., of the RAN Opex) for Rental & Leasing which is clearly addressable by Network Sharing, (b) ca. 25% in Services including Maintenance & Repair of which at least the non-Telco part is easily addressable by Network Sharing, (c) ca. 15% Personnel Cost also addressable by Network Sharing, (d) 10% Leased Lines (typical backhaul connectivity) is less dependent on Network Sharing although bandwidth volume discounts might be achievable by sharing connectivity to a shared site and finally (e) Energy & other Opex costs would in general not be impacted substantially by Network Sharing. Note that for markets with a high share of diesel generators and fuel logistics, the share of Energy cost within the RAN Opex cost category will be substantially larger than depicted here.
It is important to note here that sharing of Managed Energy Provision, similar to Tower Company lease arrangement, might provide financial synergies. However, typically one would expect Capex Avoidance (i.e., by not buying power systems) on the account of an increased Energy Opex Cost (compared to standalone energy management) for the managed services. Obviously, if such a power managed service arrangement can be shared, there might be some synergies to be gained from such an arrangement. In my opinion this is particular interesting for markets with a high reliance of diesel generators and fuelling logistics.This said
Power sharing in mature markets with high electrification rates can offer synergies on energy via applicable volume discounts though would require shared metering (which might not always be particular well appreciated by power companies).
Maybe as much as
80% of the total RAN Opex can be positively impacted (i.e., reduced) by network sharing.
Above cost structure illustration also explain why I rarely get very exited about sharing measures in Core Network Domain (i.e., spend too much time in the past to explain that while NG Core Network might save 50% of relevant cost it really was not very impressive in absolute terms and efforts was better spend on more substantial cost structure elements). Assume you can save 50% (which is a bit on the wild side today) on Core Network Opex (even Capex is in proportion to RAN fairly smallish). That 50% saving on Core translates into maybe maximum 5% of the Network Opex as opposed to RAN’s 15% – 20%. Sharing Core Network resources with another party does require substantially more overhead management and supervision than even fairly aggressive RAN sharing scenarios (with substantial active sharing).
This said, I believe that there are some internal efficiency measures to Telco Groups (with superior interconnection) and very interesting new business models out there that do provide core network & computing infrastructure as a service to Telco’s (and in principle allow multiple Telco’s to share the core network platforms and resources. My 2012 presentation on Ultra-Efficient Network Factory: Network Sharing & other means to leapfrog operator efficiencies. illustrates how such business models might work out. The first describes in largely generic terms how virtualization (e.g., NFV) and cloud-based technologies could be exploited. The LTE-as-a-Service (could be UMTS-as-a-Service as well of course) is more operator specific. The verdict is still out there whether truly new business models can provide meaningful economics for customer networks and business. In the longer run, I am fairly convinced, that scale and expected massive improvements in connectivity in-countries and between-countries will make these business models economical interesting for many tier-2, tier-3 and Generation-Z businesses.
BUT BUT … WHAT ABOUT CAPEX?
From a Network Sharing perspective Capex synergies or Capex avoidance are particular interesting at the beginning of a network rollout (i.e., Rollout Phase) as well as at the end of the Steady State where technology refreshment is required (i.e., the Modernization Phase).
Obviously, in a site deployment heavy scenario (e.g., start-ups) sharing the materials and construction cost of greenfield tower or rooftop (in as much as it can be shared) will dramatically lower the capital cost of deployment. In particular as you and your competitor(s) would likely want to cover pretty much the same places and thus sharing does become very compelling and a rational choice. Unless its more attractive to block your competitor from gaining access to interesting locations.
Irrespective, between 40% to 50% of an operators sites will only generate up-to 10% of the turnover. Those ugly-cost-tail sites will typically be in rural areas (including forests) and also on average be more costly to deploy and operate than sites in urban areas and along major roads.
Sharing 40% – 50% of sites, also known as the ugly-cost-tail sites, should really be a no brainer!
Depending on the market, the country particulars, and whether we look at emerging or mature markets there might be more or less Tower sites versus rooftops. Rooftops are less obvious passive sharing candidates, while Towers obviously are almost perfect passive sharing candidates provided the linked budget for the coverage can be maintained post-sharing. Active sharing does make rooftop sharing more interesting and might reduce the tower design specifications and thus optimize Capex further in a deployment scenario.
As operators faces RAN modernization pressures it can Capex-wise become very interesting to discuss active as well as passive sharing with a competitor in the same situation. There are joint-procurement benefits to be gained as well as site consolidation scenarios that will offer better long-term Opex trends. Particular T-Mobile and Hutchinson in the UK (and T-Mobile and Orange as well in UK and beyond) have championed this approach reporting very substantial sourcing Capex synergies by sharing procurements. Note network sharing and sharing sourcing in a modernization scenario does not force operators to engage in full active network sharing. However, it is a pre-requisite that there is an agreement on the infrastructure supplier(s).
Network Sharing triggered by modernization requirements is primarily interesting (again Capex wise) if part of electronics and ancillary can be shared (i.e., active sharing). Suppliers match is an obviously must for optimum benefits. Otherwise the economical benefits will be weighted towards Opex if a sizable amount of sites can be phased out as a result of site consolidation.
The above Figure provides an overview of the most interesting components of Network Sharing. It should be noted that Capex prevention is in particular relevant to (1) The Rollout Phase and (2) The Modernization Phase. Opex prevention is always applicable throughout the main 3 stages Network Sharing Attractiveness Cycles. In general the Regulatory Complexity tend to be higher for Active Sharing Scenarios and less problematic for Passive Sharing Scenarios. In general Regulatory Authorities would (or should) encourage & incentivize passive site sharing ensuring that an optimum site infrastructure (i.e., number of towers & rooftops) is being built out (in greenfield markets) or consolidated (in established / mature markets). Even today it is not un-usual to find several towers, each occupied with a single operator, next to each other or within hundred of meters distance.
NETWORK SHARING DOES NOT COME FOR FREE!
One of the first things a responsible executive should ask when faced with the wonderful promises of network sharing synergies in form of Ebitda and cash improvements is
What does it cost me to network share?
The amount of re-structuring or termination cost that will be incurred before Network Sharing benefits can be realized will depend a lot on which part of the Network Sharing Cycle.
(1) The Rollout Phase in which case re-structuring cost is likely to be minimum as there is little or nothing to restructure. Further, also in this case write-off of existing investments and assets would likewise be very small or non-existent pending on how far into the rollout the business would be. What might complicate matters are whether sourcing contracts needs to be changed or cancelled and thus result in possible penalty costs. In any event being able to deploy together the network from the beginning does (in theory) result in the least deployment complexity and best deployment economics. However, getting to the point of agreeing to shared deployment (i.e., which also requires a reasonable common site grid) might be a long and bumpy road. Ultimately, launch timing will be critical to whether two operators can agree on all the bits and pieces in time not to endanger targeted launch.
Network Sharing in the Rollout Phase is characterized by
(2) The Steady State Phase, where a substantial part of the networks have been rollout out, tend to be the most complex and costly phase to engage in Network Sharing passive and of course active sharing. A substantial amount of site leases would need to be broken, terminated or re-structured to allow for network sharing. In all cases either penalties or lease increases are likely to result. Infrastructure supplier contracts, typically maintenance & operations agreements, might likewise be terminated or changed substantially. Same holds for leased transmission. Write-off can be very substantial in this phase as relative new sites might be terminated, new radio equipment might become redundant or phased-out, etc If one or both sharing partners are in this phase of the business & network cycle the chance of a network sharing agreement is low. However, if a substantial amount of both parties site locations will be used to enhance the resulting network and a substantial part of the active equipment will be re-used and contracts expanded then sharing tends to be going ahead. A good example of this is in the UK with Vodafone and O2 site sharing agreement with the aim to leapfrog number of sites to match that of EE (Orange + T-Mobile UK JV) for improved customer experience and remain competitive with the EE network.
Network Sharing in the Steady State Phase is characterized by
(3) Once operators approaches the Modernization Phase more aggressive network sharing scenarios can be considered as the including joint sourcing and infrastructure procurement (e.g., a la T-Mobile UK and Hutchinson in UK). At this stage typically the remainder of the site leases term will be lower and penalties due to lease termination as a result lower as well. Furthermore, at this point in time little (or at least substantially lower than in the steady state phase) residual value should remain in the active and also passive infrastructure. The Modernization Phase is a very opportune moment to consider network sharing, passive as well as active, resulting in both substantial Capex avoidance and of course very attractive Opex savings mitigating a stagnating or declining topline as well as de-risking future loss of profitability.
Network Sharing in the Modernization Phase is characterized by
- Relative moderate restructuring & termination cost expected.
- High Capex avoidance potential.
- Substantial Opex saving potential.
- Little infrastructure write-offs.
- Lower risk of contract termination penalties.
- Manageable consolidation project.
- Instant cell splits and cost-efficient provision of network capacity.
- More aggressive network optimization –> better network.
As a rule of thumb I usually recommend to estimate restructuring / termination cost as follows (i.e., if you don’t have the real terms & conditions of contracts by the hand);
- 1.5 to 3+ times the estimated Opex savings – use the higher multiple in the Steady State Phase and the Lower for Modernization Phase.
- Consolidation Capex will often be partly synergetic with Business-as-Usual (BaU) Capex and should not be fully considered (typically between 25% to 50% of consolidation Capex can be mapped to BaU Capex).
- Write-offs should be considered and will be the most pain-full to cope with in the Steady State Phase.
NATIONAL ROAMING AS AN ALTERNATIVE TO NETWORK SHARING.
A National Roaming agreement will save network investments and the resulting technology Opex. So in terms of avoiding technology cost that’s an easy one. Of course from a Profit & Loss (P&L) perspective I am replacing my technology Opex and Capex with wholesale cost somewhere else in my P&L. Whether National Roaming is attractive or not will depend a lot of anticipated traffic and of course the wholesale rate the hosting network will charge for the national roaming service. Hutchinson in UK (as well in other markets) had for many years a GSM national roaming agreement with Orange UK, that allowed its customers basic services outside its UMTS coverage footprint. In Austria for example, Hutchinson (i.e., 3 Austria) provide their customers with GSM national roaming services on T-Mobile Austria’s 2G network (i.e., where 3 Austria don’t cover with their own 3G) and T-Mobile Austria has 3G national roaming arrangement with Hutchinson in areas that they do not cover with 3G.
In my opinion whether national roaming make sense or not really boils down to 3 major considerations for both parties:
There are plenty of examples on National Roaming which in principle can provide similar benefits to infrastructure sharing by avoidance of Capex & Opex that is being replaced by the cost associated with the traffic on the hosting network.The Hosting MNO gets wholesale revenue from the national roaming traffic which the Host supports in low-traffic areas or on a under-utilized network. National roaming agreements or relationships tends to be of temporary nature.
It should be noted that National Roaming is defined in an area were 1-Party The Host has network coverage (with excess capacity) and another operator (i.e., The Roamer or The Guest) has no network coverage but has a desire to offer its customers service in that particular area. In general only the host’s HPLMN is been broadcasted on the national roaming network. However, with Multi-Operator Core Network (MOCN) feature it is possible to present the national roamer with the experience of his own network provided the roamers terminal equipment supports MOCN (i.e., Release 8 & later terminal equipment will support this feature).
In many Network Sharing scenarios both parties have existing and overlapping networks and would like to consolidate their networks to one shared network without loosing service quality. The reduction in site locations provide the economical benefits of network sharing. Throughout the shared network both operators will radiate their respective HPLMNs and the shared network will be completely transparent to their respective customer bases.
While having been part of several discussions to shut down one networks in geographical areas of a market and move customers to a host overlapping (or better) network via a national roaming agreement, I am not aware of mobile operators which have actually gone down this path.
Regulatory and from a spectrum safeguard perspective it might be a better approach to commission both parties frequencies on the same network infrastructure and make use of for example the MOCN feature that allows full customer transparency (at least for Release 8 and later terminals).
National Roaming is fully standardized and a well proven arrangement in many markets around the world. One does need to be a bit careful with how the national roaming areas are defined/implemented and also how customers move back and forth from a national roaming area (and technology) to home area (and technology). I have seen national roaming arrangements not being implemented because the dynamics was too complex to manage. The “cleaner” the national roaming area is the simpler does the on-off national roaming dynamics become. With “Clean” is mean keep the number of boundaries between own and national roaming network low, go for contiguous areas rather than many islands, avoid different technology coverage overlap (i.e., area with GSM coverage, it should avoided to do UMTS national roaming), etc.. Note you can engineer a “dirty” national roaming scenario of course. However, those tend to be fairly complex and customer experience management tends to be sub-optimal.
Network Sharing and National Roaming are from a P&L perspective pretty similar in the efficiency and savings potentials. The biggest difference really is in the Usage Based cost item where a National Roaming would incur higher cost than compared to a Network Sharing arrangement.
An Example: Operator contemplate 2 scenarios;
- Network Sharing in rural area addressing 500 sites.
- Terminate 500 sites in rural area and make use of National Roaming Agreement.
What we are really interested in, is to understand when Network Sharing provides better economics than National Roaming and of course vice versa.
National Roaming can be attractive for relative low traffic scenarios or in case were product of traffic units and national roaming unit cost remains manageable and lower than the Shared Network Cost.
The above illustration ignores the write-off and termination charges that might result from terminating a given number of sites in a region and then migrate traffic to a national roaming network (note I have not seen any examples of such scenarios in my studies).
The termination cost or restructuring cost, including write-off of existing telecom assets (i.e., radio nodes, passive site solutions, transmission, aggregation nodes, etc….) is likely to be a substantially financial burden to National Roaming Business Case in an area with existing telecom infrastructure. Certainly above and beyond that of a Network Sharing scenario where assets are being re-used and restructuring cost might be partially shared between the sharing partners.
Obviously, if National Roaming is established in an area that has no network coverage, restructuring and termination cost is not an issue and Network TCO will clearly be avoided, Albeit the above economical logic and P&L trade-offs on cost still applies.
National Roaming can be an interesting economical alternative, at least temporarily, to Network Sharing or establishing new coverage in an area with established network operators.
However, National Roaming agreements are usually of temporary nature as establishing own coverage either standalone or via Network Sharing eventually will be a better economical and strategic choice than continuing with the national roaming agreement.
SHARING BY TOWER COMPANY (TOWERCO).
There is a school of thought, within the Telecommunications Industry, that very much promotes the idea of relying on Tower Companies (Towerco) to provide and manage passive telecom site infrastructure.
The mobile operator leases space from the Towerco on the tower (or in some instances a rooftop) for antennas, radio units and possible microwave dishes. Also the lease would include some real estate space around the tower site location for the telecom racks and ancillary equipment.
In the last 10 years many operators have sold off their tower assets to Tower companies that then lease those back to the mobile operator.
In most Towerco deals, Mobile Operators are trading off up-front cash for long-term lease commitments.
With the danger of generalizing, Towerco deals made by operators in my opinion have a bit the nature and philosophy of “The little boy peeing in his trousers on a cold winter day, it will warm him for a short while, in the long run he will freeze much more after the act”. Let us also be clear that the business down the road will not care about a brilliant tower deal (done in the past) if it pressures their Ebitda and Site Lease cost.
In general the Tower company will try (should be incented) to increase the tower tenancy (i.e., having more tenants per tower). Pending on the lease contract the Towerco might (should!) provide the mobile operator lease discount as more tenants are added to a given tower infrastructure.
Towerco versus Network Sharing is obviously a Opex versus Capex trade-off. Anyway, lets look at a simple total-cost-of-ownership example that allows us to understand better when one strategy could be better than the other.
From the above very simple and high level per tower total-cost-of-ownership model its clear that a Towerco would have some challenges in matching the economics of the Shared Network. A Mobile Operator would most likely (in above example) be better of commencing on a simple tower sharing model (assuming a sharing partner is available and not engaging with another Towerco) rather than leasing towers from a Towerco. The above economics is ca. 600 US$ TCO per month (2-sharing scenario) compared to ca. 1,100 (2-tenant scenario). Actually, unless the Towerco is able to (a) increase occupancy beyond 2, (b) reduce its productions cost well below what the mobile operators would be (without sacrificing quality too much), and (c) at a sufficient low margin, it is difficult to see how a Towerco can provide a Tower solution at better economics than conventional network shared tower.
This said it should also be clear that the devil will be in the details and there are various P&L and financial engineering options available to mobile operators and Towercos that will improve on the Towerco model. In terms of discounted cash flow and NPV analysis of the cash flows over the full useful life period the Network Sharing model (2-parties) and Towerco lease model with 2-tenants can be made fairly similar in terms of value. However, for 2-tenant versus 2-party sharing, the Ebitda tends to be in favor of network sharing.
For the Mobile Network Operator (MNO) it is a question of committing Capital upfront versus an increased lease payment over a longer period of time. Obviously the cost of capital factors in here and the inherent business model risk. The inherent risk factors for the Towerco needs to be considered in its WACC (weighted average cost of capital) and of course the overall business model exposure to
- Operator business failure or consolidation.
- Future Network Sharing and subsequent lease termination.
- Tenant occupancy remains low.
- Contract penalties for Towerco non-performance, etc..
Given the fairly large inherent risk (to Towerco business models) of operator consolidation in mature markets, with more than 3 mobile operators, there would be a “wicked” logic in trying to mitigate consolidation scenarios with costly breakaway clauses and higher margins.
From all the above it should be evident that for mobile operators with considerable tower portfolios and also sharing ambitions, it is far better to (First) Consolidate & optimize their tower portfolios, ensuring minimum 2 tenants on each tower and then (Second) spin-off (when the cash is really needed) the optimized tower portfolio to a Towerco ensuring that the long-term lease is tenant & Ebitda optimized (as that really is going to be any mobile operations biggest longer term headache as markets starts to saturate).
SUMMARY OF PART I – THE FUNDAMENTALS.
There should be little doubt that
Network Sharing provides one of the biggest financial efficiency levers available to mobile network operator.
Maybe apart from reducing market invest… but that is obviously not really a sustainable medium-long-term strategy.
In aggressive network sharing scenarios Opex savings in the order of 35% is achievable as well as future Opex avoidance in the run-rate. Depending on the Network Sharing Scenario substantial Capex can be avoided by sharing the infrastructure built-out (i.e., The Rollout Phase) and likewise in the Modernization Phase. Both allows for very comprehensive sharing of both passive and active infrastructure and the associated capital expenses.
Both National Roaming and Sharing via Towerco can be interesting concepts and if engineered well (particular financially) can provide similar benefits as sharing (active as well as passive, respectively). Particular in cash constrained scenarios (or where operators see an extraordinary business risk and want to minimize cash exposure) both options can be attractive. Long-term National Roaming is particular attractive in areas where an operator have no coverage and has little strategic importance. In case an area is strategically important, national roaming can act as a time-bridge until presence has been secure possibly via Network Sharing (if competitor is willing).
Sharing via Towerco can also be an option when two parties are having trust issues. Having a 3rd party facilitating the sharing is then an option.
In my opinion National Roaming & Sharing via Towerco rarely as Ebitda efficient as conventional Network Sharing.
Finally! Why should you stay away from Network Sharing?
This question is important to answer as well as why you should (which always seems initially the easiest). Either to indeed NOT to go down the path of network sharing or at the very least ensure that point of concerns and possible blocking points have been though roughly considered and checked of.
So here comes some of my favorites … too many of those below you are not terrible likely to be successful in this endeavor:
I would like to thank many colleagues for support and Network Sharing discussions over the past 13 years. However, in particular I owe a lot to David Haszeldine (Deutsche Telekom) for his insights and thoughts. David has been my true brother-in-arms throughout my Deutsche Telekom years and on our many Network Sharing experiences we have had around the world. I have had many & great discussions with David on the ins-and-outs of Network Sharing … Not sure we cracked it all? … but pretty sure we are at the forefront of understanding what Network Sharing can be and also what it most definitely cannot do for a Mobile Operator. Of course similar to all the people who have left comments on my public presentations and gotten in contact with me on this very exiting and by no way near exhausted topic of how to share networks.
The term the “Ugly Tail” as referring to rural and low-profitability sites present in all networks should really be attributed to Fergal Kelly (now CTO of Vodafone Ireland) from a meeting quiet a few years ago. The term is too good not to borrow … Thanks Fergal!
This story is PART I and as such it obviously would indicate that another Part is on the way … PART II – “Network Sharing – That was then, this is now” will be on the many projects I have worked on in my professional career and lessons learned (all available in the public domain of course). Here obviously providing a comparison with the original ambition level and plans with the reality is going to be cool (and in some instances painful as well). PART III – “The Tools” will describe the arsenal of tools and models that I have developed over the last 13 years and used extensively on many projects.
By now the biggest challenge of the “1,000x challenge” is to read yet another story about the “1,000x challenge”.
This said, Qualcomm has made many beautiful presentations on The Challenge. It leaves the reader with an impression that it is much less of a real challenge, as there is a solution for everything and then some.
So bear with me while we take a look at the Economics and in particular the Economical Boundaries around the Thousand Times “Challenge” of providing (1) More spectrum, (2) Better efficiency and last but not least (3) Many more Small Cells.
THE MISSING LINK
While (almost) every technical challenge is solvable by clever engineering (i.e., something Qualcomm obviously have in abundance), it is not following naturally that such solutions are also feasible within the economical framework imposed by real world economics. At the very least, any technical solution should also be reasonable within the world of economics (and of course within a practical time-frame) or it becomes a clever solution but irrelevant to a real world business.
A Business will (maybe should is more in line with reality) care about customer happiness. However a business needs to do that within healthy financial boundaries of margin, cash and shareholder value. Not only should the customer be happy, but the happiness should extend to investors and shareholders that have trusted the Business with their livelihood.
While technically, and almost mathematically, it follows that massive network densification would be required in the next 10 years IF WE KEEP FEEDING CUSTOMER DEMAND it might not be very economical to do so or at the very least such densification only make sense within a reasonable financial envelope.
Its obvious that massive network densification, by means of macro-cellular expansion, is unrealistic, impractically as well as uneconomically. Thus Small Cell concepts including WiFi has been brought to the Telecoms Scene as an alternative and credible solution. While Small Cells are much more practical, the question whether they addresses sufficiently the economical boundaries, the Telecommunications Industry is facing, remains pretty much unanswered.
The Thousand Times Challenge, as it has been PR’ed by Qualcomm, states that the cellular capacity required in 2020 will be at least 1,000 times that of “today”. Actually, the 1,000 times challenge is referenced to the cellular demand & supply in 2010, so doing the math
the 1,000x might “only” be a 100 times challenge between now and 2020 in the world of Qualcomm’s and alike. Not that it matters! … We still talk about the same demand, just referenced to a later (and maybe less “sexy” year).
In my previous Blogs, I have accounted for the dubious affair (and non-nonsensical discussion) of over-emphasizing cellular data growth rates (see “The Thousand Times Challenge: The answer to everything about mobile data”) as well as the much more intelligent discussion about how the Mobile Industry provides for more cellular data capacity starting with the existing mobile networks (see “The Thousand Time Challenge: How to provide cellular data capacity?”).
As it turns out Cellular Network Capacity C can be described by 3 major components; (1) available bandwidth B, (2) (effective) spectral efficiency E and (3) number of cells deployed N.
The SUPPLIED NETWORK CAPACITY in Mbps (i.e., C) is equal to the AMOUNT OF SPECTRUM, i.e., available bandwidth, in MHz (i..e, B) multiplied with the SPECTRAL EFFICIENCY PER CELL in Mbps/MHz (i.e., E) multiplied by the NUMBER OF CELLS (i.e., N). For more details on how and when to apply the Cellular Network Capacity Equation read my previous Blog on “How to provide Cellular Data Capacity?”).
SK Telekom (SK Telekom’s presentation at the 3GPP workshop on “Future Radio in 3GPP” is worth a careful study) , Mallinson (@WiseHarbor) and Qualcomm (@Qualcomm_tech, and many others as of late) have used the above capacity equation to impose a Target amount of cellular network capacity a mobile network should be able to supply by 2020: Realistic or Not, this target comes to a 1,000 times the supplied capacity level in 2010 (i.e., I assume that 2010 – 2020 sounds nicer than 2012 – 2022 … although the later would have been a lot more logical to aim for if one really would like to look at 10 years … of course that might not give 1,000 times which might ruin the marketing message?).
So we have the following 2020 Cellular Network Capacity Challenge:
Thus a cellular network in 2020 should have 3 times more spectral bandwidth B available (that’s fairly easy!), 6 times higher spectral efficiency E (so so … but not impossible, particular compared with 2010) and 56 times higher cell site density N (this one might be a “real killer challenge” in more than one way), compared to 2010!.
Personally I would not get too hanged up about whether its 3 x 6 x 56 or 6 x 3 x 56 or some other “multiplicators” resulting in a 1,000 times gain (though some combinations might be a lot more feasible than others!)
Obviously we do NOT need a lot of insights to see that the 1,000x challenge is a
Rally call for Small & then Smaller Cell Deployment!
Also we do not need to be particular visionary (or have visited a Dutch Coffee Shop) to predict that by 2020 (aka The Future) compared to today (i.e., October 2012)?
Data demand from mobile devices will be a lot higher in 2020!
Cellular Networks have to (and will!) supply a lot more data capacity in 2020!
Footnote: the observant reader will have seen that I am not making the claim that there will be hugely more data traffic on the cellular network in comparison to today. The WiFi path might (and most likely will) take a lot of the traffic growth away from the cellular network.
how economical will this journey be for the Mobile Network Operator?
THE ECONOMICS OF THE THOUSAND TIMES CHALLENGE
Mobile Network Operators (MNOs) will not have the luxury of getting the Cellular Data Supply and Demand Equation Wrong.
The MNO will need to balance network investments with pricing strategies, churn & customer experience management as well as overall profitability and corporate financial well being:
Growth, if not manage, will lead to capacity & cash crunch and destruction of share holder value!
So for the Thousand Times Challenge, we need to look at the Total Cost of Ownership (TCO) or Total Investment required to get to a cellular network with 1,000 times more network capacity than today. We need to look at:
Investment I(B) in additional bandwidth B, which would include (a) the price of spectral re-farming (i.e., re-purposing legacy spectrum to a new and more efficient technology), (b) technology migration (e.g., moving customers off 2G and onto 3G or LTE or both) and (c) possible acquisition of new spectrum (i..e, via auction, beauty contests, or M&As).
Improving a cellular networks spectral efficiency I(E) is also likely to result in additional investments. In order to get an improved effective spectral efficiency, an operator would be required to (a) modernize its infrastructure, (b) invest into better antenna technologies, and (c) ensure that customer migration from older spectral in-efficient technologies into more spectral efficient technologies occurs at an appropriate pace.
Last but NOT Least the investment in cell density I(N):
Needing 56 times additional cell density is most likely NOT going to be FREE,
even with clever small cell deployment strategies.
Though I am pretty sure that some will make a very positive business case, out there in the Operator space, (note: the difference between Pest & Cholera might come out in favor of Cholera … though we would rather avoid both of them) comparing a macro-cellular expansion to Small Cell deployment, avoiding massive churn in case of outrageous cell congestion, rather than focusing on managing growth before such an event would occur.
The Real “1,000x” Challenge will be Economical in nature and will relate to the following considerations:
In other words:
Mobile Networks required to supply a 1,000 times present day cellular capacity are also required to provide that capacity gain at substantially less ABSOLUTE Total Cost of Ownership.
I emphasize the ABSOLUTE aspects of the Total Cost of Ownership (TCO), as I have too many times seen our Mobile Industry providing financial benefits in relative terms (i.e., relative to a given quality improvement) and then fail to mention that in absolute cost the industry will incur increased Opex (compared to pre-improvement situation). Thus a margin decline (i.e., unless proportional revenue is gained … and how likely is that?) as well as negative cash impact due to increased investments to gain the improvements (i.e., again assuming that proportional revenue gain remains wishful thinking).
Never Trust relative financial improvements! Absolutes don’t Lie!
THE ECONOMICS OF SPECTRUM.
Spectrum economics can be captured by three major themes: (A) ACQUISITION, (B) RETENTION and (C) PERFECTION. These 3 major themes should be well considered in any credible business plan: Short, Medium and Long-term.
It is fairly clear that there will not be a lot new lower frequency (defined here as <2.5GHz) spectrum available in the next 10+ years (unless we get a real breakthrough in white-space). The biggest relative increase in cellular bandwidth dedicated to mobile data services will come from re-purposing (i.e., perfecting) existing legacy spectrum (i.e., by re-farming). Acquisition of some new bandwidth in the low frequency range (<800MHz), which per definition will not be a lot of bandwidth and will take time to become available. There are opportunities in the very high frequency range (>3GHz) which contains a lot of bandwidth. However this is only interesting for Small Cell and Femto Cell like deployments (feeding frenzy for small cells!).
As many European Countries re-auction existing legacy spectrum after the set expiration period (typical 10 -15 years), it is paramount for a mobile operator to retain as much as possible of its existing legacy spectrum. Not only is current traffic tied up in the legacy bands, but future growth of mobile data will critical depend on its availability. Retention of existing spectrum position should be a very important element of an Operators business plan and strategy.
Most real-world mobile network operators that I have looked at can expect by acquisition & perfection to gain between 3 to 8 times spectral bandwidth for cellular data compared to today’s situation.
For example, a typical Western European MNO have
- Max. 2x10MHz @ 900MHz primarily used for GSM. Though some operators are having UMTS 900 in operation or plans to re-farm to UMTS pending regulatory approval.
- 2×20 MHz @ 1800MHz, though here the variation tend to be fairly large in the MNO spectrum landscape, i.e., between 2x30MHz down-to 2x5MHz. Today this is exclusively in use for GSM. This is going to be a key LTE band in Europe and already supported in iPhone 5 for LTE.
- 2×10 – 15 MHz @ 2100MHz is the main 3G-band (UMTS/HSPA+) in Europe and is expected to remain so for at least the next 10 years.
- 2×10 @ 800 MHz per operator and typically distributed across 3 operator and dedicated to LTE. In countries with more than 3 operators typically some MNOs will have no position in this band.
- 40 MHz @ 2.6 GHz per operator and dedicated to LTE (FDD and/or TDD). From a coverage perspective this spectrum would in general be earmarked for capacity enhancements rather than coverage.
Note that most European mobile operators did not have 800MHz and/or 2.6GHz in their spectrum portfolios prior to 2011. The above list has been visualized in the Figure below (though only for FDD and showing the single side of the frequency duplex).
The 700MHz will eventually become available in Europe (already in use for LTE in USA via AT&T and VRZ) for LTE advanced. Though the time frame for 700MHz cellular deployment in Europe is still expected take maybe up to 8 years (or more) to get it fully cleared and perfected.
Today (as of 2012) a typical European MNO would have approximately (a) 60 MHz (i.e., DL+UL) for GSM, (b) 20 – 30 MHz for UMTS and (c) between 40MHz – 60MHz for LTE (note that in 2010 this would have been 0MHz for most operators!). By 2020 it would be fair to assume that same MNO could have (d) 40 – 50 MHz for UMTS/HSPA+ and (e) 80MHz – 100MHz for LTE. Of course it is likely that mobile operators still would have a thin GSM layer to support roaming traffic and extreme laggards (this is however likely to be a shared resource among several operators). If by 2020 10MHz to 20MHz would be required to support voice capacity, then the MNO would have at least 100MHz and up-to 130MHz for data.
Note if we Fast-Backward to 2010, assume that no 2.6GHz or 800MHz auction had happened and that only 2×10 – 15 MHz @ 2.1GHz provided for cellular data capacity, then we easily get a factor 3 to 5 boost in spectral capacity for data over the period. This just to illustrate the meaningless of relativizing the challenge of providing network capacity.
So what’s the economical aspects of spectrum? Well show me the money!
- needs to be Acquired (including re-acquired = Retention) via (a) Auction, (b) Beauty contest or (c) Private transaction if allowed by the regulatory authorities (i.e., spectrum trading); Usually spectrum (in Europe at least) will be time-limited right-to-use! (e.g., 10 – 15 years) => Capital investments to (re)purchase spectrum.
- might need to be Perfected & Re-farmed to another more spectral efficient technology => new infrastructure investments & customer migration cost (incl. acquisition, retention & churn).
- new deployment with coverage & service obligations => new capital investments and associated operational cost.
- demand could result in joint ventures or mergers to acquire sufficient spectrum for growth.
- often has a re-occurring usage fee associate with its deployment => Operational expense burden.
First 3 bullet points can be attributed mainly to Capital expenditures and point 5. would typically be an Operational expense. As we have seen in US with the failed AT&T – T-Mobile US merger, bullet point 4. can result in very high cost of spectrum acquisition. Though usually a merger brings with it many beneficial synergies, other than spectrum, that justifies such a merger.
Above Figure provides a historical view on spectrum pricing in US$ per MHz-pop. As we can see, not all spectrum have been borne equal and depending on timing of acquisition, premium might have been paid for some spectrum (e.g., Western European UMTS hyper pricing of 2000 – 2001).
Some general spectrum acquisition heuristics can be derived by above historical overview (see my presentation “Techno-Economical Aspects of Mobile Broadband from 800MHz to 2.6GHz” on @slideshare for more in depth analysis).
Most of the operator cost associated with Spectrum Acquisition, Spectrum Retention and Spectrum Perfection should be more or less included in a Mobile Network Operators Business Plans. Though the demand for more spectrum can be accelerated (1) in highly competitive markets, (2) spectrum starved operations, and/or (3) if customer demand is being poorly managed within the spectral resources available to the MNO.
WiFi, or in general any open radio-access technology operating in ISM bands (i.e., freely available frequency bands such as 2.4GHz, 5.8GHz), can be a source of mitigating costly controlled-spectrum resources by stimulating higher usage of such open-technologies and open-bands.
The cash prevention or cash optimization from open-access technologies and frequency bands should not be under-estimated or forgotten. Even if such open-access deployment models does not make standalone economical sense, is likely to make good sense to use as an integral part for the Next Generation Mobile Data Network perfecting & optimizing open- & controlled radio-access technologies.
The Economics of Spectrum Acquisition, Spectrum Retention & Spectrum Perfection is of such tremendous benefits that it should be on any Operators business plans: short, medium and long-term.
THE ECONOMICS OF SPECTRAL EFFICIENCY
The relative gain in spectral efficiency (as well as other radio performance metrics) with new 3GPP releases has been amazing between R99 and recent HSDPA releases. Lots of progress have been booked on the account of increased receiver and antenna sophistication.
If we compare HSDPA 3.6Mbps (see above Figure) with the first Release of LTE, the spectral efficiency has been improved with a factor 4. Combined with more available bandwidth for LTE, provides an even larger relative boost of supplied bandwidth for increased capacity and customer quality. Do note above relative representation of spectral efficiency gain largely takes away the usual (almost religious) discussions of what is the right spectral efficiency and at what load. The effective (what that may be in your network) spectral efficiency gain moving from one radio-access release or generation to the next would be represented by the above Figure.
Theoretically this is all great! However,
Having the radio-access infrastructure supporting the most spectral efficient technology is the easy part (i.e., thousands of radio nodes), getting your customer base migrated to the most spectral efficient technology is where the challenge starts (i.e., millions of devices).
In other words, to get maximum benefits of a given 3GPP Release gains, an operator needs to migrate his customer-base terminal equipment to that more Efficient Release. This will take time and might be costly, particular if accelerated. Irrespective, migrating a customer base from radio-access A (e.g., GSM) to radio-access B (e.g., LTE), will take time and adhere to normal market dynamics of churn, retention, replacement factors, and gross-adds. The migration to a better radio-access technology can be stimulated by above-market-average acquisition & retention investments and higher-than-market-average terminal equipment subsidies. In the end competitors market reactions to your market actions, will influence the migration time scale very substantially (this is typically under-estimate as competitive driving forces are ignored in most analysis of this problem).
The typical radio-access network modernization cycle has so-far been around 5 years. Modernization is mainly driven by hardware obsolescence and need for more capacity per unit area than older (first & second) generation equipment could provide. The most recent and ongoing modernization cycle combines the need for LTE introduction with 2G and possibly 3G modernization. In some instances retiring relative modern 3G equipment on the expense of getting the latest multi-mode, so-called Single-RAN equipment, deployed, has been assessed to be worth the financial cost of write-off. This new cycle of infrastructure improvements will in relative terms far exceed past upgrades. Software Definable Radios (SDR) with multi-mode (i.e., 2G, 3G, LTE) capabilities are being deployed in one integrated hardware platform, instead of the older generations that were separated with the associated floor space penalty and operational complexity. In theory only Software Maintenance & simple HW upgrades (i.e., CPU, memory, etc..) would be required to migrate from one radio-access technology to another. Have we seen the last HW modernization cycle? … I doubt it very much! (i.e., we still have Cloud and Virtualization concepts going out to the radio node blurring out the need for own core network).
Multi-mode SDRs should in principle provide a more graceful software-dominated radio-evolution to increasingly more efficient radio access; as cellular networks and customers migrate from HSPA to HSPA+ to LTE and to LTE-advanced. However, in order to enable those spectral-efficient superior radio-access technologies, a Mobile Network Operator will have to follow through with high investments (or incur high incremental operational cost) into vastly improved backhaul-solutions and new antenna capabilities than the past access technologies required.
Whilst the radio access network infrastructure has gotten a lot more efficient from a cash perspective, the peripheral supporting parts (i.e., antenna, backhaul, etc..) has gotten a lot more costly in absolute terms (irrespective of relative cost per Byte might be perfectly OKAY).
Thus most of the economics of spectral efficiency can and will be captured within the modernization cycles and new software releases without much ado. However, backhaul and antenna technology investments and increased operational cost is likely to burden cash in the peak of new equipment (including modernization) deployment. Margin pressure is therefor likely if the Opex of supporting the increased performance is not well managed.
To recapture the most important issues of Spectrum Efficiency Economics:
- network infrastructure upgrades, from a hardware as well as software perspective, are required => capital investments, though typically result in better Operational cost.
- optimal customer migration to better and more efficient radio-access technologies => market invest and terminal subsidies.
Boosting spectrum much beyond 6 times today’s mobile data dedicated spectrum position is unlikely to happen within a foreseeable time frame. It is also unlikely to happen in bands that would be very interesting for both providing both excellent depth of coverage and at the same time depth of capacity (i.e., lower frequency bands with lots of bandwidth available). Spectral efficiency will improve with both next generation HSPA+ as well as with LTE and its evolutionary path. However, depending on how we count the relative improvement, it is not going to be sufficient to substantially boost capacity and performance to the level a “1,000 times challenge” would require.
This brings us to the topic of vastly increased cell site density and of course Small Cell Economics.
THE ECONOMICS OF INCREASED CELL SITE DENSITY
It is fairly clear that there will not be a lot new spectrum available in the next 10+ years. The relative increase in cellular bandwidth will come from re-purposing & perfecting existing legacy spectrum (i.e., by re-farming) and acquiring some new bandwidth in the low frequency range (<800MHz) which per definition is not going to provide a lot of bandwidth. The very high-frequency range (>3GHz) will contain a lot of bandwidth, but is only interesting for Small Cell and Femto-cell like deployments (feeding frenzy for Small Cells).
Financially Mobile Operators in mature markets, such as Western Europe, will be lucky to keep their earning and margins stable over the next 8 – 10 years. Mobile revenues are likely to stagnate and possible even decline. Opex pressure will continue to increase (e.g., just simply from inflationary pressures alone). MNOs are unlikely to increase cell site density, if it leads to incremental cost & cash pressure that cannot be recovered by proportional Topline increases. Therefor it should be clear that adding many more cell sites (being it Macro, Pico, Nano or Femto) to meet increasing (often un-managed & unprofitable) cellular demand is economically unwise and unlikely to happen unless followed by Topline benefits.
Increasing cell density dramatically (i.e., 56 times is dramatic!) to meet cellular data demand will only happen if it can be done with little incremental cost & cash pressure.
I have no doubt that distributing mobile data traffic over more and smaller nodes (i.e., decrease traffic per node) and utilize open-access technologies to manage data traffic loads are likely to mitigate some of the cash and margin pressure from supporting the higher performance radio-access technologies.
So let me emphasize that there will always be situations and geographical localized areas where cell site density will be increased disregarding the economics, in order to increase urgent capacity needs or to provide specialized-coverage needs. If an operator has substantially less spectral overhead (e.g., AT&T) than a competitor (e.g., T-Mobile US), the spectrum-starved operator might decide to densify with Small Cells and/or Distributed Antenna Systems (DAS) to be able to continue providing a competitive level of service (e.g., AT&T’s situation in many of its top markets). Such a spectrum starved operator might even have to rely on massive WiFi deployments to continue to provide a decent level of customer service in extreme hot traffic zones (e.g., Times Square in NYC) and remain competitive as well as having a credible future growth story to tell shareholders.
Spectrum-starved mobile operators will move faster and more aggressively to Small Cell Network solutions including advanced (and not-so-advanced) WiFi solutions. This fast learning-curve might in the longer term make up for a poorer spectrum position.
In the following I will consider Small Cells in the widest sense, including solutions based both on controlled frequency spectrum (e.g., HSPA+, LTE bands) as well in the ISM frequency bands (i.e., 2.4GHz and 5.8GHz). The differences between the various Small Cell options will in general translate into more or less cells due to radio-access link-budget differences.
As I have been involved in many projects over the last couple of years looking at WiFi & Small Cell substitution for macro-cellular coverage, I would like to make clear that in my opinion:
A Small Cells Network is not a good technical (or economical viable) solution for substituting macro-cellular coverage for a mobile network operator.
However, Small Cells however are Great for
- Specialized coverage solutions difficult to reach & capture with standard macro-cellular means.
- Localized capacity addition in hot traffic zones.
- Coverage & capacity underlay when macro-cellular cell split options have been exhausted.
The last point in particular becomes important when mobile traffic exceeds the means for macro-cellular expansion possibilities, i.e., typically urban & dense-urban macro-cellular ranges below 200 meters and in some instances maybe below 500 meters pending on the radio-access choice of the Small Cell solution.
Interference concerns will limit the transmit power and coverage range. However our focus are small localized and tailor-made coverage-capacity solutions, not a substituting macro-cellular coverage, range limitation is of lesser concern.
For great accounts of Small Cell network designs please check out Iris Barcia (@IBTwi) & Simon Chapman (@simonchapman) both from Keima Wireless. I recommend the very insightful presentation from Iris “Radio Challenges and Opportunities for Large Scale Small Cell Deployments” which you can find at “3G & 4G Wireless Blog” by Zahid Ghadialy (@zahidtg, a solid telecom knowledge source for our Industry).
When considering small cell deployment it makes good sense to understand the traffic behavior of your customer base. The Figure below illustrates a typical daily data and voice traffic profile across a (mature) cellular network:
- up-to 80% of cellular data traffic happens either at home or at work.+
Currently there is an important trend, indicating that the evening cellular-data peak is disappearing coinciding with the WiFi-peak usage taking over the previous cellular peak hour.
A great source of WiFi behavioral data, as it relates to Smartphone usage, you will find in Thomas Wehmeier’s (Principal Analyst, Informa: @Twehmeier) two pivotal white papers on “Understanding Today’s Smatphone User” Part I and Part II.
The above daily cellular-traffic profile combined with the below Figure on cellular-data usage per customer distributed across network cells
shows us something important when it comes to small cells:
- Most cellular data traffic (per user) is limited to very few cells.
- 80% (50%) of the cellular data traffic (per user) is limited to 3 (1) main cells.
- The higher the cellular data usage (per user) the fewer cells are being used.
It is not only important to understand how data traffic (on a per user) behaves across the cellular network. It is likewise very important to understand how the cellular-data traffic multiplex or aggregate across the cells in the mobile network.
We find in most Western European Mature 3G networks the following trend:
- 20% of the 3G Cells carries 60+% of the 3G data traffic.
- 50% of the 3G Cells carriers 95% or more of the 3G data traffic.
Thus relative few cells carries the bulk of the cellular data traffic. Not surprising really as this trend was even more skewed for GSM voice.
The above trends are all good news for Small Cell deployment. It provides confidence that small cells can be effective means to taking traffic away from macro-cellular areas, where there is no longer an option for conventional capacity expansions (i.e., sectorization, additional carrier or conventional cell splits).
For the Mobile Network Operator, Small Cell Economics is a Total Cost of Ownership exercise comparing Small Cell Network Deployment to other means of adding capacity to the existing mobile network.
The Small Cell Network needs (at least) to be compared to the following alternatives;
- Greenfield Macro-cellular solutions (assuming this is feasible).
- Overlay (co-locate) on existing network grid.
- Sectorization of an existing site solution (i.e., moving from 3 sectors to 3 + n on same site).
Obviously, in the “extreme” cellular-demand limit where non of the above conventional means of providing additional cellular capacity are feasible, Small Cell deployment is the only alternative (besides doing nothing and letting the customer suffer). Irrespective we still need to understand how the economics will work out, as there might be instances where the most reasonable strategy is to let your customer “suffer” best-effort services. This would in particular be the case if there is no real competitive and incremental Topline incentive by adding more capacity.
Competitive circumstances could force some spectrum-starved operators to deploy small cells irrespective of it being financially unfavorable to do so.
Lets begin with the cost structure of a macro-cellular 3G Greenfield Rooftop Site Solution. We take the relevant cost structure of a configuration that we would be most likely to encounter in a Hot Traffic Zone / Metropolitan high-population density area which also is likely to be a candidate area for Small Cell deployment. The Figure below shows the Total Cost of Ownership, broken down in Annualized Capex and Annual Opex, for a Metropolitan 3G macro-cellular rooftop solution:
Note 1: The annualized Capex has been estimated assuming 5 years for RAN Infra, Backaul & Core, and 10 years for Build. It is further assumed that the site is supported by leased-fiber backhaul. Opex is the annual operational expense for maintaining the site solution.
Note 2: Operations Opex category covers Maintenance, Field-Services, Staff cost for Ops, Planning & optimization. The RAN infra Capex category covers: electronics, aggregation, antenna, cabling, installation & commissioning, etc..
Note 3: The above illustrated cost structure reflects what one should expect from a typical European operation. North American or APAC operators will have different cost distributions. Though it is not expected to change conclusions substantially (just redo the math).
When we discuss Small Cell deployment, particular as it relates to WiFi-based small cell deployment, with Infrastructure Suppliers as well as Chip Manufacturers you will get the impression that Small Cell deployment is Almost Free of Capex and Opex; i.e., hardly any build cost, free backhaul and extremely cheap infrastructure supported by no site rental, little maintenance and ultra-low energy consumption.
Obviously if Small Cells cost almost nothing, increasing cell site density with 56 times or more becomes very interesting economics … Unfortunately such ideas are wishful thinking.
For Small Cells not to substantially pressure margins and cash, Small Cell Cost Scaling needs to be very aggressive. If we talk about a 56x increase in cell site density the incremental total cost of ownership should at least be 56 times better than to deploy a macro-cellular expansion. Though let’s not fool ourselves!
No mobile operator would densify their macro cellular network 56 times if absolute cost would proportionally increase!
No Mobile operator would upsize their cellular network in any way unless it is at least margin, cost & cash neutral.
(I have no doubt that out there some are making relative business cases for small cells comparing an equivalent macro-cellular expansion versus deploying Small Cells and coming up with great cases … This would be silly of course, not that this have ever prevented such cases to be made and presented to Boards and CxOs).
The most problematic cost areas from a scaling perspective (relative to a macro-cellular Greenfield Site) are (a) Site Rental (lamp posts, shopping malls,), (b) Backhaul Cost (if relying on Cable, xDSL or Fiber connectivity), (c) Operational Cost (complexity in numbers, safety & security) and (d) Site Build Cost (legal requirements, safety & security,..).
In most realistic cases (I have seen) we will find a 1:12 to 1:20 Total Cost of Ownership difference between a Small Cell unit cost and that of a Macro-Cellular Rooftop’s unit cost. While unit Capex can be reduced very substantially, the Operational Expense scaling is a lot harder to get down to the level required for very extensive Small Cell deployments.
For a typical metropolitan rooftop (in Western Europe) we have the annualized capital expense (Capex) of ca. 15,000 Euro and operational expenses (Opex) in the order of 30,000 Euro per annum. The site-related Opex distribution would look something like this;
- Macro-cellular Rooftop 3G Site Unit Annual Opex:
- Site lease would be ca. 10,500EUR.
- Backhaul would be ca. 9,000EUR.
- Energy would be ca. 3,000EUR.
- Operations would be ca. 7,500EUR.
- i.e., total unit Opex of 30,000EUR (for average major metropolitan area)
Assuming that all cost categories could be scaled back with a factor 56 (note: very big assumption that all cost elements can be scaled back with same factor!)
- Target Unit Annual Opex cost for a Small Cell:
- Site lease should be less than 200EUR (lamp post leases substantially higher)
- Backhaul should be less than 150EUR (doable though not for carrier grade QoS).
- Energy should be less than 50EUR (very challenging for todays electronics)
- Operations should be less than 150EUR (ca. 1 hour FTE per year … challenging).
- Annual unit Opex should be less than 550EUR (not very likely to be realizable).
Similar for the Small Cell unit Capital expense (Capex) would need to be done for less than 270EUR to be fully scalable with a macro-cellular rooftop (i.e., based on 56 times scaling).
- Target Unit Annualized Capex cost for a Small Cell:
- RAN Infra should be less than 100EUR (Simple WiFi maybe doable, Cellular challenging)
- Backhaul would be less than 50EUR (simple router/switch/microwave maybe doable).
- Build would be less than 100EUR (very challenging even to cover labor).
- Core would be less than 20EUR (doable at scale).
- Annualized Capex should be less than 270EUR (very challenging to meet this target)
- Note: annualization factor: 5 years for all including Build.
So we have a Total Cost of Ownership TARGET for a Small Cell of ca. 800EUR
Inspecting the various capital as well as operational expense categories illustrates the huge challenge to be TCO comparable to a macro-cellular urban/dense-urban 3G-site configuration.
Massive Small Cell Deployment needs to be almost without incremental cost to the Mobile Network Operator to be a reasonable scenario for the 1,000 times challenge.
Most the analysis I have seen, as well as carried out myself, on real cost structure and aggressive pricing & solution designs shows that the if the Small Cell Network can be kept between 12 to 20 Cells (or Nodes) the TCO compares favorably to (i.e., beating) an equivalent macro-cellular solution. If the Mobile Operator is also a Fixed Broadband Operator (or have favorable partnership with one) there are in general better cost scaling possible than above would assume (e.g., another AT&T advantage in their DAS / Small Cell strategy).
In realistic costing scenarios so far, Small Cell economical boundaries are given by the Figure below:
Let me emphasize that above obviously assumes that an operator have a choice between deploying a Small Cell Network and conventional Cell Split, Nodal Overlay (or co-location on existing cellular site) or Sectorization (if spectral capacity allows). In the Future and in Hot Traffic Zones this might not be the case. Leaving Small Cell Network deployment or letting the customers “suffer” poorer QoS be the only options left to the mobile network operator.
So how can we (i.e., the Mobile Operator) improve the Economics of Small Cell deployment?
Having access fixed broadband such as fiber or high-quality cable infrastructure would make the backhaul scaling a lot better. Being a mobile and fixed broadband provider does become very advantageous for Small Cell Network Economics. However, the site lease (and maintenance) scaling remains a problem as lampposts or other interesting Small Cell locations might not scale very aggressively (e.g., there are examples of lamppost leases being as expensive as regular rooftop locations). From a capital investment point of view, I have my doubts whether the price will scale downwards as favorable as they would need to be. Much of the capacity gain comes from very sophisticated antenna configurations that is difficult to see being extremely cheap:
Small Cell Equipment Suppliers would need to provide a Carrier-grade solution priced at maximum 1,000EUR all included! to have a fighting chance of making massive small cell network deployment really economical.
We could assume that most of the “Small Cells” are in fact customers existing private access points (or our customers employers access points) and simply push (almost) all cellular data traffic onto those whenever a customer is in vicinity of such. All those existing and future private access points are (at least in Western Europe) connected to at least fairly good quality fixed backhaul in the form of VDSL, Cable (DOCSIS3), and eventually Fiber. This would obviously improve the TCO of “Small Cells” tremendously … Right?
Well it would reduce the MNOs TCO (as it shift the cost burden to the operator’s customer or employers of those customers) …Well … This picture also would not really be Small Cells in the sense of proper designed and integrated cells in the Cellular sense of the word, providing the operator end-2-end control of his customers service experience. In fact taking the above scenario to the extreme we might not need Small Cells at all, in the Cellular sense, or at least dramatically less than using the standard cellular capacity formula above.
In Qualcomm (as well as many infrastructure suppliers) ultimate vision the 1,000x challenge is solved by moving towards a super-heterogeneous network that consist of everything from Cellular Small Cells, Public & Private WiFi access points as well as Femto cells thrown into the equation as well.
Such an ultimate picture might indeed make the Small Cell challenge economically feasible. However, it does very fundamentally change the current operational MNO business model and it is not clear that transition comes without cost and only benefits.
Last but not least it is pretty clear than instead of 3 – 5 MNOs all going out plastering walls and lampposts with Small Cell Nodes & Antennas sharing might be an incredible clever idea. In fact I would not be altogether surprised if we will see new independent business models providing Shared Small Cell solutions for incumbent Mobile Network Operators.
Before closing the Blog, I do find it instructive to pause and reflect on lessons from Japan’s massive WiFi deployment. It might serves as a lesson to massive Small Cell Network deployment as well and an indication that collaboration might be a lot smarter than competition when it comes to such deployment: