Archive for July, 2012

Social Media Valuation …. a walk on the wild side.

Lately I have wondered about Social Media Companies and their Financial Valuations. Is it hot air in a balloon that can blow up any day? Or are the hundred of millions and billions of US Dollars tied to Social Media Valuations reasonable and sustainable in the longer run? Last question is particular important as more than 70% of the value in Social Media are 5 or many more years out in the Future.  Social Media startup companies, without any turnover, are regularly being  bought for, or able to raise money at a value, in the hundreds of millions US dollar range. Lately, Instagram was bought by Facebook for 1 Billion US Dollar. Facebook itself valued at a $100B at its IPO. Now several month after their initial public offering, Facebook may have lost as much as 50% of the originally claimed IPO value.

The Value of Facebook, since its IPO,  has lost ca. 500 Million US Dollar per day (as off 30-July-2012).

What is the valuation make-up of Social Media? And more interestingly what are the conditions that need to be met to justify $100B or $50B for Facebook, $8B for Twitter, $3B (as of 30-July-2012, $5B prior to Q2 Financials) or $1B for Instagram, a 2 year old company with a cool mobile phone Photo App? Is the Social Media Business Models Real? or based on an almost religious belief that someday in the future it will Return On Investment. Justifying the amount of money pumped into it?

My curiosity and analytical “hackaton” got sparked by the following Tweet:

Indeed! what could possible justify paying 1 Billion US Dollar for Instagram, which agreeably has a very cool FREE Smartphone Photo App (far better than Facebook’s own), BUT without any income?

  • Instagram, initially an iOS App, claims 50 Million Mobile Users (ca. 5 Million unique visitors and 31 Million page-views as of July 2012). 5+M photos are uploaded daily with a total of 1+ Billion photos uploaded. No reported revenues to date. Prior to being bought by Facebook for $1 Billion, was supposed to have been prepared for a new founding round valued at 500 Million US$.
  • Facebook has 900M users, 526M (58%) active daily and 500M mobile users (May 2012). 250M photos are uploaded daily with a total of 150 Billion photos. Facebook generated ca. $5B in revenue in 2011 and current market cap is ca. $61B (24 July 2012). 85% of FB revenue in 2011 came from advertisement.

The transaction gives a whole new meaning to “A picture is worth a Billion words”  … and Instagram is ALL about PICTURES & SOCIAL interactions!

Instagram is a (really cool & simple) mobile & smartphone optimized App. Something that would be difficult to say about FB’s mobile environment (in particular when it comes to photo experience).

One thing is of course clear. If FB is willing to lay down $1B for Instagram, their valuation should be a good deal higher than $1B (i.e., ca. $4+B?). It will be very interesting to see how FB plans to monetize Instagram. Though the acquisition might be seen as longer-outlook protective move to secure Facebook’s share of the Mobile Market, which for Social Media will become much more important than the traditional desktop access.

So how can we get a reality check on a given valuation?

Lets first look at the main Business Models of today (i.e., how the money will be or are made);

  1. Capture advertising spend – typically online advertisement spend (total of $94B in 2012 out of an expected total Media Ad spend of $530B). With uptake of tablets traditional “printed media” advertising spend might be up for grabs as well (i.e., getting a higher share of the total Media Ad spend).
  2. Virtual Goods & credits (e.g., Zynga’s games and FB’s revenue share model) – The Virtual Economy has been projected to be ca. $3B in 2012 (cumulative annual growth rate of 35% from 2010).
  3. Payed subscriptions (e.g., LinkedIn’s Premium Accounts: Business Plus, Job Seeker, etc or like Spotify Premium, etc..).
  4. B2B Services (e.g.,. LinkedIn’s Hiring Solutions).

The Online Advertisement Spend is currently the single biggest source of revenue for the Social Media Business Model. For example Google (which is more internet search than Social Media) takes almost 50% of the total available online advertisement spend and it accounts for more than 95% of Google’s revenues. In contrast, Facebook in 2011 only captured ca. 4+% of Online Ad Spend which accounted for ca. 85% of FB’s total revenue. By 2015 eMarketeer.com (see http://www.emarketer.com/PressRelease.aspx?R=1008479) has projected the total online advertisement spend could be in the order of $132B (+65% increase compared to 2011). USA and Western Europe is expected to account for 67% of the $132B by 2015.

Virtual Goods are expected to turn-over ca. $3B in 2012. The revenue potential from Social Networks and Mobile has been projected (see Lazard Capital’s Atul Bagga ppt on “Emerging Trends in Games-as-a-Service”) to be ca. $10B worldwide by 2015. If (and that is a very big if) the trend would continue the 2020 potential would be in the order of $60B (though I would expect this to be a maximum and very optimistic upside potential).

So how can a pedestrian get an idea about Social Media valuation? How can one get a reality check on these Billionaires being created en mass at the moment in the Social Media sphere?

“Just for fun” (and before I get really “serious”) I decided see whether there is any correlation between a given valuation and the number of Unique Visitors (per month) and Pageviews (per month) … my possible oversimplified logic would be that if the main part of the Social Media business model is to get a share of the Online Advertisement Spending there needs to be some sort of dependency on the those (i..e, obviously whats really important is the clickthrough (rate) but lets be forget this for a moment or two):

 The two charts (log-log scaled) shows Valuation (in Billion US$) versus Unique Visitors (in Millions) and Pageviews (in Billions). While the correlations are not perfect, they are really not that crazy either. I should stress that the correlations are power-law correlations NOT LINEAR, i.e., Valuation increases with power of unique and active users/visitors.

An interesting out-lier is Pinterest. Let’s just agree that this does per see mean that Pinterest’s valuation at $1.5B is too low! … it could also imply that the rest are somewhat on the high side! 😉

Note: Unique Visitors and Pageview statistics can be taken from Google’s DoubleClick Ad Planner. It is a wonderful source of domain attractiveness, usage and user information.

Companies considered in Charts: Google, Facebook, Yahoo, LinkedIN, Twitter, Groupon, Zynga, AOL, Pinterest, Instagram (@ $1B), Evernote, Tumblr, Foursquare, Baidu.

That’s all fine … but we can (and should) do better than that!

eMarketeer.com has given us a Online Advertisement Spend forecast (at least until 2015). In 2011, the Google’s share amounted to 95% of their revenue and for Facebook at least 85%. So we are pretty close to having an idea of the Topline (or revenue) potential going forward. In addition, we also need to understand how that Revenue translates into Free Cash Flow (FCF) which will be the basis for my simple valuation analysis. To get to a Free Cash Flow picture we could develop a detailed P&L model for the company of interests. Certainly an interesting exercise but would require “Millions” of educated guesses and assumptions for a business that we don’t really know.

Modelling a company’s P&L is not really a peaceful walk for our interested pedestrian to take.

A little research using Google Finance, Yahoo Finance or for example Ycharts.com (nope! I am not being sponsored;-) will in general reveal a typical cash yield (i.e., amount of FCF to Revenue) for a given type of company in a given business cycle.

Examples of FCF performance relative to Revenues: Google for example has had an average FCF yield of 30% over the last 4 years, Yahoo’s 4 year average was 12% (between 2003 and 2007 Google and Yahoo had farily similar yields ).  Facebook has been increasing its yield steadily from 2009 (ca. 16%) to 2011 (ca. 25%), while Zynga had 45% in 2010 and then down to 13% in 2011.

So having an impression of the revenue potential (i.e., from eMarketeer) and an idea of best practice free cash flow yield, we can start getting an idea of the Value of a given company. It should of course be clear that we can also turn this Simple Analysis around and ask what should the Revenue & Yield be in order to justify a given valuation. This would give a reality check on a given valuation as the Revenue should be in reasonable relation to market and business expectations.

Lets start with Google (for the moment totally ignoring Motorola;-):

Nothing fancy! I am basically assuming Google can keep their share of Online Advertising Spend (as taken from eMarketeer) and that Google can keep their FCF Yield at a 30% level. The discount rate (or WACC) of 9% currently seems to be a fair benchmark (http://www.wikiwealth.com/wacc-analysis:goog). I am (trying) to be conservative and assumes a 0% future growth rate (i.e., changing will in general have a high impact on the Terminal Value). If all this comes true, Google’s value would be around 190 Billion US Dollars. Today (26 July 2012) Google Finance tells me that their Market Capitalization is $198B (see http://www.google.com/finance?q=NASDAQ:GOOG) which is 3% higher than the very simple model above.

How does the valuation picture look for Facebook (pre-Zynga results as of yesterday 25 July 2012):

First thought is HALLELUJAH … Facebook is really worth 100 Billion US Dollars! … ca. $46.7 per share… JAIN (as they would say in Germany) … meaning YESNO!

  • Only if Facebook can grow from capturing ca. 6% of the Online Advertisement Spend today to 20% in the next 5 – 6 years.
  • Only if Facebook can improve their Free Cash Flow Yield from today’s ca. 25% to 30%.
  • Only if Facebooks other revenues (i.e., from Virtual Goods, Zynga, etc..) can grow to be 20% of their business.

What could possible go wrong?

  • Facebook fatigue … users leaving FB to something else (lets be honest! FB has become a very complex user interface and “sort of sucks” on the mobile platforms. I guess one reason for Instagram acquisition).
  • Disruptive competitors/trends (which FB cannot keep buying up before they get serious) … just matter of time. I expect this to happen first in the Mobile Segment and then spread to desktop/laptop.
  • Non-advertisement revenues (e.g., from Virtual Goods, Zynga, etc..) disappoints.
  • Need increasing investments in infrastructure to support customer and usage growth (i.e., negative impact on cash yields).
  • The Social Media business being much more volatile than current hype would allow us to assume.

So how would a possible more realistic case look like for Facebook?

Here I assume that Facebook will grow to take 15% (versus 20% above) of the Online Ad spend. Facebook can keep a 25% FCF Yield (versus growing to 30% in the above model). The contribution from Other Revenues has been brought down to a more realistic level of the Virtual Goods and Social Media Gaming expectations (see for example Atul Bagga, Lazard Capital Markets, analysis http://twvideo01.ubm-).

The more conservative assumptions (though with 32% annual revenue growth hardly a very dark outlook) results in a valuation of $56 Billion (i.e., a share price of ca. $26). A little bit more than half the previous (much) more optimistic outlook for Facebook. Not bad at all of course … but maybe not what you want to see if you paid a premium for the Facebook share? Facebook’s current market capitalization (26 July 2012, 18:43 CET) is ca. $60B (i..e, $28/share).

So what is Facebooks value? $100B (maybe not), $50+B? or around $60+B? Well it all depends on how shareholders believe Facebook’s business to evolve over the next 5 – 10 (and beyond) years. If you are in for the long run it would be better to be conservative and keep the lower valuation in mind rather than the $100B upside.

Very few of us actually sit down and do a little estimation ourselves (we follow others = in a certain sense we are financial lemmings). With a little bit of Google Search (yes there is a reason why they are so valuable;-) and a couple of lines of Excel (or pen and paper) it is possible to get an educated idea about a certain valuation range and see whether the price you paid was fair or not.

Lets just make a little detour!

Compare Facebook’s current market capitalization of ca. $60B (@ 26 July 2012, 18:43 CET) at $3.7B Revenue (2011) and ca. $1B of free cash flow (2011). Clearly all value is in anticipation of future business! Compare this with Deutsche Telecom AG with a market capitalization of ca. $50B at $59B (2011, down -6% YoY2010) and ca. $7.8B of free cash flow (2011). It is Fascinating that a business with well defined business model, paying customers, healthy revenue (16xFB) and cash flow (8xFB) can be worth a lot less than a company that relies solely on anticipation of a great future.  Facebook’s / Social Media Business Model future appear a lot more optimistic (the blissfull unknown) than the Traditional Telco Business model (the known” unknown). Social Media by 2015 is a game of maybe a couple of hundred Billions (mainly from advertisement, app sales and virtual economy) versus the Telecom Mobile (ignoring the fixed side) of a Trillion + (1,000 x Billion) business.

Getting back to Social Media and Instragram!

So coming back to Instagram … is it worth paying $1B for?

Let’s remind ourselves that Instagram is a Mobile Social Media Photo sharing platform (or Application) serving Apple iOS (originally exclusively so) and Android. Instagram has ca. 50+M registered users (by Q1’2012) with 5+M photos uploaded per day with a total of 1+B photos uploaded. The Instagram is a through-rough optimized smartphone application. There are currently more than 460+ photo  apps with 60Photos being a second to Instagram in monthly usage (http://www.socialbakers.com/facebook-applications/category/70-photo).

Anyway, to get an idea about Instagram’s valuation potential, it would appear reasonable to assume that their Business Model would target the Mobile Advertisement Spend (which is a sub-set of Online Ad Spend). To get somewhere with our simple valuation framework I assume:

  1. that Instagram can capture up to 10% of the Mobile Adv Spend by 2015 – 2016 (possible Facebook boost effect, better payment deals. Keep ad revenue with Facebook).
  2. Instagram’s  a revenue share dynamics similar to Facebooks initial revenue growth from Online Ad Spend (possible Facebook boost effect, better payment deals. Keep ad revenue with Facebook).
  3. Instagram could manage a FCF Yield to 15% over the period analysed (there could be substantial synergies with Facebook capital expenditures).

In principle the answer to that question above is YES paying $1B for Instagram would be worth it as we get almost $5B from our small and simple valuation exercise … if one believes;

  1. Instagram can capture 10% of the Mobile Advertisement Spend (over the next 5 – 6 years).
  2. Instagram can manage a Free Cash Flow Yield of at least 15% by Year 6.

Interesting looking at the next 5 years would indicate a value in the order of $500M. This is close to the rumored funding round that was in preparation before Facebook laid down $1B. However and not surprising most of the value for Instagram comes from the beyond 5 years. The Terminal Value amounts to 90% of the Enterprise Value.

For Facebook to breakeven on their investment, Instagram would need to capture no more than 3% of the Mobile Ad Spend over the 5 year period (assuming that the FCF Yield remain at 10% and not improving due to scale).

Irrespective;

Most of the Value of Social Media is in the Expectations of the Future.

70+% of Social Media Valuation relies on the Business Model remaining valid beyond the first 5 years.

With this in mind and knowing that we the next 5 years will see a massive move from desktop dominated Social Media to Mobile dominated Social Media, should make us somewhat nervous about desktop originated Social Media Businesses and whether these can and will make the transformation.

The question we should ask is:

Tomorrow, will today’s dot-socials be yesterday’s busted dot-coms?

PS

For the pedestrian that want to get deeper into the mud of valuation methodologies I can really recommend “Valuation: Measuring & Managing the Value of Companies” by Tim Koller, Marc Goedhart & David Wessels (http://www.amazon.com/Valuation-Measuring-Managing-Companies-Edition/dp/0470424656). Further there are some really cool modelling exercises to be done on the advertisement spend projections and the drivers behind as well as a deeper understand (i.e., modeling) of the capital requirements and structure of Social Media Business Models.

In case of interest in the simple models used here and the various sources … don’t be a stranger … get in touch!

PSPS (as of 28-July-2012) – A note on Estimated Facebook Market Capitalization

In the above Facebook valuation commentary I have used the information from Google Finance (http://www.google.com/finance?q=facebook) and Yahoo Finance (http://finance.yahoo.com/q?s=FB) both basing their Market Capitalization estimation on 2.14B Shares. MarketWatch (http://www.marketwatch.com/investing/stock/fb) appear to use 2.75B shares (i.e., 29% high than Google & Yahoo). Obviously, MarketWatch market capitalization thus are higher than what Google & Yahoo would estimate.

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Mobile Data Consumption, the Average Truth? the Average Lie?

“Figures often beguile me” leading to the statement that “There are three kinds of lies: lies, damned lies, and statistics.” (Mark Twain, 1906).

We are so used to averages … Read any blog or newspaper article trying to capture a complex issue and its more than likely that you are being told a story of averages … Adding to Mark Twain’s quote on Lies, in our data intense world ” The Average is often enough the road to an un-intentional Lie” .. or just about “The Average Lie” .

Imagine this! Having (at the same time) your feet in the oven at 80C and you head in the freezer at -6C … You would be perfectly OK! On average! as your average temperature would equal 80C + (-6C) divided by 2 which is 37C, i.e., the normal and recommended body temperature for an adult human being. However both your feet and your head is likely to suffer from such an experiment (and therefore really should not be tried out … or left to Finns used to Sauna and Icy water … though even the Finns seldom enjoyed this simultaneously).

Try this! Add together the age of the members your household and divide by the number of members. This would give you the average age of your household … does the average age you calculated have any meaning? … if you have young children or grandparents living with you, I think that there is a fairly high chance that the answers to that question is NO! …  The average age of my family”s household is 28 years. However, this number is a meaningless average representation of my household. It is 20 times higher than my sons age and about 40% lower than my own age.

Most numbers, most conclusions, most stories, most (average) analysis are based on an average representation of one or another Reality …. and as such can easily lead to Reality Distortion.

When we are presented with averages (or mean values as it is also called in statistics), we tend to substitute Average with Normal and believe that the story represents most of us (i.e., statistically this means about 68% of us all). More often than not we sit back with the funny feeling that if what we just read is “normal” then maybe we are not.

On mobile data consumption (I ll come back to Smartphone data consumption a bit later) … There is one (non-average) truth about mobile data consumption that has widely (and correctly) been communicated …

Very few mobile customers (10%) consumes the very most of the mobile data traffic (90%).

(see for example: http://www.nytimes.com/2012/01/06/technology/top-1-of-mobile-users-use-half-of-worlds-wireless-bandwidth.html/).

Lets just assume that a mobile operator make claim to an average 200MB monthly consumption (source: http://gigaom.com/broadband/despite-critics-cisco-stands-by-its-data-deluge/). Lets assume that 10% of customer base generating 90% of the traffic. It follows that the high usage segment has an average  volumetric usage of 1,800MB and the low usage segment an average volumetric usage of only 22MB.  In other words 10% of the customer base have 80+ times higher consumption than the remaining 90%. The initial average consumption (taken across the whole customer base) of 200MB communicated is actually 9 times higher than the average consumption of 90% of the customer base. It follows (with some use case exceptions) that the 10% high usage segment spends a lot more Network Resources and Time. The time the high usage segment spend actively with their device are likely to be a lot higher than the 90% low usage segment.

The 200MB is hardly normal! It is one of many averages that can be calculated. Obviously 200MB is a lot more “sexy” than to state that 90% of the customer base consumes typically 22MB.

Created using PiktoChart http://app.piktochart.com.

Do Care about Measurement and Data Processing!

What further complicates consumptive values being quoted is how the underlying data have been measured, processed and calculated!

  1. Is the averaging done over the whole customer base?,
  2. Is the averaging done over active customers?, or
  3. A subset of active customers (i.e., 2G vs 3G, 3G vs HSPA+ vs LTE vs WiFi, smartphone  vs basic phone, iPad vs iPhone vs Laptop, prepaid vs postpaid, etc..) or
  4. A smaller subset based on particular sample criteria (i.e., iOS, Android, iPad, iPhone, Galaxy, price plan, etc..) or availability (mobile Apps installed, customer approval, etc..).  or …

Without knowing the basis of a given average number any bright analysis or cool conclusion might be little more than Conjecture or Clever Spin.

On Smartphone Usage

One the most recent publicized studies on Smartphone usage comes from O2/Telefonica UK (Source: http://mediacentre.o2.co.uk/Press-Releases/Making-calls-has-become-fifth-most-frequent-use-for-a-Smartphone-for-newly-networked-generation-of-users-390.aspx). The O2 data provides an overview of average daily Smartphone usage across 10 use case categories.

The O2’s Smartphone statistics have been broken down in detail by one of our industry”s brightest Tomi Ahonen (A Must Read http://www.communities-dominate.blogs.com/ though it is drowning in his Nokia/Mr. Elop “Howler Letters”). Tomi points out the Smartphone’s disruptive replacement potential of many legacy consumer products (e.g., think: watch, alarm clock, camera,  etc..).

The O2 Smartphone data is intuitive and exactly what one would expect! Boring really! Possible with the exception of Tomi’s story telling (see above reference)! The data was so boring that The Telegraph (source: http://www.telegraph.co.uk/technology/mobile-phones/9365085/Smartphones-hardly-used-for-calls.html) had to conclude that “Smartphones Hardly Used for Calls”. Relative to other uses of course not really an untruth.

Though The Telegraph did miss 9or did not care) the fact that both Calls and SMS appeared to be what one would expect (and why would a Smartphone generate more Voice and SMS than Normal? … hmmmm). Obviously, the Smartphone is used for a lot of other stuff than calling and SMSing! The data tells us that an average Smartphone user (whatever that means) spend ca. 42 minutes on web browsing and social networking while “only” 22 minutes on Calls and SMS (i.e., actually 9 minutes of SMS sounds more like a teenager than a high-end smartphone user … but never mind that!). There are lots of other stuff going on with that Smartphone. In fact out of the total daily usage of 128 minutes only 17% of the time (i.e., 22 minutes) is used for Plain Old Mobile Telephony Services (The POMTS). We do however find that both voice minutes and legacy messaging consumption are declining faster in the Smartphone segment than for Basic Phones (which are declining rapidly as well) as OTT Mobile Apps alternatives substitute POMTS (see inserted chart from http://www.slideshare.net/KimKyllesbechLarsen/de-risking-the-broadband-business-model-kkl2411201108x).

I have no doubt that the O2 data represents an averaging across a given Smartphone sample, the question is how does this data help us to understand the Real Smartphone User and his behavior.

So how did O2 measure this data?

(1) To be reliable and reasonable, data collection should be done by an App residing in the O2 customer’s smartphone. An alternative (2) would be deep packet inspection (dpi) but this would only capture network usage which can (and in most cases will be) very different from the time the customer actively uses his Smartphone. (3) Obviously the data could also be collected by old fashion Questionnaires being filled in. This would be notoriously unreliable and I cannot imagine this being the source.

Thus, I am making the reasonable guess that the Smartphone Data Collection is mobile App based.

“Thousand and 1 Questions”: Does the data collected represents a normal O2 Smartphone user? or a particular segment that don’t mind having a Software Sniffer (i.e., The Sniffer) on the used device reporting his behavior? Is “The Sniffer” a standard already installed (and activated?) App on all Smartphone devices?, only on a certain segment? or is it downloadable? (i..e, which would require a certain effort from the customer), is the collection done for both prepaid & contract customers, both old and new smartphones (i.e., usage patterns depends on OS version/type, device capabilities such as air interface speed DL & UL, CPU, memory management, etc..) … is WiFi included or excluded?, what about Apps running in the background (are these included), etc…

I should point out that it is always much easier to poke at somebody else data analysis than it often is to collect, analyse and present such data. Though, depending on the answer to the above “1,000 + 1” questions the O2 data either becomes a fair representation of an O2 Smartphone customer or “just” an interesting data point for one of their segments.

If the average Smartphone cellular (i.e., no WiFi blend) monthly consumption in UK is ca. 450MB (+/-50MB) and if the consumer had on average cellular speed of 0.5Mbps (i.e., likely conservative with exception of streaming services which could be lower), one would expect that Time spend consuming Network Resources would be no more than 120 minutes per month or 5 minutes per day (@ R99 384kbps this would be ca. 6 min per day). If I would chose a more sophisticated QoS distribution, the Network Consumption Time would anyway not change with an order of magnitude or more.

So we have 5 minutes of Mobile Data Network Time Consumption daily versus O2’s Smartphone usage time of 106 minutes (wo Calls & SMS) … A factor 22 in difference!

For every minutes of mobile data network consumption the customer spends 20+ minutes actively with his device (i.e., reading, writing, playing, etc..).

So …. Can we trust the O2 Smartphone data?

Trend wise the data certainly appear reasonable! Whether the data represents a majority of the O2 smartphone users or not … I doubt somewhat. However, without having a more detailed explanation of data collection, sampling, and analysis it’s difficult to conclude how representable the O2 Smartphone data really is for their Smartphone customers.

Alas this is the problem with most of the mobile data user and usage statistics being presented to the public as an average (i.e., have had my share of this challenge as well).

Clearly we spend a lot more time with our device than the device spends actively at the mobile network. This trend has been known for a long time from the fixed internet. O2 points out that the Smartphone, with its mobile applications, has become the digital equivalent to a “Swiss Army Knife” and as a consequence (as Tomi also points out in his Blog) already in the process of replacing a host of legacy consumer devices, such as the watch, alarm clock, camera (both still pictures and video), books, music radios, and of course last but not least substituting The POMTS.

I have made argued and shown examples that Average Numbers we are presented with are notorious by character. What other choices do we have?  Would it be better to report the Median? rather than the Average (or  Mean)? The Median divides a given consumptive distribution in half (i.e., 50% of customers have a consumption below the Median and 50% above). Alternative we could report the Mode which would give us the most frequent consumption across our consumer distribution.

Of course if consumer usage was distributed normally (i.e., symmetric bell shaped) Mean, Median and Mode would be one and the same (and we would all be happy and bored). Not so much luck!

Most consumptive behaviors tends to be much more skewed and asymmetric (i.e., “the few takes the most”) than the normal distribution (that most of us instinctively uses when we are presented with figures). Most people are not likely to spend much thought on how a given number is calculated. However, it might be constructive to provide a %tage of the customers for which their usage is below the reported average. The reader should however note that in case the percentage figure is different from 50%, the consumptive distribution is skewed and

onset of Reality Distortion has occurred.

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Wireless Broadband Access (BWA) Greenfield Ambition… (from March 2008)

In case you are contemplating starting a wireless broadband, maybe even mobile broadband, greenfield operation in Europe there will be plenty of opportunity the next 1 to 2 years.Will it be a great business in Western Europes mature market? – probably not – but it still might be worth pursuing. The mobile incumbants will have a huge edge when it comes to spectrum and capacity for growth which will be very difficult to compete against for a Greenfield with comparable limited spectrum.Upcoming 2.50 GHz to 2.69 GHz spectrum (i.e., 2.6 GHz for short) auctions, often refered to as the UMTS extension band spectrum, are being innitiated in several European countries (United Kingdom, The Netherlands, Sweden, etc..). Thus, we are talking about 190 MHz of bandwidth up for sale to the highest bidder(s). Compared this with the UMTS auction at the 2.1 GHz band which was 140 Mhz. The European Commission has recommended to split up the 190 MHz into 2×70 MHz for FDD operations (basically known as UMTS extension band in some countries) and a (minimum ) 1×50 MHz part for TDD operation.

In general it is expected that incumbent mobile operators (e.g., Vodafone, T-Mobile, KPN, Orange, Telefonica/O2, etc..) will bid for the 2.6 GHz FDD spectrum, supplementing their existing UMTS 2.10 GHz spectrum mitigating possible growth limitation they might foresee in the future. The TDD spectrum is in particular expected to be contended by new companies, greenfield operations as well as fixed-line operators (i.e, BT) with the ambition to launch broadband wireless access BWA (i..e, WiMAX) networks. Thus, new companies which intend to compete with today’s mobile operators and their mobile broadband data proporsitions. Furthermore, just as mobile operators with broadband data competes with fixed broadband business (i.e., DSL & cable); so is it expected that the new players would likewise compete with both existing fixed and mobile broadband data proporsitions. Obviously, new business might not limit their business models to broadband data but also provide voice offerings.

Thus, the competive climate would become stronger as more players contend for the same customers and those customer’s wallet.

Let’s analyse the Greenfields possible business model as the economical value of starting up a broadband data business in mature markets of Western Europe. The analysis will be done on a fairly high level which would give us an indication of the value of the Greenfield Business model as well as what options a new business would have to optimize that value.

FDD vs TDD Spectrum

The 2.6 GHz auction is in its principles assymetric, allocating more bandwidth to FDD based operation than to TDD-based Broadband Wireless Access (BWA) deployment; 2×70 MHz vs 1×50 MHz. It appears fair to assuming that most incumbent operators will target 2×20 MHz FDD which coincide with the minimum bandwidth target for the Next-Generation Mobile Network (NGMN)/Long-Term Evolution (LTE) Network vision (ref: 3GPP LTE).

For the entrant interested in the part of the 1×50 MHz TDD spectrum would in worst case need 3x the FDD spectrum to get an equivalent per sector capacity as an FDD player, i.e., 2×20 MHz FDD equivalent to 1×60 MHz TDD with a frequency re-use of 3 used by the TDD operator. Thus, in a like-for-like a TDD player would have difficulty matching the incumbants spectrum position at 2.6 GHz (ignoring the incumbant having a significantly stronger spectrum position from the beginning).

Of course better antenna systems (moving to re-use 1), improved radio resource management, higher spectral efficiency (i.e., Mbps/MHz) as well as improved overall link budgets might mitigate possible disadvantage in spectral assymmetry benefiting the TDD player. However, those advantages are more a matter of time before competing access technologies bridge an existing performance gab (technology equivalent tit-for-tat).

Comparing actual network performance of FDD-based UMTS/HSPA (High-Speed Packet Access) with WiMAX 802.16e-2005 the performance is roughly equivalent in terms of spectral efficiency. However, in general in Europe there has been allocated far more FDD-based spectrum than TDD-based which overall does result in a considerable capacity and growth issues for TDD-based business models. Long-Term Evolution (LTE) path is likely to be developed both for FDD and TDD based access and equivalent performance might be expected in terms of bits-per-second to Hz performance.

Thus, it is likely that a TDD-based network would become capacity limited sooner than a mobile operator having a full portfolio of FDD-based spectrum (i.e., 900 MHz (GSM), 1800 MHz (GSM), 2,100 MHz (FDD UMTS) and 2,500 MHz (FDD – UMTS/LTE) to its disposition. Therefore, a TDD based business model could be expected to look differently than an incumbants mobile operators existing business model.

The Greenfield BWA Business Case

Assume that Greenfield BWA intends to start-up its BWA business in a market with 17 million inhabitants, 7.4 million households, and a surface area of 34,000 km2. The Greenfield’s business model is based on house-hold coverage with focus on Urban and Sub-Urban areas covering 80% of the population and 60% of the surface area.

It is worth mentioning that the valuation approach presented here is high-level and should not replace proper financial modelling and due dilligence. This said, the following approach does provide a good guidance to the attractiveness of a business proporsition.

Greenfield BWA – The Technology Part

The first exercise the business modeller is facing is to size the network needed consistent with the business requirements and vision. How many radio nodes would be required to provide coverage and support the projected demand – is the question to ask! Given frequency and radio technology it is relative straightforward to provide a business model estimate of the site numbers needed.

Using standard radio engineering framework (e.g., Cost231 Walfish-Ikegami cell range model (Ref.:Cost321)) a reasonable estimate for a typical maximum cell range which can be expected subject to the radio environment (i.e, dense-city, urban, sub-urban and rural). Greenfield BWA intends to deploy (mobile) WiMAX at 2.6 GHz. Using the standard radio engineering formula a 1.5 km @ 2.6 GHz Uplink limited cell range is estimated. Uplink limited implies that the range between the Customer Premise Equipment (CPE) and the Basestation (BS) is shorter than the other direction from BS to CPE. This is a normal situation as the CPE equipment often is the limiting factor in network deployment considerations.

The 1.5-km cell range we have estimated above should be compared with typical cell ranges observed in actual mobile networks (e.g., GSM900, GSM1800 and UMTS2100). Typically in dense-city (i.e., Top-3 cities) areas, the cell range is between 0.5 and 0.7 km depending on load. In urban/metropolitan radio environment we often find an average between 2.0 – 2.5 km cell range depending on deployed frequency, cell load and radio environment. In sub-urban and rural areas one should expect an average cell range between 2.0 – 3.5 km depending on frequency and radio environment. Typically cell load would be more important in city and urban areas (i.e., less frequency dependence) while the frequency will be most important in sub-urban and rural areas (i.e., low-frequency => higher cell range => fewer sites; higher frequency => lower cell range => higher number of sites).The cell range (i.e., 1.5 km) and effective surface area targeted for network deployment (i.e., 20,000 km2) provides an estimate for the number of coverage driven sites of ca. 3,300 BWA nodes. Whether more sites would be needed due to capacity limitations can be assessed once the market and user models have been defined.

Using typical infrastructure pricing and site-build cost the investment level for Western Europe (i.e., Capital expenses, Capex) should not exceed 350 million Euro for the network deployment all included. Assuming that the related network operational expense can be limited to 10%(excluding personnel cost) of the cumulated Capex, we have a yearly Network related opex of 35 million Euro (after rollout target has been reached). After the the final deployment target has been reached the Greenfield should assume a capital expense level of minimum 10% of their service revenue.

It should not take Greenfield BWA more than 4 years to reach their rollout target. This can further be accelerated if Greenfield BWA can share existing incumbant network infrastructure (i.e., site sharing) or use independent tower companies services. In the following assume that the BWA site rollout can be done within 3 years of launch.

Greenfield BWA the Market & Finance Part

Greenfield BWA will target primarily the house-hold market with broadband wireless access services based on the WiMAX (i.e., 802.16e standard). Voice over IP will be supported and offered with the subscription.

Furthermore, the Greenfield BWA intends to provide stationary as well as normadic services to the house-hold segment. In addition Greenfield BWA also will provide some mobility in the areas they provide coverage. However, this would not be their primary concern and thus national roaming would not be offered (reducing roaming charges/cost).

Greenfield BWA reaches a steady-state (i.e., after final site rollout) customer market-share of 20% of the Household base; ca. 1.1 million household subscriptions on which they have a blended revenue per household €20 per month can be expected. Thus, a yearly service revenue of ca. 265 million Euro. From year 4 and onwards a maintenance Capex level of 25 million Euro is kept (i.e., ca. 10% of revenue).

Greenfield BWA manage its cost strictly and achieve an EBITDA margin of 40% from year 4 onwards (i.e, total annual operational cost of 160 million Euro).

Depreciation & Amortisation (D&A) level is kept at a level of $40 million annually (steady-state). Furthermore, Greenfield Inc has an effective tax rate of 30%.

Now we can actually estimate the free cash flow (FCF) Greenfield Inc would generate from the 4th year forward:

(all in million Euro)
Revenue €265
-Opex €158
=EBITDA €106
– D&A €40 (ignoring spectrum amortization)
– Tax €20 (i.e., 30%)
+ D&A €40
=Gross Cash Flow €86
-Capex €25
=FCF €61

assuming zero percent FCF growth rate and operating with a 10% (i.e., this could be largely optimistic for a pure Greenfield operation. Having 15% – 25% is not unheard off to reflect the high risks) Weighted Average Cost of Capital (i.e., WACC) the perpetuity value from year 4 onwards would be €610 million. In Present Value this is €416 million, net €288 million for the initial 3 years discounted capital investment (for network deployment) and considering the first 3 years cumulated discounted EBITDA 12 million provides

a rather weak business case of ca. 140 million (upper) valuation prior to spectrum investment where-of bulk valuation arises from the continuation value (i.e., 4 year onwards).

Alternative valuation would be to take a multiple of the EBITDA (4th year) as a sales price valuation equivalent; typically one would expect between 6x and 10x the (steady-state) EBITDA and thus €636 mio (6x) to €1,000 mio (10x).

The above valuation assumptions are optimistic and it is worthwhile to note the following;

1. €20 per month per household customer should be seen as optimistic upper value; lower and more realistic might not be much more than €15 per month.
2. 20% market share is ambitious particular after 3 years operation.
3. 40% margin with 15% customer share and 3,300 radio nodes is optimistic but might be possible if Greenfield BWA can make use of Network Sharing and other cost synergies in relation to for example outsourcing.
4. 10% WACC is assumed. This is rather low given start-up scenario. Would not be surprised that this could be estimated to be as high as 15% to 20%.If point 1 to 4 lower boundaries would be applied to above valuation logic the business case would very quickly turn in red (i.e., negative); leading to the conclusion of a significant business risk given the scope of above business model.Our hypothetical Greenfield BWA should target paying minimum license fee for the TDD spectrum; upper boundary should not exceed €50 million to mitigate too optimistic business assumptions.The City-based Operation Model

Greenfield BWA could choose to focus their business model on the top-10 cities and their metropolitan areas. Lets assume that by this 50% of population or house-holds are captured as well as 15% of the surface area. This should be compared with the above assumptions 80% population and 60% surface area coverage.

The key business drivers would look as follows (in paranthesis the previous values have been shown for reference).

Sites 850 (3,300) rollout within 1 to 2 years (3 years).
Capex €100 mio (€350) for initial deployment; afterwhich €18 mio (€25).

Customer 0.74 mio (1.1)
Revenue €178 mio (€264)
EBITDA €72 mio (€106)
Opex €108 mio (€160)
FCF €38 mio (€61)
Value €210 mio (€140)

The city-based network strategy is about 50% more valuable than a more extensive coverage strategy would be.

Alternative valuation would be to take a multiple of the EBITDA (3rd year) as the sales price valuation equivalent; typically one would expect between 6x and 10x the (steady-state) EBITDA and thus €432 mio (6x) to €720 mio (10x).

Interestingly (but not surprising!) Greenfield BWA would be better of focusing on smaller network but in areas of high population density is financially more attractive. Greenfield BWA should avoid coverage based rollout strategy known from the mobile operator business model.

The question is how important is it for the Greenfield BWA to provide coverage everywhere? if their target is primarily households based customers with normadic and static mobility requirements then such a “coverage where the customer is” business model might actually work?

Source: http://harryshell.blogspot.de/2008/03/wireless-broadband-access-bwa.html

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Did you know? Did you consider? (from March 2008)

In 2007 the European average mobile revenue per user (ARPU per month) was €28+/-€6; a drop of ca. 4% compared to 2006 (the EU inflation level in 2007 was ca. 2.3%).

of the €28 ARPU, ca. 16% could be attributed to non-voice usage (i.e,. €4.5).

of the €4.5 Non-Voice ARPU, ca. 65% could be attributed to SMS usage (i.e, €3.0).

Thus, leaving €1.5 for non-voice (mobile) data service (i.e., 5.4% of total ARPU).

The increase that most European countries have seen in their mobile Non-Voice Revenue has by far not been able to compensate for the drop in ARPU across most countries over the last 5 to 6 years.

Adding advanced data (e.g., UMTS and HSPA) capabilities to the mobile networks around Europe has not resulted in getting more money out of the mobile customer (but absolute revenue has grown due to customer intake).

Although most European UMTS/HSPA operators report a huge uptake (in relative terms) of Bytes generated by the customers, this is not reflected in the ARPU development.

Maybe it really does not matter as long as the mobile operators overall financial performance remains excelent (i.e., Revenues, Customers, EBITDA, Cash, ….)?

Is it possible to keep healthy financial indicators with decreasing ARPU, huge data usage growth and investments into brand-new radio access technologies targeting the €1.5 per month per user?

Source: http://harryshell.blogspot.de/2008_03_01_archive.html

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Winner of the 700-MHz Auction is … Google! (from April 2008)

The United States has recently ended (March 2008) the auction of 5 blocks (see details below) of the analog TV spectrum band of 700-MHz. More specifically the band between 698 – 763 MHz (UL) and 728 – 793 MHZ (DL), with a total bandwidth of 2×28 MHz. In addition a single band 1×6 MHz in 722 – 728 MHz range was likewise auctioned. The analog TV band is expected to be completely vacated by Q1 2009.

The USA 700 MHz auction result was an impressive total of $19.12 billion, spend buying the following spectrum blocks: A (2×6 MHz), B (2×6 MHz), C (2×11 MHz) and E (1×6 MHz) blocks. The D (2×5 Mhz) block did not reach the minimum level. A total of 52 MHz (i.e, 2×23 + 1×6 MHz) bandwidth was auctioned off.

Looking with European eyes on the available spectrum allocated per block it is not very impressive (which is similar to other US Frequency Blocks per Operator, e.g., AWS & PCS). The 700 MHz frequency is clearly very economical for radio network coverage deployment in particular compared the high-frequency AWS spectrum used by T-Mobile, Verizon and Sprint. However, the 6 to 11 MHz (UL/DL) is not very impressive from a capacity sustainance perspective. It is quiet likely that this spectrum would be exhausted and rapidly leading to a significant additional financial commitment to cell splits / capacity extensions.

This $19.12 billion for 52 MHz translates to $1.22 per MHz spectrum per Population @ 700 MHz.

This should be compared to following historical auctions
* $0.56/MHz/Pop @ 1,700 MHz in 2006 US AWS auction
* $0.15/MHz/Pop (USA Auction 22 @ 1999) to $4.74/MHz/Pop (NYC, Verizon).
* $1.23/MHz/Pop Canadian 2000 PCS1900 Auction of 40MHz.
* $5.94/MHz/Pop UK UMTS auction (2001) in UK auctioning a total of 2×60 MHz FDD spectrum (TDD not considered).
* $7.84/MHz/Pop German UMTS auction in 2001 (2×60 MHz FDD, TDD not considered).

(Note: the excesses of the European UMTS auctions clearly illustrates a different time and place).

What is particular interesting is that Verizon “knocked-out” Google by paying $4.74 billion for the nationwide C-block of 2×11 MHz. “Beating” Google’s offer of $4.6 billion.

However, Google does not appear too sadened of the outcome and …. why should they! Google has to a great extend influenced the spectrum conditions allowing for open access (although it remains to be seen what this really means) to the C spectrum block; The USA Federal Communications Commission (FCC) has proposed to apply “open access” requirements for devices and applications on a the nation wide spectrum block C (2×11 MHz). 

Clearly Google should be regarded as the winner of the 700 MHz auction. They have avoided committing a huge amount of cash for the spectrum and on-top having to deploy even more cash to build and operate a wireless network (i.e., which is really their core business anyway).

Googling the Business Case
Google was willing to put down $4.6 billion for the 2×11 MHz @ 700 MHz. Let’s stop up an ask how their business case possible could have looked like.

At 700 MHz, with not too ambitious bandwidth per user requirements, Google might achieve a typical cell range between 2.5 and 4 km (Uplink limited, i.e., user equipment connection to base station). Although in “broadcast/downlink” mode, the cell range could be significantly larger (and downlink is all you really need for advertisement and broadcast;-).

Assume Google’s ambition was top-100 cities and 1-2% of the USA surface area they would need at least 30 thousand nodes. Financially (all included) this would likely result in $3 to $5 billion network capital expense (Capex) and a technology driven annual operational expense (Opex) of $300 to $500 million (in steady-state). On top of the spectrum price.

Using above rough technology indicators Google (if driven by sound financial principles) must have had a positive business case for a cash-out of minimum $8 billion over 10 years, incl. spectrum and discounted with WACC of 8% (all in all being very generous) and annual Technology Opex of minimum $300 million. On top of this comes customer acquisition, sales & marketing, building a wireless business operations (obviously they might choose to outsource all that jazz).

… and then dont forget the customer device that needs to be developed for the 700 MHz band (note GSM 750 MHz falls inside the C-band). Typically takes between 3 to 5 years to get a critical customer mass and then only if the market is stimulated.

It would appear to be a better business proporsition to let somebody else pay for spectrum, infrastructure, operation, etc… and just do what Google does best … selling advertisments and deliver search results … for mobile devices … maybe even agnostic to the frequency (seems better than wait until critical mass has been reached at the 700 MHz).

But then again … Google reported for full year 2007 a $16.4 billion in advertising revenues (up 56% compared to the previous year).(see refs Google Investor Relations). Imagine what this could be if extended to wireless / mobile market. Still lower than Verizon’s 2007 full year revnue of $23.8B (up 5.5% from 2006) but not that much lower considering the difference in growth rate.

The “successfull” proud owners (Verizon, AT&T Mobility, etc….) of the 700 MHz spectrum might want to keep in mind that Google’s business case for entering wireless must have been far beyond the their proposed $4.6 billion.

Appendix:
The former analog TV spectrum auction has been divided UHF spectrum into 5 blocks:
Block A: 2×6 MHz bandwidth (698–704 and 728–734 MHz); $3.96 billion
Block B: 2×6 MHz bandwidth (704–710 and 734–740 MHz); $9.14 billion dominated by AT&T Mobility.
Block C: 2×11 MHz bandwidth (746–757 and 776–787 MHz) Verizon $4.74 billion
Block D: 2×5 MHz bandwidth (758–763 and 788–793 MHz) No bids above the minimum.
Block E: 1×6 MHz bandwidth (722–728 MHz)Frontier Wireless LCC $1.26 billion

Source: http://harryshell.blogspot.de/2008/04/winner-of-700-mhz-auction-is-google.html

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Google Openness – Your Click makes Google Tick (From April 2008)

In an open letter to the chairman of the USA Federal Communications Commission (FCC) Kevin J. Martin, Google pleas for openness in the recently ended (i..e, March 2008) auction for the 700 MHz;

1. Open Applications – users can gain access to and use any applications, services or content.
2. Open Devices – any device on any network.
3. Open Wholesale Services – Service Providers and Virtual Network Operators should get wholesale access to the 700 MHz based network(s) on reasonably non-discriminatory commercial terms.
4. Open Network Access – service provides and virtual network operators should be allowed to interconnect with the 700 MHz wireless network(s).

The two first points of Open Applications and Open Devices are in principle independent of the 700 Mhz auction, although they can of course be made mandatory in the particular auction requirements and ….. so they were.

The Open Wholesale Service point makes the mind bugle (well at least mine) while figuring out funny wholesale models that would be non-discriminatory to both the wireless operator (having invested in spectrum and network) and Googles-and-alike (GAAs … whomever other than Google that might be?). The biggest question for a network operator providing wholesale to GAAs is likely going to be how to get a piece of the Google advertisement revenue pie.

Open Applications
Within devices capabilities and network possibilities this does not sound like mission impossible. Obviously, if a wireless network is interconnected to the web (i.e., 4th requirement) services and application available in general to a device (pc, laptop, etc.) connected to the fixed internet would also be available to the mobile device.

However, there are particular services and applications that wireless operators might want to traffic control and manage. Particular in the case of having only an 11 MHz bandwidth available (i.e., USA nationwide C-band @ 700MHz) on the air-interface, heavy peer-2-peerk applications and streaming might result in severe congestion and loss of service quality. Thus, the ability to control and manage the Quality of Service per application / content category will be necessary in order to avoid that few heavy users jeopardize the service quality for the majority of average wireless users.

The wireless operator however should have no problems in complying with Google Point 1.

Open Devices – Open Networks
This requirement might appear harmless and not worth worrying about. In principle a user with a subscription and who pays the access price can have access to any network his device is capable of communicating with (not exactly true in most mobile standards, such as GSM and UMTS/HSPA, of today). Basically WiFi hotspot access comes closest to such a business principle and if the customer does not care about the mobile operator services and content this would suffice.

The mobile business model is (even technically) not build on principles of free access between wireless / mobile networks. A mobile subscription (post-paid or pre-paid) is associated with customer acquisition cost, often subsidising the user terminal. The subscription requires the customer to keep paying for a period of time to pay-back the upfront customer investment done by the mobile operator.

Allowing a business model were customers can freely roam/move across networks might require a different financing mechanism (or none) of the consumers device and access rights. As a service provider with whole-sale agreements with several wireless network operators could enable this for their customer base. For a traditional mobile operator such a model would not be very attractive unless national roaming is invoked due to lack of coverage in a given area.

The Google proposal is from a business model very interesting (altans likely disruptive) although would also require some rethinking of current mobile AAA (i.e., Authentication, Authorization, and Accounting) architecture.

Furthermore, one might fear that by moving to the proposed Google model, that few internet-based businesses would end-up “owning” the customer-data (g-search, gmail, g-chat, g-blog, g-msisdn, g-device, etc..), while the customer-data ownership is currently spread out across several mobile and fixed telecommunication business. The legacy mobile / wireless operator becomes a bit carrier paid by those few internet-based businesses.

Open Wholesale Services
For the Google business this is a really fun one to think about. How would that work for an entity as Google?, for which close to 100% of revenues comes from advertising (i.e., 2007-earnings shows that 98.91% of their $16.594 billion from advertising).

The value for Google going wireless is clearly from opening up a new channel for advertising. The growth potential entering the mobile channel is potential enormeous, with mobile penetration approaching 100% and even far beyond in many European markets (closer to 120%+).

Normal telecommunication wholesale models are based on volumetric usage (i.e., Minutes or Bytes). However Google would hardly trigger any direct volumetric usage with exception of the volume it takes to download google.com. Alas there might be a considerable traffic stream arising from YouTupe and some from gmail usage. Even following an advertising link will generate traffic although not necessarily generating much additional volumetric usage. Of course the question is how to distinguish between Google generated traffic and non-Google traffic?

Furthermore, the price per advertisement click that Google earns could be significantly different (i.e., higher) from the cost of the click according with a standard volumetric wholesale model. Furthermore, a different click might have different values but still generate the same volume and associated cost.

Maybe the wireless operator should not care too much how Google earns its money as long as the traffic generated by providing access to happy Googlers and GAAs are recovered by a healthy margin and does not jeopardize the quality of other customers.

Open Network Access
Yeah this sort of make sense …. without this the first three points become rather academic. There is no essential technical barriers for interconnect.

Source: http://harryshell.blogspot.de/2008/04/google-openess-your-click-make-google.html

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Backhaul Pains (from April 2008)

Backhaul, which is the connection between a radio node and the core network, is providing mobile-wireless operators possible with the biggest headache ever (apart from keeping a healthy revenue growth in mature markets 😉 … it can be difficult to come by in the right quantities and can be rather costly with conventional transmission cost-structures … Backhaul is expected to have delayed the Sprint WiMAX rollout of their Xohm branded wireless internet service. A Sprint representative is supposed to have said: “You need a lot of backhaul capacity to do what’s required for WiMax.” (see forexample WiMax.com blog)

What’s a lot?

Well … looking at the expected WiMAX speed per Base Station (BS) of up-to 50 Mbps (i.e., 12 – 24x typical backhaul supporting voice demand), it is clear that finding suitable and low-cost bachaul solutions might be challenging. Conventional leased lines would be grossly un-economical at least if priced conventionally; xDSL and Fiber-to-the-Premises (FTTP) infrastructure that could support (economically?) such bandwidth demand is not widely deployed yet.

Is this a Sprint issue only? Nope! …. Sprint cannot be the only mobile-wireless operator with this problem – for UMTS/HSPA mobile operators the story should be pretty much the same (unless an operator has a good and modern microwave backhaul network supporting the BS speed).

Backhaul Pains – Scalability Issues
The backhaul connection can be either via a Leased Line (LL) or a Microwave (MW) radio link. Sometimes a MW link can be leased as well and might even be called a leased line.

With microwave (MW) links one can easily deliver multiples of 2.048 Mbps (i.e., 10 – 100 Mbps) on the same connection for relative low capital cost (€500 – €1,000 per 2.048 Mbps) and low operational expense. However planning and deployment experience and spectrum is required.

In many markets network operators have been using conventional (fixed) leased lines, leased from incumbent fixed-line providers. The pricing model is typically based on an upfront installation fee (might be capitalized) and a re-occurring monthly lease. On a yearly basis this operational expense can be in the order of €5,000 per 2.048 Mbps, i.e., 5x to 10 x the amount of a MW connection. Some price-models trade-off the 1-off installation fee with a lower lease cost.

Voice was the Good for Backhaul; Before looking at the broadband wireless data bandwidth demand its worth noticing that in the good old Voice days (i.e., GSM, IS95, ..) 1x to 2x 2.048 Mbps was more than sufficient to support most demands on a radio base station (BS).

Mobile-Wireless Broadband data enablers are the Bad and quickly becoming the Very Ugly for Backhaul; With the deployment of High Speed Packet Access (HSPA) on-top of UMTS and with WiMAX (a la Sprint) a BS can easily provide between 7.2 to 14.4 Mbps or higher per sector depending on available bandwidth. With 3 sectors per BS the total supplied data capacity could (in theory … ) be in excess of 21 Mbps per radio Base Station.

From the perspective of backhaul connectivity one would need at least an equivalent bandwidth of 10x 2.048 Mbps connections. Assuming such backhaul lease bandwidth is available in the first instance, with conventional leased line pricing structure, such capacity would be very expensive, i.e., €50,000 per backhaul connection per year. Thus, for 1,000 radio nodes an operator would pay on an annual basis 50 million Euro (Opex directly hitting the EBITDA). This operational expense could be 8 times more than a voice-based operational leased-line expense.

Now that’s alot!

Looking a little ahead (i.e., next couple of years) our UMTS and WiMAX based mobile networks will undergo the so-called Long-Term Evolution (LTE; FDD and TDD based) with expected radio node downlink (i.e., base station to user equipment) capacity between 173 Mbps and 326 Mbps depending on antenna system and available bandwidth (i.e., minimum 20 Mhz spectrum per sector). Thus over a 3-sectored BS (theoretical) speeds in excess of 520 Mbps might be dreamed of (i.e., 253x 2.048 Mbps – and this is HUGE!:-). Alas across a practical real-life deployed base station (on average) no more than 1/3 of the theoretical speed should be expected.

“Houston we have a problem” … should be ringing in any CFO / CTO’s ears – a. Financially near-future developments could significantly strain the Technology Opex budgets and b.Technically providing cost-efficient backhaul capacity that can sustain the promised land.

A lot of that above possible cost can and should be avoided; looking at possible remedies we have several options;

1. High capacity microwave backhaul can prevent the severe increase in leased line cost; provided spectrum and expertise is available. Financially microwave deployment has the advantage of being mainly capital-investment driven with resulting little additional operational expense per connection. It is expected that microwave solutions will be available in the next couple of years which can provide connection capacity of 100 Mbps and above.

Microwave backhaul solutions are clearly economical. However, it is doubtful that LTE speed requirements can be met even with most efficient microwave backhaul solutions?

2. Move to different leased line (LL) pricing mechanisms such as flat pricing (eat all you can for x-Euro). Changing the LL pricing structure is not sufficient. At the same time providers of leased-line infrastructure will be “forced” (i.e., by economics and bandwidth demand) to move to new types of leased bandwidth solutions and architectures in order to sustain the radio network capabilities; ADSL is expected to develop from 8(DL)/1(UL) Mbps to 25(DL)/3.5(UL) Mbps with ADSL2+; VDSL (UL/DL symmetric) from ca. 100 Mbps to 250 Mbps with VDSL2 (ITU-T G.993.2 standard).

Clearly a VDSL2-based infrastructure could support today’s HSPA/WiMAX requirements, as well as the initial bandwidth requirements of LTE. Although VDSL2-based networks are being deployed around Europe (and the world) it is not not widely available.

Another promising mean of supporting the radio-access bandwidth requirements is Fiber to the Premises (FTTP), such as for example offered by Verizon in certain areas of USA (Verizon FiOS Service). With Gigabit Passive Optical Network (GPON, ITU-T G.984 standard) maximum speeds of 2,400 Mbps (DL) and 1,200 Mbps (UL) can be expected. If available FTTP to the base station would be ideal – provided that the connection is priced no higher than a standard 2.048 Mbps leased line to day (i.e., €5,000 benchmark). Note that for a mobile operator it could be acceptable to pay a large 1-off installation fee which could partly finance the FTTP connection to the base station.

Cost & Pricing Expectations
It is in general accepted by industry analysts that broadband wireless services are not going to add much to mobile operators total service revenue growth. In optimistic revenue scenarios data revenue compensates for stagnating/falling voice revenues. EBITDA margins will (actually are!) under pressure and the operational expenses will be violently scrutinized.

Thus, mobile operators deploying UMTS/HSPA, WiMAX and eventually (in the short-term) LTE cannot afford to have its absolute Opex increase. Therefore, if a mobile-wireless operator has a certain backhaul Opex, it would try to keep it at the existing level or reduce it over time (to mitigate possible revenue decline).

For the backhaul leased-capacity providers this is sort of bad news (or good? as it forces them to become economically more efficient) …. as they would have to finance their new fixed higher-bandwidth infrastructures (i.e., VDSL or FTTP) with little additional revenue from the mobile-wireless operators.

Economically it is not clear whether mobile-wireless cost-structure expectations will meet the leased-capacity providers total-cost of deploying networks supporting the mobile-wireless bandwidth demand.

However, for the provider of leased fixed-bandwith, providing VDSL2 and/or FTTP to the residential market should finance their deployment model.

With more than 90% of all data traffic being consumed in-house/in-door and with VDSL2/Fiber-to-the-Home (FTTH) solutions being readily available to the Homes (in urban environments at least) of business as well as residential customers, will mobile-wireless LTE base stations be loaded to the extend that very-high capacity (i.e., beyond 50 Mbps) backhaul connections would be needed?

Source: http://harryshell.blogspot.de/2008/04/backhaul-pains.html

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