On Cellular Data Pricing, Revenue & Consumptive Growth Dynamics, and Elephants in the Data Pipe.

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I am getting a bit sentimental as I haven’t written much about cellular data consumption for the last 10+ years. At the time, it did not take long for most folks in and out of our industry to realize that data traffic and, thereby, so many believed, the total cost of providing the cellular data would be growing far beyond the associated data revenues, e.g., remember the famous scissor chart back in the early two thousand tens. Many believed (then) that cellular data growth would be the undoing of the cellular industry. In 2011 many believed that the Industry only had a few more years before the total cost of providing cellular data would exceed the revenue rendering cellular data unprofitable. Ten years after, our industry remains alive and kicking (though they might not want to admit it too loudly).

Much of the past fear was due to not completely understanding the technology drivers, e.g., bits per second is a driver, and bytes that price plans were structured around not so much. The initial huge growth rates of data consumption that were observed did not make the unease smaller, i.e., often forgetting that a bit more can be represented as a huge growth rate when you start with almost nothing. Moreover, we also did have big scaling challenges with 3G data delivery. It became quickly clear that 3G was not what it had been hyped to be by the industry.

And … despite the historical evidence to the contrary, there are still to this day many industry insiders that believe that a Byte lost or gained is directly related to a loss or gain in revenue in a linear fashion. Our brains prefer straight lines and linear thinking, happily ignoring the unpleasantries of the non-linear world around us, often created by ourselves.

Figure 1 illustrates linear or straight-line thinking (left side), preferred by our human brains, contrasting the often non-linear reality (right side). It should be emphasized that horizontal and vertical lines, although linear, are not typically something that instinctively enters the cognitive process of assessing real-world trends.

Of course, if the non-linear price plans for cellular data were as depicted above in Figure 1, such insiders would be right even if anchored in linear thinking (i.e., even in the non-linear example to the right, an increase in consumption (GBs) leads to an increase in revenue). However, when it comes to cellular data price plans, the price vs. consumption is much more “beastly,” as shown below (in Figure 2);

Figure 2 illustrates the two most common price plan structures in Telcoland; (a, left side) the typical step function price logic that associates a range of data consumption with a price point, i.e., the price is a constant independent of the consumption over the data range. The price level is presented as price versus the maximum allowed consumption. This is by far the most common price plan logic in use. (b, right side) The “unlimited” price plan logic has one price level and allows for unlimited data consumption. T-Mobile US, Swisscom, and SK Telecom have all endorsed the unlimited with good examples of such pricing logic. The interesting fact is that most of those operators have several levels of unlimited tied to the consumptive behavior where above a given limit, the customer may be throttled (i.e., the speed will be reduced compared to before reaching the limit), or (and!) the unlimited plan is tied to either radio access technology (e.g., 4G, 4G+5G, 5G) or a given speed (e.g., 50 Mbps, 100 Mbps, 1Gbps, ..).

Most cellular data price plans follow a step function-like pricing logic as shown in Figure 2 (left side), where within each level, the price is constant up to the nominal data consumption value (i.e., purple dot) of the given plan, irrespective of the consumption. The most extreme version of this logic is the unlimited price plan, where the price level is independent of the volumetric data consumption. Although, “funny” enough, many operators have designed unlimited price plans that, in one way or another, depend on the customers’ consumption, e.g., after a certain level of unlimited consumption (e.g., 200 GB), cellular speed is throttled substantially (at least if the cell under which the customer demand resources are congested). So the “logic” is that if you wanted true unlimited, you still need to pay more than if you only require “unlimited”. Note that for the mathematically inclined, the step function is regarded as (piece-wise) linear … Although our linear brains might not appreciate that finesse very much. Maybe a heuristic that “The brain thinks in straight lines” would be more precisely restated as “The brain thinks in continuous non-constant monotonous straight lines”.

Any increase in consumption within a given pricing-consumption level will not result in any additional revenue. Most price plans allow for considerable growth without incurring additional associated revenues.

NETHERLANDS vs INDONESIA – BRIEFLY.

I like to keep informed and updated about markets I have worked in, with operators I have worked for, and with. I have worked across the globe in many very diverse markets and with operators in vastly different business cycles gives an interesting perspective on our industry. Throughout my career, I have been super interested in the difference between Telco operations and strategies in so-called mature markets versus what today may be much more of a misnomer than 10+ years ago, emerging markets.

The average cellular, without WiFi, consumption per customer in Indonesia was ca. 8 GB per month in 2022. That consumption would cost around 50 thousand Rp (ca. 3 euros) per month. For comparison, in The Netherlands, that consumption profile would cost a consumer around 16 euros per month. As of May 2023, the median cellular download speed was 106 Mbps (i.e., helped by countrywide 5G deployment, for 4G only, the speed would be around 60 to 80 Mbps) compared with 22 Mbps in Indonesia (i.e., where 5G has just been launched. Interestingly, although most likely coincidental, in Indonesia, a cellular data customer would pay ca. 5 times less than in the Netherlands for the same volumetric consumption. Note that for 2023, the average annual income in Indonesia is about one-quarter of that in the Netherlands. However, the Indonesian cellular consumer would also have one-fifth of the quality measured by downlink speed from the cellular base station to the consumer’s smartphone.

Let’s go deeper into how effective consumptive growth of cellular data is monetized… what may impact the consumptive growth, positively and negatively, and how it relates to the telco’s topline.

CELLULAR BUSINESS DYNAMICS.

Figure 3 Between 2016 and 2021, Western European Telcos lost almost 7% of their total cellular turnover (ca. 7+ billion euros over the markets I follow). This corresponds to a total revenue loss of ca. 1.4% per year over the period. To no surprise, the loss of cellular voice-based revenue has been truly horrendous, with an annual loss ca. 30%, although the Covid year (2021 and 2022, for that matter) was good to voice revenues (as we found ourselves confined to our homes and a call away from our colleagues). On the positive side, cellular data-based revenues have “positively” contributed to the revenue in Western Europe over the period (we don’t really know the counterfactual), with an annual growth of ca. 4%. Since 2016 cellular data revenues have exceeded that of cellular voice revenues and are 2022 expected to be around 70% of the total cellular revenue (for Western Europe). Cellular revenues have been and remain under pressure, even with a positive contribution from cellular data. The growth of cellular data volume (not including the contribution generated from WiFi usage) has continued to grow with a 38% annualized growth rate and is today (i.e., 2023) more than five times that of 2016. The annual growth rate of cellular data consumption per customer is somewhat lower ranging from the mid-twenties to the end-thirties percent. Needless to say that the corresponding cellular ARPU has not experienced anywhere near similar growth. In fact, cellular ARPU has generally been lowered over the period.

Some, in my opinion, obvious observations that are worth making on cellular data (I come to realize that although I find these obvious, I am often confronted with a lack of awareness or understanding of those);

Cellular data consumption grows much (much) faster than the corresponding data revenue (i.e., 38% vs 4% for Western Europe).

The unit growth of cellular data consumption does not lead to the same unit growth in the corresponding cellular data revenues.

Within most finite cellular data plans (thus the not unlimited ones), substantial data growth potential can be realized without resulting in a net increase of data-related revenues. This is, of course, trivial for unlimited plans.

The anticipated death of the cellular industry back in the twenty-tens was an exaggeration. The Industry’s death by signaling, voluptuous & unconstrained volumes of demanded data, and ever-decreasing euros per Bytes remains a fading memory and, of course, in PowerPoints of that time (I have provided some of my own from that period below). A good scare does wonders to stimulate innovation to avoid “Armageddon.” The telecom industry remains alive and well.

Figure 4 The latest data (up to 2022) from OECD on mobile data consumption dynamics. Source data can be found at OECD Data Explorer. The data illustrates the slowdown in cellular data growth from a customer perspective and in terms of total generated mobile data. Looking over the period, the 5-year cumulative growth rate between 2016 and 2021 is higher than 2017 to 2022 as well as the growth rate between 2022 and 2021 was, in general, even lower. This indicates a general slowdown in mobile data consumption as 4G consumption (in Western Europe) saturates and 5G consumption still picks up. Although this is not an account of the observed growth dynamics over the years, given the data for 2022 was just released, I felt it was worth including these for completeness. Unfortunately, I have not yet acquired the cellular revenue structure (e.g., voice and data) for 2022, it is work in progress.

WHAT DRIVES CONSUMPTIVE DATA GROWTH … POSITIVE & NEGATIVE.

What drives the consumer’s cellular data consumption? As I have done with my team for many years, a cellular operator with data analytics capabilities can easily check the list of positive and negative contributors driving cellular data consumption below.

Positive Growth Contributors:

  • Customer or adopter uptake. That is, new or old, customers that go from non-data to data customers (i.e., adopting cellular data).
  • Increased data consumption (i.e., usage per adopter) within the cellular data customer base that is driven by a lot of the enablers below;
  • Affordable pricing and suitable price plans.
  • More capable Radio Access Technology (RAT), e.g., HSDPA → HSPA+ → LTE → 5G, effectively higher spectral efficiency from advanced antenna systems. Typically will drive up the per-customer data consumption to the extent that pricing is not a barrier to usage.
  • More available cellular frequency spectrum is provisioned on the best RAT (regarding spectral efficiency).
  • Good enough cellular network consistent with customer demand.
  • Affordable and capable device ecosystem.
  • Faster mobile device CPU leads to higher consumption.
  • Faster & more capable mobile GPUs lead to higher consumption.
  • Device screen size. The larger the screen, the higher the consumption.
  • Access to popular content and social media.

Figure 5 illustrates the description of data growth as depending on the uptake of Adopters and the associated growth rate α(t) multiplied by the Usage per Adopter and the associated growth rate of usage μ(t). The growth of the Adopters can typically be approximated by an S-curve reaching its maximum as there are few more customers left to adopt a new service or product or RAT (i.e., α(t)→0%). As described in this section, the growth of usage per adopter, μ(t), will depend on many factors. Our intuition of μ is that it is positive for cellular data and historically has exceeded 30%. A negative μ would be an indication of consumptive churn. It should not be surprising that overall cellular data consumption growth can be very large as the Adopter growth rate is at its peak (i.e., around the S-curve inflection point), and Usage growth is high as well. It also should not be too surprising that after Adopter uptake has reached the inflection point, the overall growth will slow down and eventually be driven by the Usage per Adopter growth rate.

Figure 6 Using the OECD data (OECD Data Explorer) for the Western European mobile data per customer consumptive growth from 2011 to 2022, the above illustrates the annual growth rate of per-customer data mobile consumption. Mobile data consumption is a blend of usage across the various RATs enabling packet data usage. There is a clear increased annual growth after introducing LTE (4G) followed by a slowdown in annual growth, possibly due to reaching saturation in 4G adaptation, i.e., α3G→4G(t) → 0% leaving μ4G(t) driving the cellular data growth. There is a relatively weak increase in 2021, and although the timing coincides with 5G non-standalone (NSA) introduction (typically at 700 MHz or dynamics spectrum share (DSS) with 4G, e.g., Vodafone-Ziggo NL using their 1800 MHz for 4G and 5G) the increase in 2020 may be better attributed to Covid lockdown than a spurt in data consumption due to 5G NSA intro.

Anything that creates more capacity and quality (e.g., increased spectral efficiency, more spectrum, new, more capable RAT, better antennas, …) will, in general, result in an increased usage overall as well as on a per-customer basis (remember most price plans allow for substantial growth within the plans data-volume limit without incurring more cost for the customer). If one takes the above counterfactual, it should not be surprising that this would result in slower or negative consumption growth.

Negative growth contributors:

  • Cellular congestion causes increased packet loss, retransmissions, and deteriorating latency and speed performance. All in all, congestion may have a substantial negative impact on the customer’s service experience.
  • Throttling policies will always lower consumption and usage in general, as quality is intentionally lowered by the Telco.
  • Increased share of QUIC content on the network. The QUIC protocol is used by many streaming video providers (e.g., Youtube, Facebook, TikTok, …). The protocol improves performance (e.g., speed, latency, packet delivery, network changes, …) and security. Services using QUIC will “bully” other applications that use TCP/IP, encouraging TCP/IP to back off from using bandwidth. In this respect, QUIC is not a fair protocol.
  • Elephant flow dynamics (e.g., few traffic flows causing cell congestion and service degradation for the many). In general, elephant flows, particularly QUIC based, will cause an increase in TCP/IP data packet retransmissions and timing penalties. It is very much a situation where a few traffic flows cause significant service degradation for many customers.

One of the manifestations of cell congestion is packet loss and packet retransmission. Packet loss due to congestion ranges from 1% to 5%. or even several times higher at moments of peak traffic or if the user is in a poor cellular coverage area. The higher the packet loss, the worse the congestion, and the worse the customer experience. The underlying IP protocols will attempt to recover a lost packet by retransmission. The retransmission rate can easily exceed 10% to 15% in case of congestion. Generally, for a reliable and well-operated network, the packet loss should be well below 1% and even as low as 0.1%. Likewise, one would expect a packet retransmission rate of less than 2% (I believe the target should be less than 1%).

Thus, customers that happen to be under a given congested cell (e.g., caused by an elephant flow) would incur a substantially higher rate of retransmitted data packages (i.e., 10% to 15% or higher) as the TCP/IP protocol tries to make up for lost data packages. The customer may experience substantial service quality degradation and, as a final (unintended) “insult”, often be charged for those additional retransmitted data volumes.

From a cellular perspective, as the congestion has been relieved, the cellular operator may observe that the volume on the congested cell actually drops. The reason is that the packet loss and retransmission drops to a level far below the congested one (e.g., typically below 1%). As the quality improves for all customers demanding service from the previously overloaded (i.e., congested) cell, sustainable volume growth will commence in total and as well as will the average consumption on a customer basis. As will be shown below for normal cellular data consumption and most (if not all) price plans, a few percentage points drop in data volume will not have any meaningful effect on revenues. Either because the (temporary) drop happens within the boundaries of a given price plan level and thus has no effect on revenue, or because the overall gainful consumptive growth, as opposed to data volume attributed to poor quality, far exceeds the volume loss due to improved capacity and quality of a congested cell.

Well-balanced and available cellular sites will experience positive and sustainable data traffic growth.

Congested and over-loaded cellular sites will experience a negative and persistent reduction of data traffic.

Actively managing the few elephant flows and their negative impact on the many will increase customer satisfaction, reduce consumptive churn, and increase data growth, easily compensating for the congestion-induced increases due to packet retransmission. And unless an operator consistently is starved for radio access investments, or has poor radio access capacity management processes, most cell congestion can be attributed to the so-called elephant flows.

CELLULAR DATA CONSUMPTION IN REAL NETWORKS – ON A SECTOR LEVEL.

And irrespective of whatever drives positive and negative growth, it is worth remembering that daily traffic variations on a sector-by-sector basis and an overall cellular network level are entirely natural. An illustration of such natural sector variation over a (non-holiday) week is shown below in Figure 7 (c) for a sector in the top-20% of busiest sectors. In this example, the median variation over all sectors in the same week, as shown below, was around 10%. I often observe that even telco people (that should know better) find this natural variation quite worrisome as it appears counterintuitive to their linear growth expectations. Proper statistical measurement & analysis methodologies must be in place if inferences and solid analysis are required on a sector (or cell) basis over a relatively short time period (e.g., day, days, week, weeks,…).

Figure 7 illustrates the cellular data consumption daily variation over a (non-holiday) week. In the above, there are three examples (a) a sector from the bottom 20% in terms of carried volume, (b) a sector with a median data volume, and (c) a sector taken from the top 20% of carried data volume. Over the three different sectors (low, median, high) we observe very different variations over weekdays. From the top-20%, we have an almost 30% variation between the weekly minimum (Tuesday) and the weekly maximum (Thursday) to the bottom-20% with a variation in excess of 200% over the week. The charts above show another trend we observe in cellular networks regarding consumptive variations over time. Busy sectors tend to have a lower weekly variation than less busy sectors. I should point out that I have made no effort to select particular sectors. I could easily find some (of the less busy sectors) with even more wild variations than shown above.

The day-to-day variation is naturally occurring based on the dynamic behavior of the customers served by a given sector or cell (in a sector). I am frequently confronted with technology colleagues (whom I respect for their deep technical knowledge) that appear to expect (data) traffic on all levels monotonously increase with a daily growth rate that amounts to the annual CAGR observed by comparing the end-of-period volume level with the beginning of period volume level. Most have not bothered to look at actual network data and do not understand (or, to put it more nicely, simply ignore) the naturally statistical behavior of traffic that drives hourly, daily, weekly, and monthly variations. If you let statistical variations that you have no control over drive your planning & optimization decisions. In that case, you will likely fail to decide on the business-critical ones you can control.

An example of a high-traffic (top-20%) sector’s complete 365 day variations of data consumption is shown below in Figure 8. We observe that the average consumption (or traffic demand) increases nicely over the year with a bit of a slowdown (in this European example) during the summer vacation season (same around official holidays in general). Seasonal variations is naturally occurring and often will result in a lower-than-usual daily growth rate and a change in daily variations. In the sector traffic example below, Tuesdays and Saturdays are (typically) lower than the average, and Thursdays are higher than average. The annual growth is positive despite the consumptive lows over the year, which would typically freak out my previously mentioned industry colleagues. Of course, every site, sector, and cell will have a different yearly growth rate, most likely close to a normal distribution around the gross annual growth rate.

Figure 8 illustrates a top-20% sector’s data traffic growth dynamics (in GB) over a calendar year’s 365 days. Tuesdays and Saturdays are likely below the weekly average data consumption, and Thursdays are more likely to be above. Furthermore, the daily traffic growth is slowing around national holidays and in the summer vacation (i.e., July & August for this particular Western European country).

And to nail down the message. As shown in the example in Figure 9 below, every sector in your cellular network from one time period to the other will have a different positive and negative growth rate. The net effect over time (in terms of months more than days or weeks) is positive as long as customers adopt the supplied RAT (i.e., if customers are migrating from 4G to 5G, it may very well be that 4G consumed data will decline while the 5G consumed data will increase) and of course, as long as the provided quality is consistent with the expected and demanded quality, i.e., sectors with congestion, particular so-called elephant-flow induced congestion, will hurt the quality of the many that may reduce their consumptive behavior and eventually churn.

Figure 9 illustrates the variation in growth rates across 15+ thousand sectors in a cellular network comparing the demanded data volume between two consecutive Mondays per sector. Statistical analysis of the above data shows that the overall average value is ca. 0.49% and slightly skewed towards the positive growths rates (e.g., if you would compare a Monday with a Tuesday, the histogram would typically be skewed towards the negative side of the growth rates as Tuesday are a lower traffic day compared to Monday). Also, with the danger of pointing out the obvious, the daily or weekly growth rates expected from an annual growth rate of, for example, 30% are relatively minute, with ca. 0.07% and 0.49%, respectively.

The examples above (Figures 7, 8, and 9) are from a year in the past when Verstappen had yet to win his first F1 championship. That particular weekend also did not show F1 (or Sunday would have looked very different … i.e., much higher) or any other big sports event.

CELLULAR DATA PRICE PLAN LOGIC.

Figure 10 above is an example of the structure of a price plan. Possibly represented slightly differently from how your marketeer would do (and I am at peace with that). We observe the illustration of a price level of 8 data volume intervals on the upper left chart. This we can also write as (following the terminology of the lower right corner);

Thus, for the package allowing the customer to consume up to 3 GB is priced at 20 (irrespective of whether the customer would consume less). For package a consumer would pay 100 for a data consumption allowance up to 35 GB. Of course, we assume that the consumer choosing this package would generally consume more than 24 GB, which is the next cheaper package (i.e., ).

The price plan example above clearly shows that each price level offers customers room to grow before upgrading to the next level. For example, a customer consuming no more than 8 GB per month, fitting into , could increase consumption with 4 GB (+50%) before considering the next level price plan (i.e., ). This is just to illustrate that even if the customer’s consumption may grow substantially, one should not per se be expecting more revenue.

Even though it should be reasonably straightforward that substantial growth of a customer base data consumption cannot be expected to lead to an equivalent growth in revenue, many telco insiders instinctively believe this should be the case. I believe that the error may be due to many mentally linearizing the step-function price plans (see Figure 2 upper right side) and simply (but erroneously) believing that any increase (decrease) in consumption directly results in an increase (or decrease) in revenue.

DATA PRICING LOGIC & USAGE DISTRIBUTION.

If we want to understand how consumptive behavior impacts cellular operators’ toplines, we need to know how the actual consumption distributes across the pricing logic. As a high-level illustration, Figure 11 (below) shows the data price step-function logic from Figure 9 with an overall consumptive distribution superimposed (orange solid line). It should be appreciated that while this provides a fairly clear way of associating consumption with pricing, it is an oversimplification at best. It will nevertheless allow me to estimate crudely the number of customers that are likely to have chosen a particular price plan matching their demand (and affordability). In reality, we will have customers that have chosen a given price plan but either consume less than the limit of the next cheaper plan (thus, if consistently so, could save but go to that plan). We will also have customers that consume more than their allowed limit. Usually, this would result in the operator throttling the speed and sending a message to the customer that the consumption exceeds the limit of the chosen price plan. If a customer would consistently overshoots the limits (with a given margin) of the chosen plan, it is likely that eventually, the customer will upgrade to the next more expensive plan with a higher data allowance.

Figure 11 above illustrates on the left side a consumptive distribution (orange line) identified by its mean and standard deviation superimposed on our price plan step-function logic example. The right summarizes the consumptive distribution across the eight price plan levels. Note that there is a 9th level in case the 200 GB limit is breached (0.2% in this example). I am assuming that such customers pay twice the price for the 200 GB price plan (i.e., 320).

In the example of an operator with 100 million cellular customers, the consumptive distribution and the given price plan lead to a fiat of 7+ billion per month. However, with a consumptive growth rate of 30% to 40% annually per active cellular data user (on average), what kind of growth should we expect from the associated cellular data revenues?

Figure 12 In the above illustration, I have mapped the consumptive distribution to the price plan levels and then developed the begin-of-period consumptive distribution (i.e., the light green curve) month by month until month 12 has been reached (i.e., the yellow curve). I assume the average monthly consumptive cellular data growth is 2.5% or ca. 35% after 12 months. Furthermore, I assume that for the few customers falling outside the 200 GB limit that they will purchase another 200 GB plan. For completeness, the previous 12 months (previous year) need to be carried out to compare the total cumulated cellular data revenue between the current and previous periods.

Within the current period (shown in Figure 12 above), the monthly cellular data revenue CAGR comes out at 0.6% or a total growth of 7.4% of monthly revenue between the beginning period and the end period. Over the same period, the average data consumption (per user) grew by ca. 34.5%. In terms of the current year’s total data revenue to the previous year’s total data revenue, we get an annual growth rate of 8.3%. This illustrates that it should not be surprising that the revenue growth can be far smaller than the consumptive growth given price plans such as the above.

It should be pointed out that the above illustration of consumptive and revenue growth simplifies the growth dynamics. For example, the simulation ignores seasonal swings over a 12-month period. Also, it attributes 1-to-1 all consumption falling within the price range to that particular price level when there is always spillover on both upper and lower levels of a price range that will not incur higher or lower revenues. Moreover, while mapping the consumptive distribution to the price-plan giga-byte intervals makes the simulation faster (and setup certainly easier), it is also not a very accurate approach to the coarseness of the intervals.

A LEVEL DEEPER.

While working with just one consumptive distribution, as in Figure 11 and Figure 12 above, allows for simpler considerations, it does not fully reflect the reality that every price plan level will have its own consumptive distribution. So let us go that level deeper and see whether it makes a difference.

Figure 13 above, illustrates the consumptive distribution within a given price plan range, e.g., the “5 GB @ 30” price-plan level for customers with a consumption higher than 3 GB and less than or equal to 5 GB. It should come as no surprise that some customers may not reach even the 3 GB, even though they pay for (up to) 5 GB, and some may occasionally exceed the 5 GB limit. In the example above, 10% of customers have a consumption below 3 GB (and could have chosen the next cheaper plan of up to 3 GB), and 3% exceed the limits of the chosen plan (an event that may result in the usage speed being throttled). As the average usage within a given price plan level approaches the ceiling (e.g., 5 GB in the above illustration), in general, the standard deviation will reduce accordingly as customers will jump to the Next Expensive Plan to meet their consumptive needs (e.g., “12 GB @ 50” level in the illustration above).

Figure 14 generalizes Figure 11 to the full price plan and, as illustrated in Figure 12, let the consumption profiles develop in time over a 12-month period (Initial and +12 month shown in the above illustration). The difference between the initial and 12 months can be best appreciated with the four smaller figures that break up the price plan levels in 0 to 40 GB and 40 to 200 GB.

The result in terms of cellular data revenue growth is comparable to that of the higher-level approach of Figure 12 (ca. 8% annual revenue growth vs 34 % overall consumptive annual growth rate). The detailed approach of Figure 11 is, however, more complicated to get working and requires much more real data to work with (which obviously should be available to operators in this time and age). One should note that in the illustrated example price plan (used in the figures above) that at a 2.5% monthly consumptive growth rate (i.e., 34% annually), it would take a customer an average of 24 months (spread of 14 to 35 month depending on level) to traverse a price plan level from the beginning of the level (e.g., 5 GB) to the end of the level (12 GB). It should also be clear that as a customer enters the highest price plan levels (e.g., 100 GB and 200 GB), little additional can be expected to be earned on those customers over their consumptive lifetime.

The illustrated detailed approach shown above is, in particular, useful to test a given price plan’s profitability and growth potential, given the particularities of the customers’ consumptive growth dynamics.

The additional finesse that could be considered in the analysis could be an affordability approach because the growth within a given price level slows down as the average consumption approaches the limit of the given price level. This could be considered by slowing the mean growth rate and allowing for the variance to narrow as the density function approaches the limit. In my simpler approach, the consumptive distributions will continue to grow at a constant growth rate. In particular, one should consider more sophisticated approaches to modeling the variance that determines the spillover into less and more expensive levels. An operator should note that consumption that reduces or consistently falls into the less expensive level expresses consumptive churn. This should be monitored on a customer level as well as on a radio access cell level. Consumptive churn often reflects the supplied radio access quality is out of sync with the customer demand dynamics and expectations. On a radio access cell level, the diligent operator will observe a sharp increase in retransmitted data packages and increased latency on a flow (and active customer basis) hallmarks of a congested cell.

WRAPPING UP.

To this day, 20+ odd years after the first packet data cellular price plans were introduced, I still have meetings with industry colleagues where they state that they cannot implement quality-enhancing technologies for the fear that data consumption may reduce and by that their revenues. Funny enough, often the fear is that by improving the quality for typically many of their customers being penalized by a few customers’ usage patterns (e.g., the elephants in the data pipe), the data packet loss and TCP/IP retransmissions are reducing as the quality is improving and more customers are getting the service they have paid for. It is ignoring the commonly established fact of our industry that improving the customer experience leads to sustainable growth in consumption that consequently may also have a positive topline impact.

I am often in situations where I am surprised with how little understanding and feeling Telco employees have for their own price plans, consumptive behavior, and the impact these have on their company’s performance. This may be due to the fairly complex price plans telcos are inventing, and our brain’s propensity for linear thinking certainly doesn’t make it easier. It may also be because Telcos rarely spend any effort educating their employees about their price plans and products (after all, employees often get all the goodies for “free”, so why bother?). Do a simple test at your next town hall meeting and ask your CXOs about your company’s price plans and their effectiveness in monetizing consumption.

So what to look out for?

Many in our industry have an inflated idea (to a fault) about how effective consumptive growth is being monetized within their company’s price plans.

Most of today’s cellular data plans can accommodate substantial growth without leading to equivalent associated data revenue growth.

The apparent disconnect between the growth rate of cellular data consumption (CAGR ~30+%), in its totality as well on an average per-customer basis, and cellular data revenues growth rate (CAGR < 10%) is simply due to the industry’s price plan structures allowing for substantial growth without a proportion revenue growth.

ACKNOWLEDGEMENT.

I greatly acknowledge my wife, Eva Varadi, for her support, patience, and understanding during the creative process of writing this Blog.

FURTHER READING.

Kim Kyllesbech Larsen, Mind Share: Right Pricing LTE … and Mobile Broadband in general (A Technologist’s observations) (slideshare.net), (May 2012). A cool seminal presentation on various approaches to pricing mobile data. Contains a lot of data that illustrates how far we have come over the last 10 years.

Kim Kyllesbech Larsen, Mobile Data-centric Price Plans – An illustration of the De-composed. | techneconomyblog (February, 2015). Exploring UK mobile mixed-services price plans in an attempt to decipher the price of data which at the time (often still is) a challenge to figure out due to (intentional?) obfuscation.

Kim Kyllesbech Larsen, The Unbearable Lightness of Mobile Voice. | techneconomyblog (January, 2015). On the demise of voice revenue and rise of data. More of a historical account today.

Tellabs “End of Profit” study executive summary (wordpress.com), (2011). This study very much echoed the increasing Industry concern back in 2010-2012 that cellular data growth would become unprofitable and the industry’s undoing. The basic premise was that the explosive growth of cellular data and, thus, the total cost of maintaining the demand would lead to a situation where the total cost per GB would exceed the revenue per GB within the next couple of years. This btw. was also a trigger point for many cellular-focused telcos to re-think their strategies towards the integrated telco having internal access to fixed and mobile broadband.

B. de Langhe et al., “Linear Thinking in a Nonlinear World”, Harvard Business Review, (May-June, 2017). It is a very nice and compelling article about how difficult it is to get around linear thinking in a non-linear world. Our brains prefer straight lines and linear patterns and dependencies. However, this may lead to rather amazing mistakes and miscalculations in our clearly nonlinear world.

OECD Data Explorer A great source of telecom data, for example, cellular data usage per customer, and the number of cellular data customers, across many countries. Recently includes 2022 data.

I have used Mobile Data – Europe | Statista Market Forecast to better understand the distribution between cellular voice and data revenues. Most Telcos do not break out their cellular voice and data revenues from their total cellular revenues. Thus, in general, such splits are based on historical information where it was reported, extrapolations, estimates, or more comprehensive models.

Kim Kyllesbech Larsen, The Smartphone Challenge (a European perspective) (slideshare.net) (April 2011). I think it is sort of a good account for the fears of the twenty-tens in terms of signaling storms, smartphones (=iPhone) and unbounded traffic growth, etc… See also “Eurasia Mobile Markets Challenges to our Mobile Networks Business Model” (September 2011).

Geoff Huston, “Comparing TCP and QUIC”, APNIC, (November 2022).

Anna Saplitski et al., “CS244 ’16: QUIC loss recovery”, Reproducing Network Research, (May 2016).

RFC9000, “QUIC: A UDP-Based Multiplexed and Secure Transport“, Internet Engineering Task Force (IETF), (February 2022).

Dave Gibbons, What Are Elephant Flows And Why Are They Driving Up Mobile Network Costs? (forbes.com) (February 2019).

K.-C. Lan and J. Heidemann, “A measurement study of correlations of Internet flow characteristic” (February 2006). This seminal paper has inspired many other research works on elephant flows. A flow should be understood as an unidirectional series of IP packets with the same source and destination addresses, port numbers, and protocol numbers. The authors define elephant flows as flows with a size larger than the mean plus three standard deviations of the sampled data. Though it is important to point out that the definition is less important. Such elephant flows are typically few (less than 20%) but will cause cell congestion by reducing the quality of many requiring a service in such an affected cell.

Opanga Networks is a fascinating and truly innovative company. Using AI, they have developed their solution around the idea of how to manage data traffic flows, reduce congestion, and increase customer quality. Their (N2000) solution addresses particular network situations where a limited number of customer data usage takes up a disproportionate amount of resources within the cellular network (i.e., the problem with elephant flows). Opanga’s solution optimizes those traffic congestion-impacting flows and results in an overall increase in service quality and customer experience. Thus, the beauty of the solution is that the few traffic patterns, causing the cellular congestion, continue without degradation, allowing the many traffic patterns that were impacted by the few to continue at their optimum quality level. Overall, many more customers are happy with their service. The operator avoids an investment of relatively poor return and can either save the capital or channel it into a much higher IRR (internal rate of return) investment. I have seen tangible customer improvements exceeding 30+ percent improvement to congested cells, avoiding substantial RAN Capex and resulting Opex. And the beauty is that it does not involve third-party network vendors and can be up and running within weeks with an investment that is easily paid back within a few months. Opanga’s product pipeline is tailor-made to alleviate telecom’s biggest and thorniest challenges. Their latest product, with the appropriate name Joules, enables substantial radio access network energy savings above and beyond what features the telcos have installed from their Radio Access Network suppliers. Disclosure: I am associated with Opanga as an advisor to their industrial advisory board.

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