[caption id="attachment_7942" align="alignleft" width="300"] Source: The Express Tribune http://tribune.com.pk/story/786369/october-surprise-inflation-hits-record-17-month-low-clocks-in-at-5-8/[/caption]
From time to time, policymakers, government officials and telco executives find their email in-boxes filled with alarmist, and sometimes unsolicited, press releases warning about mobile internet price increases, measured according to a price comparison methodology called the “Digital Fuel Monitor” (DFM). These press releases include an offer to purchase the full version of the very expensive report to get fully acquainted with its results.
The reports conduct price analyses in the mobile telephony market in different countries of the European Union and the OECD, typically concluding that prices are on the rise, especially in those countries where markets have recently consolidated.
In one of its most recent reports, it was claimed that the prices of Drillisch in Germany had risen by 138% since the merger of Telefónica and E-Plus, and O2's prices in that country had increased by double digits in the same period.
Such statements seemed rather surprising in view of the actual evolution of mobile internet prices in Germany and the reported ARPUs of the companies, and thus likely inaccurate. The DFM report had already been challenged in the past both by Vodafone and by Frontier Economics so we were reluctant to spend a large amount of money to buy it in order to understand a set of conclusions that we did not share. Instead, Telefónica decided to dedicate those resources to assess the validity and consistency of the DFM methodology. We approached the consulting firm Solchaga Recio & Associates (SR) to conduct a review of the methodology, which we are glad to make hereby freely available.
The findings are conclusive and reveal a serious conceptual and methodological flaw in the DFM approach. The flaw leads to ungrounded results, nevertheless marketed with no qualms.
DFM’s method is based on the study of a metric called “price per incremental gigabyte” on the grounds that it is the one used to calculate competitiveness in the electricity sector, in this last case using €/kWh.
SR’s review explains that this metric is generated by plotting a range of data packages commercially available on a graph showing the data package size (GByte) versus price (€) from an operator, and then drawing a trend –regression– line. Then, the report uses as a measure of “price”, the slope –being in €/GByte – of the line: the greater the slope, the greater the price.
To assess price changes –either at different points in time or prices by different providers– DFM quantifies the difference between the two lines’ slopes. SR’s analysis shows that what is actually being measured is the difference in unit prices between the two group of data packages being compared, not a change in the overall average price, which is a very different thing from the price per unit of the packages themselves. It would be the same as saying the distance travelled by a car is its speed, whereas the speed is the distance travelled over a given time – or the slope of the line plotting distance versus time.
This conceptual mistake is explored by SR using crystal clear examples. SR moves the points on the line –data package size or price– in ways that are beneficial to customers: either by reducing the price per unit increasing the data allowance volume of data provided, or by reducing the package price. Then, they assess the results yielded by DFM’s methodology.
The review concludes that according to such methodology, the launch of a new data package plan with a larger GByte allowance and higher price, could be understood as if the carrier has undertaken a real price increase, even if the new plan would actually have the lowest price per unit than any pre-existing plan in a given market. There are cases where even a reduction in the price of a plan is also interpreted as a price increase as it increases the slope of the regression line.
Lastly, SR’s review also shows how the DFM methodology would lead to the conclusion that operators with lower prices are more expensive than others whose prices are actually higher.
The nonsensical nature of these results brings the flaws of the analyzed methodology into sharp focus. We are happy to have spent our money gaining an understanding of the DFM methodology and its shortcomings. We suspect this will lead policymakers and executives to treat the figures advertising the DFM’s findings with the utmost precaution.
You can –at zero cost–- read the full report here.