By Rob Chimsky, Vice President Insights at Guavus

Average Revenue per User (ARPU) has long been the benchmark metric for success in the wireless industry.

altHowever, ARPU was borne from a wireless age when voice, post-paid, and subscriptions were the dominant drivers of success.

The last couple of years have seen the wireless industry go through dramatic changes as data traffic has become the growth engine, while aggregate traditional transport revenues have plateaued in mature markets. One evolving result of these changes has been to make ARPU increasingly irrelevant as a measure.

The obvious changes that have occurred are the significant growth in data as the primary driver of operator revenue and the increasing prevalence of prepaid services. Data and prepaid have independently changed the nature and level of revenue.

Perhaps an even more important trend related to revenue, though not quite as obvious, has been the extreme growth in embedded wireless. Mature markets today are seeing over 100 percent penetration, a clear indicator of the trend toward people carrying multiple wireless devices.

Each of these devices also comes with somewhat different wireless revenue plans and pricing models, representing different business relationships between the user and the operator. Not only are usage and pricing levels different in these relationships, but profitability and service relationships can also be very different.

In this environment, the concept of ARPU starts to lose any constructive meaning, as it only describes one facet of what has fast become a multi-faceted interaction between the user and the operator.

The end result is that operators must find a more appropriate benchmark for success. The fundamental aspect of this new perspective requires a change from looking at the user as a single discrete relationship to examining the user more holistically and incorporating their various and disparate ways of interaction with the operator.

Verizon, for example, has already taken a step in this direction by stating in 2012 that it would start to look at Average Revenue per Account (ARPA) rather than ARPU, in order to take a broader view of actual subscriber revenue performance.

Added to this, operators need to move beyond traditional sources of returns, which have essentially come from transport, and gain new revenue generators. These new sources will also require a 360 degree view of the customer in terms of characteristics, demands, and patterns, which can be used internally to improve customer experience management (CEM) and externally to create new revenue sources; as well as potentially selling this knowledge to help enhance third-party applications and services.

The key enabler that is allowing operators to gain this more complete customer perspective is big data analytics, which takes data from various sources, fuses it together, correlates it, and identifies valuable insights from the data, in a real-time or near real-time basis, and on a very large scale.

Combining network performance data from operators’ infrastructure, with customer call records, subscription and device information, can create a new type of client DNA, thereby allowing operators to really understand their customers and more effectively measure their business.

The analysis also allows operators to move from being reactive to proactive; predicting potential issues ahead of time, managing these issues in a more structured fashion, and ultimately making better business decisions.

Operators can then use this intimate customer knowledge to change the revenue paradigm, creating new services and sources of income in areas such as ecommerce, gaming, and mobile advertising.

Additionally, having visibility of a customer’s devices, location, mobility habits, and usage patterns, can help operators to better tailor the services and offers available to that customer.

In the future, customer policy and preference selections can also be added to the analysis, so the operator can take effective action based on customer direction in response to a situation observed or projected to occur.

Furthermore, assuming the operator has the customer’s permission, these insights can be shared with third parties to provide a richer user experience inside and outside the operator’s network, as well as enabling another source of revenue for the operator.

All of this creates a much closer relationship between the operator and the customer, which will be hard for a non-incumbent operator to match, and thus creates a strong barrier to customer churn.

The shift in market and technology dynamics is creating a death knell for the ARPU metric, but breathes life into a much more precise revenue model where operators can identify and prioritise customers by profitability through a rich suite of subscriber analytics.

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