Mobile operators are facing a revenue dilemma these days. The numbers may vary according to which research you read, but the conclusion is the same: Data revenues are not even close to meeting escalating usage patterns.
By way of example, AT&T estimates that mobile data traffic grew 50-fold between 2006 and 2009. Nokia Siemens, for its part, predicts it will increase 300-fold over the next five years. Despite these increases, Nokia also predicts that data revenues for operators are only projected to triple over the next five years.
Many are trying to redress this balance before the gap widens beyond control. In an attempt to monetize data traffic usage, AT&T and O2 were among the first to revise their pricing plan structures from the “all you can eat” model and limit how much data subscribers can use.
This may be a sign of future pricing models, but it still falls short of optimizing the monetary value of data usage. In some cases, blanket price increases or restriction policies may lead to disgruntled customers and jeopardize the ability to acquire new ones. In others, operators may end up underestimating usage leading to lost revenue opportunities from heavy users; or overestimating it and overcharging lighter ones. In order to succeed, operators need to develop pricing and promotion plans that are smarter, more tailored and more flexible to meet a wider range of subscriber needs. Those pricing models will take into account the quantity of data consumed as well as the type of applications and services.
A Different Landscape
The challenge for smart pricing lies in the inability for operators to track subscriber data usage in any significant detail. Typical business intelligence and analytics tools can only provide operators with limited views of subscriber behavior. What they end up with is a general understanding of the peaks and valleys of data traveling across networks over specific periods of time or regions. This impedes their capacity to build offers that are both relevant and valuable to subscribers while remaining profitable for them. For example, most operators would find it a challenge to build an offer that includes unlimited Facebook usage while charging for any other type of data services (e.g. email, browsing etc.)
There’s a reason that “breaking down” data usage is problematic. The mobile market functions differently from others, where CRM programs can pinpoint the buying and usage patterns of specific subscribers and/or demographics through a centralized point of information capture or database.
Mobile operators, however, no longer control their subscriber base the way they used to. In the recent past, operators were able to use a dedicated portal to control and manage the customer experience, thereby enjoying ready access to usage patterns and behaviors. The past few months, however, have seen a veritable explosion of vendor-agnostic applications and app stores. That means fewer customers are using mobile operator portals to access information and services. While portal data remains useful, it is far less representative of user behavior.
A New Intelligence
The new form of mobile data intelligence can go beyond the basics to provide actionable metrics that drive smarter pricing and marketing decisions. With the kind of understanding MDI provides, operators can be much more innovative and proactive in developing customized plans that better suit their subscriber – and bottom line – needs. It enables marketers to take into account quantity but also the type of applications and services consumed by subscriber segments.
An MDI solution links information about all data services such as data volume, usage patterns, and device information, to create the next generation of KPIs, including:
- For each service: # of users, data volume, # of sessions
- For each service: top devices by # of users, data volume, # of sessions
- For each device: top services by # of users, data volume, # of sessions
These KPIs reflect the richness of the mobile data environment — not just a few services coming from a single provider. In addition, they capture detailed information about any service and application moving across an operator’s network, regardless of the source.
Digging Deeper With Mobile Data Intelligence
In addition to tracking fundamental trends mentioned here, marketers can also track difficult-to-obtain trends such as device fleet distribution, including inbound roamers and gray-market devices. They can also use MDI to uncover “hidden segments” (e.g. top devices for social networking, top application downloads per age group, etc.).
This provides the tools needed for “smart pricing,” so marketers can develop multi-tiered plans that address different users’ requirements, while monetizing the portion of the traffic that was once unaccounted for without compromising customer satisfaction. With the level and accuracy of segmentation information available through MDI, marketers can fine-tune the marketing mix to improve the effectiveness of marketing for acquisition, conversion and retention.
Let us consider a couple of examples.
- A North American operator offers an unlimited data plan for a monthly flat fee. It wants to determine if a recent increase in data usage is driven by feature phones, smartphones or dongles (broadband wireless adapters). By applying MDI, the operator is able to see consumption profiles for each type of access. It turns out that dongles account for the majority of the increase, and that a small cluster of smartphone users also tend to consume far more than the average. The operator then uses MDI to determine if there is a particular type of application that is causing the increase, and finds that P2P traffic accounts for the majority of the increase in dongle traffic, and that the majority of non-P2P users usually consume less than 500 MB per month, while the P2P users typically consume a minimum of 5 GB per month. The solution is a two-tier pricing structure for dongle users — a 500 MB plan at US$35, and 5 GB plan at $60 per month.
- An Asian mobile service provider wants to increase its proportion of post-paid customers who represent a value revenue stream. It applies MDI to understand which customers are more likely to convert from pre-paid to post-paid in order to develop a targeted marketing campaign. The operator discovers that a majority of pre-paid customers who converted to post-paid were heavy users of email, so it gives priority to pre-paid customers with intensive email use for its direct marketing campaign. The result is a five-fold above average increase in take-up rates.
Beating the Revenue Gap
Mobile operators have been facing a significant challenge in meeting escalating data demands without compromising service delivery and revenues. As with any business model, however, detailed insight into consumer behavior is an essential building block to developing marketing campaigns and pricing models that optimize service delivery and customer satisfaction.
While the mobile world has some unique characteristics, the need for mobile data intelligence is becoming increasingly clear. Only through detailed metrics can operators truly realize the revenue potential of data traffic.
Stephen Kerwick is VP of marketing science and analytics at Neuralitic Systems, a provider of mobile data intelligence solutions.