Big Data at the Operator - Better Ways to Approach Roaming

An article published in Connected World Asia Pacific, by Guy Reiffer, VP Marketing and Partnerships at Starhome Mach. End Quote

(1888PressRelease) July 15, 2016 - Obtaining all relevant information about silent roamers, such as location, handset type, visit duration and the breakdown of voice and data usage, is possible when the operator has a comprehensive business view that integrates data from both clearing and network services. The composite Big Data picture gained when combining data from different sources can be leveraged to identify the factors that are important to the individual roamers. With this knowledge, operators can initiate attractive promotions for those high-potential roamers to encourage usage, especially for data.

Big data in telecom is far from a new concept. Mobile network operators have been sitting on huge amounts of valuable information for decades. Technology has finally caught up, and now operators can harness this vast amount of data to better analyze, manage and improve many aspects of their business.

One area of an operator's business to benefit from the application of big data is roaming. Millions of travelers cross international borders daily. In today's LTE/3G world, operators must track their roamers, deliver the same quality of service as in the home network, protect themselves against fraud, ensure they fulfill their wholesale agreements with their roaming partners. All this while maximizing profit.

Big data to identify and encourage silent roamers
Big data can be used to more easily identify and encourage silent roamers to use their mobile devices. Mobile operators are well aware that a great majority of their subscribers do not use their mobile phones while roaming because of fear of bill shock. For example, information from Starhome Mach's customer base of over 300 operators reveals that 68% of global roamers are 'silent,' i.e. they use their phones at a minimum, if at all, while roaming. In fact, Starhome Mach's analysis shows that 1% of active roamers generate 80% of roaming data traffic.

Nevertheless, skepticism exists about the real revenue potential from these roamers. At a recent Roaming Conference in London, a participant noted that while many of the presentations discussed the silent roamer phenomenon, he was not convinced that efforts by operators to change the situation would actually increase income from this segment.

Is this skepticism justified? Experience shows that by analyzing the right information with the right tools, these roamers can be identified and encouraged to use their mobile devices in order to derive additional income. The key element is the ability to identify the silent roamers in real time combined with the ability to analyze which roamers have the greatest revenue potential based on knowledge of subscribers' behavior patterns domestically.

Obtaining all relevant information about silent roamers, such as location, handset type, visit duration and the breakdown of voice and data usage, is possible when the operator has a comprehensive business view that integrates data from both clearing and network services. The composite Big Data picture gained when combining data from different sources can be leveraged to identify the factors that are important to the individual roamers. With this knowledge, operators can initiate attractive promotions for those high potential roamers to encourage usage, especially for data. For example:

Period (1 day, 30 days)
Location (all of Europe, only Paris)
Size (unlimited, 1 GB)
Tariff (different price alternatives)
By analyzing the results of these segmented targeted offers, operators can fine tune and improve their marketing campaigns. The net result is increased usage and revenues.

Big data to manage the quality of service
Another good application of big data analysis at the operator level covers the realm of roaming quality of service (QoS).

Mobile operators have access to great quantities of data about their roaming quality of service (QoS). For instance, key performance indicators (KPIs) measure roaming QoS such as information on multi-network technology, network registration, traffic types and patterns, subscriber segmentation, device types and inter-carrier agreements.

The challenge is using this information effectively to identify problems, measure their business impact, and then prioritize to determine how best to invest resources. It makes more business sense to focus first on problems that affect high-revenue VIP subscribers rather than solving QoS issues that impact a small or low-usage subscriber segment with a limited effect on revenues. This is the essence of revenue-based quality of service.

Operators with access to information from different sources such as data clearing services and the monitoring of network traffic have an important advantage over their competitors, as they can obtain a comprehensive all-in-one 'big data' revenue view of all roaming QoS issues. With this view, mobile operators can use knowledge about their subscribers (who they are, their usage patterns, etc.) to focus on the most lucrative customers and destinations.

Using big data to identify QoS issues can also be applied to performance management, such as the performance of roaming partners. A mobile network operator can use this information to analyze and rank its roaming partners based on key performance indicators, which, in turn, becomes a valuable tool in wholesale negotiations.

With access to the largest possible range of data and effective use of this data, operators can identify the critical QoS issues, measure their effect on revenues, and improve service to their subscribers. The result is improved customer satisfaction and loyalty, which leads to increased operating revenues from an important segment of operators' business.

Analysis of inbound roamers for better business decisions
Inbound roamers are a lucrative segment for mobile operators, who typically invest considerable efforts to increase the number of visitors that register to their networks. MNOs also try to maximize the duration of their visitors' registration period, keeping roamers in their network longer.

From the perspective of the roaming managers, quantifying their share of the overall inbound market is vital in order to understand and improve their position and, in addition, negotiate the best possible wholesale agreements with their roaming partners. To be successful in today's competitive wholesale market, operators need as much information as possible about their competitors and their market share.

Mobile operators potentially have access to large quantities of relevant information. The two major sources of data are the operators' own network systems such as Steering of Roaming (SoR) and Gateway Location Register (GLR), and the call records collected by their data clearing house.

Combining the data from these sources can provide key information about the inbound market such as the operator's and competitors' estimated market share and the ARPU or value of the operator's market share.

Today, margins are under pressure more than ever before, so it is critical that operators have immediate access to all parameters that influence their roaming margin. The time when inter-operator tariff (IOT) costs were the only variable is long over. Operators need to monitor a diverse set of parameters: IOTs, commitments, the quality of partner networks, the retail behavior of their roaming subscribers, and inbound market share.

The value of the inbound visitors from each partner network is very important in assessing and negotiating wholesale agreements with these partners. For example, it is not sufficient that the number of inbound roamers from a particular network meets the wholesale agreement if these roamers yield lower than average usage. Furthermore, having inbound market share information available when negotiating a deal can give the operator a good indication of the potential of each roaming partner in the visited country.

Another benefit of big data analytics for mobile network operators is the ability to detect changes in their share of visitors, which may indicate possible quality of service problems or a change in a partner's steering of roaming strategy. Detection of such changes will allow the operator to act accordingly, such as negotiating a new deal or adjusting steering of roaming to maximize its own margins.

Finally, not all inbound roamers are created equal. By identifying silent and low usage inbound roamers operators can selectively maintain high revenue roamers in their network.

By translating all the inbound market share data points into a proper analysis, operators can efficiently execute their roaming strategies by adapting their steering of roaming, improving the quality of their inbound roamers, and negotiating more profitable IOT agreements.

For more information about Roaming Services, visit: http://www.starhomemach.com/

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