Four Data Capabilities for Telecommunications
March 22nd, 2017
One of the big themes at Mobile World Congress this year was how the telecommunications (telco) industry can benefit from data. Telcos are increasingly looking to develop new applications on top of their data in an ongoing race to escape commoditization of their core connectivity business. While many have invested heavily in analytic technologies, they still struggle with translating the insights into actions.
This post looks at four business analysis capabilities that connect the dots between promising applications of data assets:
- integrated customer view
- optimization of network capex and opex
- improved contact center productivity
- real-time location intelligence
Integrated customer view
A data lake is an architectural approach that consolidates many disparate data sources across the enterprise into a single repository, or lake. This removes friction from an organization’s data value chain, making it easier and faster to surface insight across operational silos. Most telcos have established one or more data lakes across their organization. The anchor use case that prompted these investments is often the desire to obtain an integrated customer view.
Specifically, a data lake can be used to integrate the entire transaction and usage history of an individual, household, or corporate account across a broader range of touchpoints. Usually it is the Marketing or the Base Retention team who is tasked with building a “golden record” of each customer that joins transactional and usage data. The golden record provides a relationship and customer lifetime value view of a household. For markets with a high proportion of pre-paid customers, for instance, this integrated view provides exponentially more insight than the prevailing SIM centric view where a customer is literally “just a number.”
Telcos use these insights to drive so-called next-best-actions (NBAs) in their marketing campaigns to this segment. A common NBA for the prepaid segment is to a accelerate “top ups,” enticing consumers to prepay for usage sooner or in larger increments. That action is very profitable because a fair share of prepaid service credits expire unused.
The insights this generates are not limited to the commercial teams. For the first time, it is possible to analyze and report network events at a “true” customer, service, network element, and device level, starting with mobile network data.
Over time, more data sources can be added to provide an exhaustive end-to-end view of all organizational touch points for a customer. Usage data from all products joins the data lake, including broadband, pay TV, and over-the-top services. All channels of interaction come into view, including customer operations, self-care, point-of-sale, and digital. In that way, the golden record that might start as a 90 or 180-degree view of a customer relationship is ultimately extended to a true 360-degree view including all behavioral data, interactions, and observations.
Optimization of network capex and opex
Telcos, even in medium-sized countries, spend hundreds of millions of dollars per year on network rollout, upgrades, and maintenance. What if you could use the insights from better analytics to defer or avoid even a small percentage of that spend? You could, for example, look at how improvements in LTE in specific neighborhoods correlate to likely business outcomes such as revenue growth, customer satisfaction, and churn.
Admittedly, that can be a daunting project. An easier starting point is to look at the operational data generated by your existing network. This is based on an analysis of typical cost categories such as site maintenance and rental, energy consumption of mast sites and base stations, personnel expenses, and equipment replacement. A basic objective of this analysis is to explore how the network operations team can “do more with less.” Since the various physical resources and associated costs often sit in separate silos and disparate systems, an analysis that is based on joined-up data sources often yields unprecedented insights.
A more advanced objective of this analysis is to relate network opex to better customer and business outcomes. For instance, data science can be used to correlate network maintenance with congestion (when, where, from what types of traffic?), perceived call quality, commercially relevant gaps in indoor and small cell coverage, and applications/users/devices exhibiting behavior anomalies.
Improved contact center productivity
For many carriers, an increasing share of support cases are related to mobile data usage and associated charges. Traditionally, contact center agents do not have granular insights into a particular customer’s data usage, and hence are unable to provide effective call resolution. Moreover, customers who are not digitally savvy may consume contact center agent attention because, for example, they are unaware of the battery drain of certain settings and mobile applications. This generates costly caseload for the carrier’s call center, preventing agents from focusing on more valuable customer interactions such as up-selling or actual network issue resolution.
Insights from data science can be used to off-load these cases to cheaper channels of engagement, such as online self-service and interactive voice recognition. Specifically, the insights from data science models running in production are accessed and consumed by any authorized users of the data lake. These users include business analysts, who might leverage the insights for periodic changes to the menu of options or to the script that consumers experience when calling in.
However, making these insights actionable at the front-line in near real time requires many process and systems changes. While machine learning can generate timely and effective recommendations at each step of the customer journey, most carriers lack holistic and flexible customer journey optimization systems that would allow these insights to be fed into a front-line system easily (e.g., a recommendation pops up on the user’s mobile device or on the call center agent’s screen). Hence, while data can create material business value in both network operations and customer operations longer term, the network operations domain may be more suited to agile experimentation, particularly since customer operations are often outsourced.
Real-time location intelligence
Over the last few years, many carriers have created a business unit dedicated to new ventures such as advertising, online media, classified, mobile payments, etc. A starting point for this capability is to visualize on a map the movement of mobile phone subscribers in a segmented way. These insights can also be used by the carrier itself to optimize its media and outdoor advertising spend. For instance, a visualization showing a retailer’s catchment area would be based on insights drawn from the following data sources: where the mobile subscriber is at home, where the mobile subscriber works1, what segment they are part of (e.g., blue collar, high income, out-of-state visitor), what their typical commuting routes and stops are, etc. Naturally, this is served up in an aggregated way that respects the privacy laws of each jurisdiction. Retailers, advertisers, public transit agencies, and others with a keen interest in all things local will subscribe to that real-time intelligence.
Conclusion
In our experience, the capabilities covered here generate substantial value for a carrier’s bottom line. In a future post, we will look at the data sources needed to enable these capabilities. In the meantime, learn more about how we can help you build your own data strategy with our Data Strategy Position Paper, or by getting in touch.
1. Mobile subscriber “at home” BTS (base transceiver station) and mobile subscriber “at work” BTS, respectively.↩