Telecom CXM enabled by Big Data Analytics

The rapid proliferation of smartphones has brought with it many changes like the increase in data consumption, migration to 3G/4G, emergence of OTT providers besides increasing customer’s expectation from the networks. Today’s mobile networks are perpetually in a state of flux, where the end user experience is dependent upon several internal (device related) as well as external factors. While earlier, customer engagement management could be managed by pulling a few network levers, it is now more complex, calling for bespoke solutions that leverage big data analytics to provide holistic view of the user’s actual experience on the networks.

Operators should realize that in today’s mobile enabled networks, it is usually the subscriber’s perspective of the mobile operator and not the mobile operator’s perspective of the subscriber that counts. In order to drive customer engagement, telcos must step into the shoes of its customers to understand them better. In this context, the maturing of technologies, like, big data analytics couldn’t have come at a better time enabling telcos to sort through huge volumes of data to draw inferences as well as trends needed for understanding customer’s actual (not inferred) user experience.

A good example of this is churn management. Big data analytics is used to spot customers who are about to churn on the basis of dormancy scoring models based on several parameters. Depending upon the dormancy score and the customer’s expected life time value, the system picks the best campaign from the campaign portfolio to drive contextually driven customer retention programs. Similarly, operators can analyze data usage patterns of their customers to profile multi-SIM users, which can go a long way in winning them back through timely customer engagement programs. For example, usage pattern of customer Y reveals “zero” activity during peak hours during the day. In-order to drive engagement the operator could send a promotion, say: “Get 50% off for all calls during 8 AM to 8 PM.”

Any such tool or system should answer the “who”, “when” and “where” of customer side of engagement: Who is the customer? Where is the customer present? When to roll out the engagement? For example, in order to roll out highly contextual campaigns the operators has to first segment the customer (the who) on the basis of demographic information, transactional patterns, life cycle, device type and social groups. Similarly, the “where” of any customer engagement model provide information on customer’s location and network environment. Finally, we have the “when” which lays down the timeframe and the conditions for rolling out any program of customer engagement.

The biggest challenge before the operator is to derive context from the humongous amount of data available in the cloud and on the server and utilize it for driving highly personalized CEM. Data poses its own share of problems — especially, the three Vs: Velocity, variety, volume. How to get insight from data on a real time basis? How to deal with structured as well as unstructured data? How to store this data? Also, operators must realize the customer relationships, if they are not moving forward, tend to atrophy over a period time, and hence need constant tending and nourishment (customer life cycle management).

With so much risk riding on networks, operators have to locate issues fast or risk losing their customers to competitors. In the highly competitive telecom marketplace, operators need bespoke solutions that leverage big data analytics for driving customer engagement processes that are customer triggered, aligned to customer life cycle, interactive and near real time.

Originally published at on March 3, 2016.