The mobile phone is now our ubiquitous companion. While owning a jazzy phone is a compelling style statement, that real value comes from having a unique mobile number, and from all the data that the phone holds.

Till a few years ago, mobile phone numbers were linked to the communication service provider (CSP). So changing the CSP meant changing the mobile number as well. This was such a nuisance that many customers preferred the pain of poor service to the pain of number change.

The introduction of mobile number portability (MNP) allowed the customer to have his cake and eat it too. But it left CSPs searching for their cake, and wondering if they could eat it even if they found it.

In telecom jargon, this is the problem of customer churn. To solve this problem we must turn to big data analytics.

Customer-ChurnWhy does a customer churn in the first place? He churns if he thinks he’s not getting what he wants from his current CSP. What does he want? From the voice perspective he wants good connectivity, crisp conversations, seamless handovers and no call drops. From the data perspective he wants quick downloads, richer content and no traffic jams. And, for both voice and data, the customer wants the lowest costs!

So what can analytics do? It can help the CSP check churn by identifying customer preferences and then monitoring the customer experience.

The initial naïve view is that there must be scores of customer preference types, and it would be impossible to identify and deal with all of them. After all, the businessman who has long prime time conversations with a customer in Dubai or Alaska is so different from the thrifty man from Pune who only believes in missed calls; likewise, the streaming video addict is so different from someone who checks his mail only once in four hours.

In reality things are much less complicated. Less than a dozen segments usually suffice to classify most customer preferences. We use cluster analysis to identify segments so that customers within a segment share similar preferences, and customers between clusters have markedly different usage preferences.

Once these segments are identified, the CSP devises usage plans that best address the preferences of customers in each segment, thereby significantly reducing the tendency to churn.

Monitoring the customer experience pro-actively is just as important to contain churn. There are always tell-tale signs; for example, has the customer recently had angry interactions with the call center, made critical observations on Facebook or Twitter, or unexpectedly changed his usage pattern? These ‘symptoms’ must be immediately treated because industry estimates indicate that retaining an existing customer is much less expensive than acquiring a new customer.

The classical approach to treat such symptoms involved creating loyalty programs, offering more discounts, or throwing some gifts and freebies. But as mobile usage data becomes richer and more pervasive, there are now many more opportunities to extend that personal touch.

Imagine, for example, the following scenario when you make an angry call to complain: The operator greets you cordially and says “I’m guessing you are calling to report the sudden spurt in call drops when you call from your office location. I’m sorry we are having power failure issues in that tower. We hope to fix the problem by 6 pm this evening, but, to show that we are really sorry, we are offering you Rs 100 more of talk time!” This isn’t a fantasy scenario; big data analytics makes it both possible and feasible.

Here are more examples of CSPs providing the personal touch: alerts on mobile data usage, tips on bandwidth hogging apps and services, or bundling in an attractive WhatsApp or Candy Crush offer if analytics indicates that you are an avid user of the app.

preferenceIndeed mobile data analytics can help CSPs get even smarter: how can we increase revenue from existing loyal customers, how can we identify risky customer behavior sooner and take corrective steps faster, how can we reduce the customer retention cost, what is the estimated lifetime value of a customer, and how can we increase this value?

While it has become fashionable to use buzzwords like volume, velocity, value and veracity to describe big data approaches, we have no doubt that mobile big data analytics indeed offers opportunities that are vast, versatile, viable and victorious.

— This article, written with Amit Goenka, first appeared on tele.net.in on May 16, 2014.

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