There’s no doubt about it-telecom is a numbers game, where data is the king. Take note, though, data doesn’t merely refer to information pertaining to subscribers, average revenue per user, et all. It is, essentially, the entire gamut of numbers generated by the global telecom space on a daily basis.
To illustrate, industry analysts have estimated that a copious 2.5 quintillion bytes of data pertaining to individuals, places, locations, processes, et all, is net every day. This data comes from multiple sources, such as sensors used to gather climate-related information, posts on social media sites, digital pictures and videos, purchase transaction records, cell phone GPS signals, the list is endless. That’s one side. On the other, 7,857,452730 (and counting) mobile connections were counted globally (by GSMA Intelligence for November 2016) and telecom operators net revenue of $1.06 trillion in financial year 2015. In other words, number crunching (albeit a run-of-the-mill process), is equally essential.
There is a catch, though. Operators would do well to remember that merely aggregating vast amounts of structured and unstructured data will NOT set the cash registers ringing. What will, though, is a clear-cut plan on how to extract actionable insights from the data pile. This is where (and why) big data steps in. Now, without running the risk of repeating myself, (for blogs on big data and analytics do tend to get repetitive) let me begin by saying that deploying these tools isn’t just a necessity for any operator, it is a norm. Here’s why-careful and thorough analysis of this diverse and unformatted digital data can help operators unearth new revenue streams, as well as gain a mine of insights into a customer’s behaviour. Going a step further, operators can scrutinize and track conversations on social media to ensure no negative publicity is coming their way. In fact, the possibility of creating and supporting various hypotheses on the business becomes a reality for these players. How? Well, simply put, it requires operators to deep-dive into this unstructured pool of information to analyze it against existing business warehouse data in an accurate and concise manner.
This is, of course, merely the tip of the iceberg. But, the focus of this blog isn’t to expound the benefits of big data on an operator’s business. While that is, of course, a given, it isn’t the only aspect. What I am alluding to is how big data and analytics can be effectively leveraged by telecom retailers to push contextual, personalized and relevant offerings to customers in a timely manner and at any point in their journey.
Why the focus on retailers? Well, they are an important, if oft underestimated part of a customer’s (especially prepaid) telecom experience. In fact, it wouldn’t be erroneous to state that a prepaid subscriber’s journey is far from simple and very patchy. Here’s how-typically, operators do their bit by sharing the best and most relevant offers available with subscribers and retailers alike. And that, unfortunately, is where the link ends. Why? Well, because in this entire process, there isn’t any sync between the retailer and subscriber. For example, the operator shares the details of Plan A with the subscriber and the details of Plan B, C and D with the retailer, along with the entailed commissions (of course). The subscriber, meanwhile, finds Plan A to be in line with their requirement and approaches the retailer so as to purchase it. Only to find that the retailer is completely unaware of the plan in question, let alone what kind of commission comes with it! In other words, the entire experience boils down to low awareness and thus low profit for the retailer.
Now, let’s step back to gauge the larger picture. Operators are, by and large, a bit wary of prepaid subscribers. Why? Well, to begin with, the level of uncertainty is higher, compared to their post-paid counterparts. The latter receives a bill every month and operators have full, detailed profiles of each customer they’re serving. All in all, a win-win proposition for both. It isn’t that cut-and-dry with prepaid customers. It is often cited as the segment operators know the least about, with good reason! The operator is not interacting with this segment on a monthly basis. These players are neither sending a bill, nor have adequate information to chalk out a detailed profile of these customers. The last point holds true, especially in the developing world, where customers can purchase inexpensive SIM cards at various retail outlets (such as grocery stores, etc). Having said that, however, let’s not forget or underestimate the fact that these subscribers unknowingly impact an operator’s revenue, via decisions pertaining to when, where and how much they top-up.
So, what can big data and analytics do to simplify a retailer’s existence? Well, to begin with, it can help these players figure out the kind of offering they should market to each individual customer at any given point in time. This will, of course, be based on where that customer stands from a behavioral point-of-view-i.e.-are they a new customer? When did they last top-up their account? Is their balance sufficiently low to target them? The next step is to reach out to the customer via a simple SMS (or other ways) that highlights the latest offerings they can avail of-all in a relevant, timely and contextual manner, of course!
What makes an offering “contextual”? Well, by deploying big data, all of the retailer’s data is turned into actionable and behavioral insights. These are further used to ensure that the appropriate treatment (in terms of marketing) is applied to each customer at the right time. Let’s break it down further. Essentially, all available data is explored and analysed thoroughly to create an overview of the customer (of sorts). For example, a customer’s financial transactions such as purchases, spending, balances, etc. are scrutinized and combined with call data records across voice, SMS, data, video, etc. With this information, the retailer is able to gather that the customer (for instance) purchased an international calling voucher and made five calls to London and topped-up their prepaid account. Essentially, big data helps the retailer to “plot” events on a timeline for each customer, which are then analysed and familiar patterns are highlighted, in order to predict the customer’s behaviour.
Of course, let’s not forget one important aspect. The marketing messages sent out aren’t design to overwhelm the customer. The idea isn’t to design messages, target individuals and then relentlessly bombard them with a series of messages, hoping that one will find its mark. Big data helps the retailer to identify a set of parameters, pertaining to the customer’s usage patterns, which helps the player model the different messages, the timing, etc, in a selective manner. The aim is to create a sample size of customers to filter and determine what works and what doesn’t. Naturally, the ideas that hit the bulls-eye are tailored as per the target audience base.
This, in a very 360 degree overhead format, is how big data can be used to make a retailer’s business easier and more financially rewarding. Please note, though, there is no “one size fits all” approach to deploying these tools. Having said that, don’t underestimate the mine of information these tools can uncover! That is, of course, if one is interested in enhancing customer experience management and real return-on-investments!