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Has big data had its moment in the sun? Interestingly, a few years ago, (that is, when the concept was making headlines) this query would have elicited a resounding NO. Today, though, the response isn’t that clear-cut.

Here’s why-on one hand, industry data suggests that companies are looking for new ways to leverage analytics to the fullest. The bottom-line? Well, boosting their bottom-lines, actually (pardon the pun). Essentially, the idea is to highlight a shift towards leveraging big data smartly and not just for the sake of it. In other words, don’t just analyze large data reservoirs; unearth those vital bits of information that make the difference to your business. To add to this, research firm IDC has predicted that revenue from the sales of big data and analytics applications, tools, and services will increase by more than 50 per cent, from nearly $122 billion in 2015 to more than $187 billion in 2019. Interestingly so, services will account for more than half of all revenue by 2019. It means most companies will be using big data technology in conjunction with expert knowledge. That’s that, then.

Now, permit me to play devil’s advocate. Why get restricted by terms like big data? Isn’t data, by itself, enough, given the role it plays in our lives today? Sample this, for instance, the Cisco Visual Networking Index: Forecasts and Methodology, 2016-2021 has stated that in 2016, global IP traffic was a staggering 1.2 ZB per year. By 2021, this is expected to touch 3.3 ZB per year. In other words, we live in a data-rich age, and how!

And this is the interesting bit-companies haven’t given up on the concept of mining vast sets of data for insights. That’s still very much prevalent. So much so that one doesn’t need to fix the “big data” label on it. One just calls it data. It is now taken for granted that the huge data at our disposal will glean actionable insights.

But, there is a catch, though. The technology of collecting this vast data repository isn’t enough. What is to be done with it? What applications can be developed from this information? Actually, to rephrase, which applications can best leverage this data?

And here’s where I bring in the customer experience management angle. After all, what’s a blog on big data analytics without it? As mentioned before, the technology for its own sake simply isn’t good enough anymore. If it were, would we actually be reading articles heralding the demise of big data? And there are a fair few, I must say! On that note, even deciding to use the data at one’s disposal smartly isn’t enough. One must ask the following questions:

What is the quality of insights one obtains from the data?

What is one trying to achieve from this data?

Does one have the appropriate data to address the above?

Will the insights obtained be beneficial for one’s business?

How does one leverage these insights for this purpose?

Net, net, the idea of this blog isn’t to say that big data is dead. Nor is it to applaud its achievements. The idea is to say that big data for the sake of big data simply isn’t enough. The idea is to get to the why and how of existing data. It is learning how and which applications can be leveraged to even reach the why’s and how’s. It is about accepting that big data can (and does) have its pitfalls.

It may just be about smarter data. Or not. Watch this space for more.



January 24, 2018 0 comment
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The customer is always right. No longer just part of Marketing 101, the axiom holds more sinister implications for retailers. Especially today, where being “always-on” isn’t just a phrase-it is a business norm!

Permit me to elaborate-today’s customers are an increasingly sceptical lot, a fact that isn’t helped by disruptive forces like mobile devices and social media. Today, the bottom-line, no, necessity is shifting from passive to active brand engagement. And why not? The number of customer touch points is increasingly exponentially. It is thus only logical that brands ought to be where their customers are.

So, coming to the crux of this piece-big data analytics can help retailers achieve this. And lots more. Why? Well, retailers typically have access to a significant volume of data. Generated across the supply chain and at diverse customer touch-points, this data is further multiplied via digital customers and social media channels.

However, retailers will do well to remember that merely aggregating a vast amount of structured and unstructured data isn’t likely to translate into healthy bottom-lines. The idea is to extract actionable insights from the data pile. Of course, this is just the tip of the iceberg. Let’s break down the argument.




How Big Data Analytics is the Ticket

Uncovering Actionable Customer Insights

Big data analytics enables retailers to uncover a mine of information pertaining to a customer’s behaviour and usage patterns. This further enables them to push contextual, relevant and personalized offerings in a timely manner and at any point in the customer’s journey.

This process is usually based on where that customer stands from a behavioral point-of-view. By examining certain factors, such as the amount of time the customer has spent on the network, usage patterns, etc, the retailer can reach out to the customer via an SMS (or other ways) that highlights the latest offerings they can avail of. Moreover, 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. 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.

Ensuring Retailer Loyalty

Big data analytics can be leveraged to examine a retailer’s behaviour. Thereafter, personalized offers and incentivization schemes can be developed, to ensure enhanced and improved retailer loyalty.

Enhancing Channel Productivity

A “top-down” approach is usually adopted, with regard to the sales channel. This implies that targets are identified at the operator’s level and are expected to be absorbed by the channel’s various elements across the organization. However, region-wise analysis of past performance and trends can improve the accuracy of predicting future sales potential and help the operator set channel targets accordingly. The bottom-line is an optimally utilized sales channel, with relevant targets and adequate incentives.

Before one gets carried away, though, it is important to remember that big data analytics isn’t the panacea to all retail-related evils. It will, however, play a significant role in helping brands keep pace with their customers. After all, the customer is always right. It is just a question of pushing the right buttons to ensure the customer stays. Isn’t that the whole point, after all?

August 17, 2017 0 comment
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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!

November 16, 2016 0 comment
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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.

March 3, 2016 0 comment
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