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 !
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