A Customer Named Pat: A Big Data Tale
“Big Data” means much more than mere size. There is incredible value and opportunity to be derived from an in-depth understanding of the data: data analysis and insight by Communications Service Providers (CSPs) will provide a baseline view of how, when and where customers use their services. In today’s world where more and more content, services, and offers are being provided by third parties, data-driven insight can lead to valuable knowledge about the customer and increase the value CSPs provide.
Of course, this insight begins with the customer’s data. And the CSP’s customer data is much greater than the collection of network usage records. Customer data includes historical activity, financial balances, payment methods and timing, an understanding of the social relationships, and movement patterns and “content networks” reflected in the details of those usage records. Who does the customer influence, and by whom is she influenced? Where does she connect to services, and through which device? And what content or service does she find most valuable?
This is a story about Pat. Like many of us, Pat begins her day by combining
breakfast with a series of online activities. She may use a variety of devices, whatever is closest at hand as she prepares to leave the home; a smartphone, a tablet, or a PC. We assume that her busy, online-fueled morning is focused on her top-of-the-mind concerns of the moment.
Pat first searches for dishwashers and compares prices. She then proceeds to the Starbucks site to reload more money onto her stored-value card. After ensuring she is all set with her supply of coffee for the week, she spends a while reading safety reviews for a baby stroller. Her morning browsing ends at the Dallas Morning News’ website where she looks up the 10-day weather forecast for Dallas.
Based on the information of Pat that we have captured throughout the morning, there are a lot of assumptions that can be drawn. The online providers that were involved in any way with Pat’s morning activity can interpret it and present her with various information and offers during her subsequent visits. General Electric and Home Depot might offer her unbeatable deals on dishwashers. Starbucks might offer her a free pastry with her next coffee purchase. Babies “R” Us and Chuck E. Cheese’s send her offers catering to the needs of a young child. And lastly, the Dallas Morning News offers her a subscription for their iPad news app.
So, how valuable do you think these offers are to Pat? At first glance they seem to be very relevant for what she’s interested in at the moment, as reflected by her morning online session.
But a much more accurate view of Pat can be derived not just from the morning spent with the search engine, but coupled with information from other sources like service provider geographical and physical locations, banks, and social networks. By aggregating information from the Biggest data set, we can know Pat all the better. Look closely now—what do you notice? Pat is a man!
Pat is a contractor and is currently working on a home renovation project. General Electric and Home Depot are still interested in Pat, but may make very different offers to him. Location-based information tells us that there is a Peet’s Coffee across the street from the Starbucks Pat usually frequents on his way to the job site. Chances are Peet’s Coffee would like to give Starbucks some competition for Pat.
Pat’s social network data reveals that he, in fact, lives in Chicago and has
teenage children. His sister, on the other hand, is in Dallas and just welcomed her first child into the world! His stroller and weather related searches were in preparation for a trip to visit his sister that he is planning. And while the Babies “R” Us offer might be relevant at the moment, he might also appreciate offers from Best Buy or Applebee’s that are more relevant for the interests of his grown-up children.
So, what can we learn from this Pat exercise?
The customer is far more likely to respond to any interaction, advertising, or pushed content if it is targeted and relevant. If content is targeted and relevant, it is less annoying and invasive. And, if it is less annoying and invasive, the customer is happy and his experience is enhanced. After all, shouldn’t this be one of the top objectives of your Big Data Strategy?
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