By Barton Goldenberg
Let’s briefly examine how a few organizations are applying Big Data analysis and insight to work.
• A major insurance company’s automobile division determined some time ago that to accurately assess the risk associated with insuring an individual’s car, they needed more than the traditional demographic variables like age, sex, location of the vehicle, etc. This is why they recently teamed up with a third party to offer their Drive Safe & Save In-Drive program that promises “the safer you drive, the more you save on your auto insurance.” By installing inside a customer’s car a technology device that monitors the customer’s driving behavior, this insurance company is able to collect additional valuable information to assess how often the customer drives the car, the distance they typically drive for, how often they brake heavily, etc. The In-Drive program data complements existing demographic data, and allows this company to build Big Data models that analyze this data so that in the near future each branch of the company can offer the right insurance product at the right price to each user based on the unique driving characteristics and needs of that user.
• A major food distributor was one of the earliest users of Big Data. By creating sophisticated data models, they were able to analyze local purchase patterns so as to be able to recommend to an operator exactly what products they should be purchasing to optimize business sales. This has created a valuable collaboration between this distributor and the operators, to the envy of most of the food manufacturers that would like but do not have this kind of partnership with the operators.
• A major financial services company wanted to sell additional annuities, life insurance, and mutual funds through their Financial Advisor (FA) network. To do this, they leveraged Big Data modeling tools to determine who were their best Financial Advisors (FAs), inclusive of identifying the “high potential” FAs, and why. With this knowledge in hand, this company created a game plan to improve FA sales performance via an effective cross/upsell program as well as to improve operational efficiency by targeting the best FAs for identified products. A by-product of their Big Data modeling was a better understanding of when FAs churn and what to do to increase retention of targeted FAs.
In my next blog post, I will discuss the components of effective, data-driven decision-making.
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Barton Goldenberg, is the founder and president of ISM Inc., customer-centric strategists/implementers serving best-in-class organizations globally. As a CRM leader for 30 years, he was among the first three inductees in the CRM Hall of Fame. Recognized as a leading “customer-focused” author, his latest book, The Definitive Guide to Social CRM, is hailed as the roadmap for Social CRM success. Barton is a popular speaker on “maximizing customer relationships to gain market insights, customers and profits”. He is a long-term columnist for CRM Magazine and speaker for CRMevolution and frequently quoted in the media.