April 13, 2012

Insurance Big Data - Predictor Harvesting

Increasingly in, insurance, retail, telecom and banking industry, the ability to accurately pinpoint customers who have a greater chance of exhibiting certain behavior and proactively put interventions in place is becoming a competitor differentiator. One of the most important tasks in trying to model customer behavior is to precisely zero in on predictors which influence a behavioral outcome using advanced analytical models. For example in Insurance industry which are the top 5 influence levers which determine whether a policy holder would surrender his policy

One lever which organisations use is "FIELD IMMERSION". Basically In field “immersion”, the behavioural modelling/investigation team spends a day in the life of an agent /customer trying to get direct experience of the factors at play while an agent is trying to persuade an existing subscriber to hold on to his/her policy. The rational for direct “customer immersion” is based on the fact that direct observations of these interactions can give us a window into workings of agent-customer dynamics and yield new insights as to what causes a policy holder to renew a policy. This can them be statistically modeled into the policy scoring model provided the data exists. Also thru field immersion a behavioral investigator gets access to nuances /behavioral dynamics at play which we may not be privy to in other methods. For example while observing the agent-customer interaction during a field visit led us to hypothesize that agents had a greater influence over policy holder if the agent-customer relationship was > 3 years and the customer’s age was greater than 47 years. This was statistically validated by a simple discriminant analysis and hypothesis testing and finally fed as an input to the scoring process. This was one practical example of a modelling variable received from field immersion whose statistical test of significance (chi square value) was better than those received from hypothesis workshops. Similarly in another interaction it was observed that in certain zip codes, there was a unique word of mouth referral campaign being executed which was causing policy holders to surrender. This was an instance where data to model this phenomenon was not readily available and the modeling team brainstormed with the customer team to institutionalize a new data collection process to capture details about competitive campaigns which can fuel surrenders


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