Monday, December 20, 2010

Predicting Life Expectancy With Magazine Subscriptions (And Other Information...)

A recent Wall Street Journal article says  "Life insurers are testing an intensely personal new use for the vast dossiers of data being amassed about Americans: predicting people's longevity."  The 'vast dossiers of data' are owned by data-gathering companies with "extensive files on most U.S. consumers-online shopping details, catalog purchases, magazine subscriptions, leisure activities and information from social-networking sites..." This information is traditionally sold for marketing purposes, so that direct mailers can target interested customers with their offers. This service is attractive to both the retailer and the customer, because retailers only wish to contact customers that will find value in the offer. Using said information to qualify consumers for life insurance though (as suggested by Deloitte), changes the dynamics of that relationship, giving consumers an incentive to hide transactions, demographic information and purchases from these data-gathering entities... with potentially catastrophic results for their business model.

Modeling customer life expectancy with marketing data offers a number of operational advantages, although "Deloitte and the life insurers stress the databases wouldn't be used to make final decisions about applicants. Rather, the process would simply speed up applications from people who look like good risks. Other people would go through the traditional assessment process." This caveat is necessary because of legal and regulatory requirements surrounding insurance applications, in addition to the implications for the data companies' business model. Assuming that this system could replace blood tests entirely though, customers could be saved the inconvenience of drawing blood, the customer relationship would literally become painless, insurers could price more accurately, and processing timeframes would become instantaneous.

Benefits of Quantification

  • Cost Savings: More efficient than requesting lab tests, "Deloitte says insurers could save $125 per applicant..."
  • Greater Accuracy in Estimating Risk: The use of consumer marketing data in combination with blood tests would enable greater accuracy. For example, does the customer have a gym membership? "A key part of the [test]... was estimating a person's risk for illnesses such as high blood pressure and depression. Deloitte's models assume that many diseases relate to lifestyle factors such as exercise habits and fasts-food diets."
  • Earlier Premium Estimates: Consumers could obtain an accurate estimate of premiums before blood tests were possible. This would reduce the number of misquotes and reduce the lab test expense for individuals who will ultimately not buy insurance.
  • Pain Avoidance Premium: If an insurer could avoid the use of blood tests (and correspondingly, needles) they could charge more than needle-requiring competitors because it is of particular value to 'needle-phobic' customers.
  • Emotional Branding: Even when a customer is not afraid of needles, having your customer associate pain with your brand is never good.


  • The Fair Credit Reporting Act applies, if the Deloitte model is ever used to deny an insurance application. This would require mailing justifications to affected customers, and means that opportunities for consumers to correct innaccurate information in their file must be provided.
  • Needs to be approved by insurance regulators (the article indicated that the idea hadn't been broached with Connecticut, New Jersey or the New York state government).
  • Risk of substantial public backlash.
  • Changes Consumer Behavior and Destroys Data Business Model: If insurers start using information in this fashion, consumers would likely avoid businesses selling customer data to Axciom... thererby reducing model potency over time.
WSJ 19NOV2010. "Insurers Test Data Profiles To Identify Risky Clients" by Leslie Scism and Mark Maremont.