When applied to lists of senior citizens and 64 year old’s (age-ins), these algorithms help health insurers make more informed marketing decisions. The 2019 algorithms used enhanced techniques to achieve better predictive results.
The new 2019 algorithms produce scores for:
List Scoring is a process of applying an algorithm to a set of consumer records to create an estimate of each consumer’s propensity to act in a certain way. Health insurers use these models to effectively align the right products with the consumers most likely to want them. When list scoring is used, insurers report higher responses to direct mail, and lower costs per sale. To accomplish a variety of goals, insurers may use algorithmic scores alone or in combination with one another.
Deft Research achieved substantial improvements over previous algorithms by incorporating more years of survey data, conducting thorough data mining processes, and applying new machine learning technology.
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Related blog post: Age-in Conversions and List Scoring
About Deft Research
Deft Research is a market research firm and the health care industry’s most trusted source for relevant market and consumer information. Combining primary research, local market data, and big data, our studies provide health plans the information they need to strategically plan their marketing, product and sales campaigns.