Most health plans are not satisfied with the proportion of their former commercial members who convert into Medicare members. This under-performance is called the Age-in Conversion problem. And it challenges Medicare marketers trying to enroll their own individual and employer-sponsored health plan customers.
Each part of this approach delivers its own value.
Age-in Conversion studies almost always survey a health plan’s own members – or persons of a certain age who were previously in IFM or employer plans and are no longer. The surveys identify the strengths of competition, relative brand values, and the member experience factors that influence age-in choices. Without this decision support, health plan personnel have had trouble aligning on a more positive future in which Age-in Conversion rates rise.
The list scoring algorithms enable health plans to apply market intelligence to an entire list of consumers. These scores help clients make more informed marketing decisions at the prospect level. To develop the algorithms, Deft used both primary market research and additional data. This leads to market insights specifically designed for health insurance marketing decisions.
The new 2019 algorithms produce scores for:
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.
Related Post: Propensity Scores and List Scoring