2 min read

The New Open Enrollment in Health Insurance

Now that the Senate has greenlighted the tax bill, healthcare insurance organizations will be forced to take action. With consumers and families no longer required through the individual mandate to have health insurance coverage, enrollment programs in most states will need to assess the ripple effect on coverage and adjust to a set of new realities, including who they want to cover and its impact on marketing strategies.

It is not uncommon for insurance carriers to build direct mail and online campaigns based on models of consumer risk pools that associate demographic attributes to score a consumer file. Predictive models are driven by the extrapolations of demographic offline and online indicators — activities such as plan matching based on age, health profiles, location and affluence. These profiles are typically matched to households and their otherwise unrelated consumer behaviors. Once identified these households and persons can be targeted based on plan offers to postal addresses. These addresses are then mapped through onboarding to digital IDs based on one-off web visits or searches. Online these inferred actions score people to anonymous pools of cookies. Each pool of 50 to 200 cookies are formed as anonymous clusters built from inferred actions and device IDs. Due to the inherent challenges in the cookie matching process, clustering is necessary in order to secure any semblance of coverage and scale.

While the cookie matching process inflates audiences of high scoring households, it also dilutes the integrity of the audience. The incremental cost to serve an online ad is often so low, this dilution of the target audience is typically overlooked.

An alternative method is now available, designed specifically to deliver the coverage, accuracy and validation that is nearly impossible with healthcare consumer audiences. This new methodology is established by developing healthcare profiles based on provider services offered in the local markets along with household demographics. Layers of deterministic data are used to quantify current plan enrollees so that forward-looking marketing can identify the right households and (most importantly) validate the delivery of online advertising to the right audience. This solution addresses both the veracity of the audience as well as end-to-end execution of the campaign.

The Semcasting Medical Healthcare Data (MHD) is a suite of data solutions outlining over 1.2 million health care providers, 120,000 hospitals and clinics, and the distribution profiles of over 3,000 brand and generic drugs for over 1,550 disease states. Combined, this data was used to generate a robust Health Index that ranks patient populations in service areas.

In order to protect consumer privacy at a level that meets or exceeds HIPAA health privacy recommendations and standards of HiTrust, coverage of consumers online is maximized using Semcasting’s patented Smart Zones targeting and Mobile Footprint technology – providing coverage of over 250 million U.S. Homes and Mobile delivery points.

To review a case study and learn more please contact us today or download our Medical, Healthcare, and Pharmaceutical Data Suite.

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