Boost AEP Campaigns with Targeted ListsDid Your AEP Campaign Measure Up?
For many health insurance Marketers, a key problem is managing and improving the return on investment of their campaigns. A typical metric used is overall ROI: sales divided by marketing & sales dollars spent. Another metric capturing management’s attention is “cost-per-conversion” or marketing & sales dollars spent divided by number of sales. Clients tell us that making the marketing dollar go further is a constant concern. And this time of year, as clients review last Fall’s marketing results, the spotlight intensifies.
One way to boost Marketing’s ROI is to leverage analytics that identify consumers who are most likely to respond to promotions. The analytic tools that generally provide the greatest impact are those that identify consumers most likely to respond to direct mail, direct response TV, on-line ads, or social media campaigns. Additionally, analytic tools to identify consumer product choice (MA vs. MedSupp) as well as specific product preferences, and propensity for shopping activities provide additional insight. To develop these analytic tools, Deft Research uses the shopping and product preferences of health insurance consumers. By combining these analytical models, clients have been able to focus their marketing dollars more effectively.
But analytic models on their own are only a small piece of delivering actionable market insights. Most critical is the data being used in the models as well as assessing how this data is interpreted, processed, and cleansed. Models built on data without the necessary domain knowledge and preparation can produce results which don’t produce meaningful insights, or worse produce misleading results. Deft conducts extensive analysis and research on the data incorporated into the models before the model building process begins. Once built, the models are validated using real world data and scenarios to measure model performance.
Deft Research’s solution uses primary, local market, and big data to predict consumer health insurance behavior.
Deft Research has also taken the unique approach of combining publicly available demographic data with Deft’s proprietary primary research. This primary research provides granular insights not available through generalized demographic data. By combining both data sources, Deft develops a more robust model. Data from these models can also be incorporated into client models. This allows clients to leverage Deft primary research data in other ways and use the detail for their own proprietary modeling.
Deft Research has found that the health insurance specific information available through a combination of historical survey research, market characteristics, and publicly available demographics has valuable applications in predicting consumer behavior. Specifically, health insurance data can be used to target consumers through a channel and with a product that they are most likely to find valuable. When that occurs, the ROI or cost-per-conversion (and all other metrics) tend to improve substantially.
Deft Research’s List Scoring service uses propensity scores that are powered by a decade of proprietary survey data from more than 100,000 health insurance consumers. Health insurers send Deft their list and Deft appends consumer data and assigns a propensity score to each individual on the list. Health insurers then use the scored list to develop targeted campaigns that align the right product offering to the right consumer and reduces marketing expense.