Health insurance coverage is provided by several public and private sources in the United States.
In the extremely competitive world of healthcare acquisition, many firms seek a reduction in the cost per thousand mailed. In fact, if a procurement style of cost management is in place that is your KPI. For many years, this approach was sufficient for success. In today’s fragmented consumer marketplace, the focus must be on audience identification. Who wants to buy our policy? Not every consumer over 65 years old wants to buy our specific product-Medicare Advantage or Dental etc. The shift from Cost per M to Cost per Lead is often perplexing due to the increase in cost for the audience/mailing list. It seems illogical to spend more per thousand. However, it is required. Standard demographic mailing: List $20.00/M Total mail cost $350.00/M Response rate .006 Cost per lead=$58.33 (350/6) But if I raise the cost of the mailing list to $70.00/M or $50.00/M higher and get a response rate of 2.0 percent what is the outcome? Propensity based mailing: List $70.00/M Total mail cost $400.00/M Response rate .02 Cost per lead $20.00 each (400/20) The client that I mentioned reduced their mail volume by 3 million pieces and saved over a million dollars annually. By switching their KPI. If your organization has put procurement in charge of your marketing expenses then I recommend you stop and review their decisions. Efficient targeting is the key to lower cost per acquisition as opposed to lowest cost per thousand mailed.
Every year a relatively small group of Medicare enrollees switch their Medicare coverage.
The foundation of successful customer acquisition is prospect data that targets consumers who are in the market to buy a specific insurance product. Too often, insurance marketers rely on simple targeting criteria such as age, income and gender. The use of this commodity prospect data results in a higher effective cost per lead, fewer policies sold, and higher lapse rates. Fortunately, the use of life event trigger data can help you target individuals who are highly likely to purchase an insurance product in the near future.
The foundation of successful customer acquisition is prospect data that targets consumers who are in the market to buy a specific insurance product. Too often, insurance marketers rely on simple targeting criteria such as age, income and gender. The use of this commodity prospect data results in a higher effective cost per lead, fewer policies sold, and higher lapse rates. Fortunately, the use of purchase propensity data can help you target individuals who are highly likely to purchase an insurance product in the near future.
Suppose you are driving your car on a dark and curvy mountain road, on a rainy night, with a very foggy front windshield, dim headlights, and a perfectly clear rear view mirror to guide your path forward? It’s not a comforting scenario. Surprisingly, this is how many companies still operate when attempting to understand and improve their customers’ experiences and the resulting customer satisfaction (or JD Power/Net Promoter) ratings. They know a great deal about the past but very little about the future; they know a lot about the average customer but very little about the individual customer.
When it comes to life insurance leads, too many companies are wasting their marketing dollars. In fact, their cost per lead is 2-3 times too high.
A cursory glance at today's market research news makes it immediately clear that big data and advanced analytics are hot topics So why all the hoopla? What is big data in the context of market research and what are some working examples? What is the real promise of big data market research (Big Data MR)? And most importantly, what immediate next steps can you take to harvest some near-term Big Data MR benefits?
On average, about 10% of your Medicare Advantage members churn each year (Kaiser Family Foundation). Let’s assume that you have 100,000 members and you average $800 per member per month (PMPM) in Medicare payments and premiums to your health plan. At a 10% churn rate, you are losing 10,000 members and $96 million in plan reimbursements!
Are you prepared for AEP 2017? Are you targeting the right Medicare beneficiaries? Are you using the most productive marketing channels for Medicare marketing? What will get you better AEP results in 2017? We answer these questions in our new Executive Brief: Successful Data-Driven Medicare Marketing in 2017.