Life insurance companies that utilize direct mail marketing have relied on traditional response models to more efficiently target consumers for whom their products are relevant, attractive, appropriate and affordable. The actuaries at life insurance companies have applied sophisticated data science for years to establish risk, premiums, marketing allowances and more. The actuaries at these companies learned a fundamental truth about consumers: people are different demographically, behaviorally and socio-economically. Women generally live longer than men. Smokers represent an extremely high risk. There are high risk activities that some consumers enjoy more than others. In terms of health outcomes heredity has a role, body mass index has a role, and overall lifestyle has a role. Of course, these factors can be unique to each consumer. Paradoxically, the same principles that insurers use to accurately underwrite or deny life insurance policies also apply to the marketing of those policies. Why? Both analytic processes are attempting to predict an outcome; in the case of direct mail marketing, the outcome is a positive response to a marketing campaign and ultimately a sale. Consumers’ responses to marketing are based on different factors. These factors reflect who they are, where they are, how they behave, their attitude toward the world around them, their family situation, and what comprises their overall lifestyles. This is why “one size fits all” or “out of the box” predictive analysis does not work well when it comes to predicting response behavior. There are methods that utilize multiple segment-based models to determine which models—or combination of models—yield the highest response for a given audience. This methodology is alternatively referred to as ensemble modeling, model stacking, or nested modeling. Each of these specific approaches has subtle differences that are meaningful to nerdy data scientists, but their outcomes are the same: build and combine models that yield the greatest lift across diverse, heterogeneous population by determining market segments or geographic segments and refine the modeling parameters within each of those. Today, we’re going to focus on a nested modeling methodology. Nested models are founded on the premise that different consumer characteristics—whether they be geographic, demographic, lifestyle, attitudinal or behavioral—result in different reasons why people respond or do not respond. The problem with this approach is that the more ingredients that get added to the model soup recipe, the more diluted the outcome of that recipe becomes. It’s like the old data science joke analogy about the fallacy of marketing to the average consumer: you’re standing in a bucket of ice water with your head stuck in a hot oven. On average, you feel just fine! Nested models use the same group of predictors, but instead reserve the most differentiating variables to effectively distribute the target audience into more homogeneous population cohorts. Each group is modeled independently, and then reassembled into a single target audience with optimum response propensity. As we said, variables with significant value differences across different groups make good candidates for nesting within the overall ensemble. In the example below, we see that consumers’ state of residence is a meaningful separating factor because of very different population attributes. In the example below, we are focusing on the 64+ population. Nesting is not necessarily limited to a single variable split; indeed, the modeler can further refine and tune the final model through multiple levels of nesting. This has become a far more feasible approach than in the recent past due to fast (usually cloud-based) computing resources and statistical software applications that facilitate builds like this. In our example, we find that we can leverage many of these demographic variables to optimize response for our life insurance direct marketing campaign. How does this approach work in reality? The effectiveness of response models is measured by lift, a statistic that demonstrates how much mailing efficiency the final model yields with actual response rates. In the chart below, “global model” is the application of a single, traditional response model. “Individual state models” refers to models constructed like the diagram above demonstrates, and “best model for each case” selects the higher of the two resulting propensity scores for each prospective consumer. The bottom line is that with a smart, thoughtful nested modeling approach, an insurance company would realize enormous savings in its direct marketing costs. Reach us to see how our approach can benefit your business.
First, the consumer will have from October 15th to December 7th, 2018 for AEP. Then from January 1st to March 31st, 2019 there will be an open enrollment period. What communication plan do you have to first indicate that you would like to be the healthcare provider and then insure they are satisfied with their choice? At DDG, we segment this audience into market segments*: Consumers who wish to become a Medicare Advantage Member: 1,049,182 Individuals who prefer to purchase Medicare Supplement Insurance: 1,255,367 Persons who desire PDP (Part D) only: 937,833 Dual Eligible individuals: 488,602 Special Needs Persons (SNP): 6,621,042 Turning 65** 3,342,487 Turning 66** 3,342,487 Turning 67** 3,236,222 Movers*** 1,305,457 These audiences are very useful in AEP campaigns—direct mail and Facebook. We call these custom audiences-dataFaces. However, OEP will present a different challenge. Our market(ing) research does indicate that many consumers can not accurately describe the form of Medicare coverage they possess. Do you have a Medicare Advantage Plan? Does it include prescription drug coverage? Did you purchase a Medicare Supplement policy? Are you dependent on only Medicare Part A and Part B? Thus, it is certainly understandable that the member can become very dissatisfied with their coverage during the following months. For example, did the individual consumer grasp that the PDP selected should match the prescription drugs that they are currently using? Does your customer service groups have training that enables them to assist the consumer in making the choice that optimizes their satisfaction? Or does your member learn that they had choices from their neighbor and thus becomes irate that your firm did not explain. To measure your risk of “members switching” you can consider both a. market research and b. churn analytics. Obviously, the goal of the two exercises is different but both can provide you with keen insight for member behavior in OEP.
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 Medicare opportunities are rapidly flying past us Each month a new group of Baby Boomers qualify for Medicare. In fact, approximately 10,000 individuals per day search for information with keywords such as: “What is Medicare?” “What are the financial implications to me?” “What should frame my Medicare choices?”
Chapel Hill, NC – Dino Fire, President of Research and Analytics at datadecisions Group and Wendy Weathers, Senior Analyst in Grid Planning and Performance at Salt River Project, present a white paper at the EPRI Grid Analytics and Power Quality Conference and Exhibition 2018. The white paper is “The Power of One: Electric Grid Reliability and Its Effects on Individual Customer Satisfaction,” and explores three primary areas empirically relating residential utility customer satisfaction and electrical grid reliability.
Each year I ask my friends — colleagues who work at a healthcare-related entity — when does your Medicare marketing plan indicate that you should start evaluating the adjustments for the new year? You might be surprised how many companies are behind schedule and waiting too long to ask some of the most critical questions to set up for a successful year. As datadecisions Group only works on a few of the issues involved in offering Medicare to the senior American, I will limit my comments to those topics.
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.