This is the sixth in a series of findings from Data Decisions Group’s 2022 Medicare Preferences Study. We explore the differences in importance Medicare Advantage and Medicare Supplement members place on attributes of their respective plans. While important to members of both plan types, access to specific providers and network resources is, by far, the main reason Medicare Supplement members choose the plans they do. Among 19 different plan attributes and features, more than 1 in 5 Medicare Supplement members identify their network coverage as the most important one. Among Medicare Advantage members, access to their preferred providers is one of three important attributes; prescription drug coverage and add-on benefits are nearly equal to network coverage in terms of importance for these consumers. Since network options are generally more limited with Medicare Advantage plans, it’s apparent that members are willing to trade off any HMO-like restrictions associated with their plans for the additional, low or no-cost benefits these plans offer, such as vision coverage, dental insurance, and, especially, prescription drug coverage. In most situations Medicare Supplement plans, unlike Medicare Advantage plans, offer these features only with additional premium costs. Medicare Supplement providers should recognize that there are a couple of unambiguous motivators for many consumers. First, access to their preferred doctors and specialists is of paramount importance, and second (related to the first) members need to believe that the premiums they pay are worth the value of that network access. A word about the methodology for this part of the research: The shares of importance for plan features were determined through a choice exercise known as Maximum Differential Scaling, or MaxDiff. In this exercise, respondents are asked to choose the most important and least important attribute from a short list of all the possible attributes. This choice exercise is repeated many times, with respondents seeing each of the 19 attributes several times, but in different combinations. All of the differences between Medicare Advantage and Medicare Supplement members in the chart above are statistically significant at the 95% confidence level. Data Decisions Group conducted the 2022 Medicare Preferences Study, a large, nationwide study of Medicare Advantage and Medicare Supplement alternatives among 2,324 current Medicare-qualified consumers and 64-year-olds who will become eligible soon. The online study was fielded between March 28 and April 11, 2022. The margin of error for this study is approximately ± 1.5%. For more information about the research contact
This is the fifth in a series of findings of Data Decisions Group’s 2022 Medicare Preferences Study. Here, we review brand loyalty metrics for providers of Medicare Supplement coverage.
This is the fourth in a series of findings of Data Decisions Group’s 2022 Medicare Preferences Study. Here, we review brand loyalty metrics for providers of Medicare Advantage coverage. In Article #5, we’ll look at the same information among the major providers of Medicare Supplement plans.
This is the third in a series of findings of Data Decisions Group’s 2022 Medicare Preferences Study. Here, we review brand loyalty metrics for the category overall. In Article #4, we’ll look at the same information among the major providers individually.
This is the second in a series of findings of Data Decisions Group’s 2022 Medicare Preferences Study. In Article #1, we discussed the differences between age-ins and current plan members when it comes to determining the importance of Medicare Advantage and Medicare Supplement (Medigap) plan features.
DDG’s Medicare Options Consumer Key Drivers Study quantifies the reasons Medicare-eligible (as well as those soon to be eligible, age-ins) consumers make the decisions they do regarding the Medicare options available to them.
As of this writing (from my home office, of course), over 5 million people in the United States have contracted the novel coronavirus known as COVID-19. After a period of reopening businesses across the country, climbing case rates have driven many states to begin closing back down again. Almost seven months after the first U.S. case was reported, there are still no clear indicators of when life will return to normal.
Until (relatively) recently, electric utilities pretty much had a single mission: the generation, transmission, and monetization of electrons. It was a simple business model. The customer flips a switch, or claps their hands, or yells across the room to Alexa to turn on the lights, and the fulfillment process happens, literally at the speed of light. For many years, that was enough. But what happened before is not what happens today. Instead, we live in an economic marketplace where cable service providers provide home security, phone companies provide video entertainment options, drivers with some spare time provide rides from the airport, and Amazon provides, well, everything. Utility companies learned that within a fairly narrow space, they too can compete for share of consumers’ share of wallets for products that most consumers would have traditionally sought elsewhere. The “narrow space,” at least today, tends to center on either 1) products and services directly related to the home, such as things like home warranties, appliance repair and maintenance, home management and efficiency systems, and yes, home security; or 2) optional, voluntary programs like community solar. Configuring and ultimately pricing these types of products is a well-established, tried and true exercise in product development research. Got ballpark pricing questions? Use Van Westendorp Price Elasticity research. Want to see what consumers will trade off for lower prices? Use conjoint analysis. Want to offer complex pricing models based on tons of optional add-ons and choices? Use adaptive conjoint. Want to pick a winner among some product finalists? Use a monadic experimental design. The limitation with all these methods is that they generally yield relative consumer adoption estimates, not absolute ones. Relative metrics reliably show which product will outperform the others but doesn’t help much in populating that top line in the product’s pro forma. And obviously, it helps to know how many people will sign up for a community solar initiative—and at what price—before a utility can reasonably estimate their investments in hardware, marketing, insurance, rolling trucks, regulatory compliance, and, importantly, impact on their brand. In research for consumer package goods, the transformation from relative adoption estimates to hard forecasts of adoption and dollars has relied on mountains of historical data that model specific products in specific categories (e.g. a new flavor of potato chips in the salty snacks category). For new concepts like community solar initiatives, however, this history doesn’t broadly exist. Fortunately, many utilities do have historical adoption rates for many analogous, if antiquated, products. In the final, monadic stage of product development, a key question is about purchase intent. Generally speaking, the concept with the highest PI wins. Producing a forecast from these outputs, while far from an elementary exercise, is founded on best-practice modeling and marketing science. Adoption of some original products can be treated as dependent variables is a logistic model, with things like household demographics, marketing outreach, seasonal factors, and other characteristics as predictors to that outcome. A similar process can then be applied to the “new product,” using an outcome of “definitely would purchase” as the binomial target result and the current values (or, in the case of marketing effort, estimated values) along with their previous coefficients as the predictors. The final data science application involves scaling the model’s results to the adoption levels of previous products. In a nutshell, the modeling application for these utility applications is similar to the large-scale efforts applied in CPG and other sectors, except that the process tends to be unique to each utility, based on their history, customers, and many other factors. This fact doesn’t allow for an easy, out-of-the-box solution, but it does allow for a scientific, empirical, and defensible approach to forecasting new product sales.