Using a MaxDiff Analysis to Make Decisions
The changing healthcare environment has created confusion among consumers. Our client, a health-insurance provider, wanted to know how best to communicate with their customers about the Affordable Care Act and what messages they need to use. Decision Analyst conducted a MaxDiff analysis on the proposed message statements to determine which messages consumers preferred.
The Affordable Care Act (ACA), also known as Obamacare, is a United States federal statute signed into law by President Barack Obama on March 23, 2010. Together with the Health Care and Education Reconciliation Act, it represents the most significant government expansion and regulatory overhaul of the U.S. healthcare system since the passage of Medicare and Medicaid in 1965. The passage of these acts has brought confusion among healthcare consumers. Our client, a health-insurance provider, was interested in learning what insurance consumers thought of the ACA and how they needed to position their messaging in light of it. The health-insurance company wanted to explore various messaging elements related to the company and the healthcare act to determine which would be most important and compelling to consumers. Decision Analyst suggested testing the messages and evaluating them via a MaxDiff analysis.
The primary research objective for this project was to understand which new positioning statements concerning the new healthcare act were most compelling to consumers and warranted further development. Specifically:
- Which message concept would be liked the best and why?
- How would consumers rate the messages for believability, relevance, fit with brand, etc.?
- Determine awareness and attitudes about the ACA.
Research Design and Methods
A total of 400 Internet interviews were completed using the American Consumer Opinion® online panel. Respondents were required to be at least 18 years old, reside in our client’s geographic sales region, and have health insurance.
In addition to being asked specific questions about the ACA, respondents were also asked to rate 10 messages the client was considering.
It was determined that in order to get the information the client needed, while keeping the survey simple for a respondent to complete, a MaxDiff analysis would be conducted. A MaxDiff analysis offers the following key benefits:
- The survey task forces respondents to make a discriminating choice about which statement is the most important and which the least important to them; there is no possibility to encounter scale bias (e.g., some respondents are high raters or low raters for all attributes).
- The most-least survey task is less difficult for the respondent to do than a full sort and rank, but still produces a full ranking of all statements for each respondent.
For this MaxDiff exercise, the 10 messages the client wanted to evaluate were grouped into 30 sets of three messages each. So each respondent didn’t have to rate all 30 sets, the 30 sets were divided into three groups of 10 sets. Each respondent was randomly assigned to a group and was asked to select the message statement that was most important in their decision-making and the statement that was least important in their decision-making.
After the MaxDiff exercise each respondent rated each message for believability, relevance, fit with brand, etc.
After the data collection was completed, a Hierarchical Bayes (HB) choice model was run to produce unique statement importance scores for each individual respondent. Then, based on the individual respondent results, a relative preference score for each message statement was calculated.
As a result of the data collection and MaxDiff analysis, our client was able to obtain a better understanding of the health-insurance consumers’ awareness, knowledge, and concerns about the Affordable Care Act. The results of the MaxDiff analysis pointed our client towards the messages that were most compelling to consumers in their region and warranted further development.
Concept Testing Services
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