Quantitative Research

MME believes that it is the interaction of patient, physician, pharmacist, and other healthcare professionals that results in the ultimate response to the value of product offerings and their costs. Developing research that models these effects and predicts market-wide behavior is the foundation of MME’s quantitative pricing research.

Sound research is designed to answer specific questions based on previous work and understanding, not by employing the same method for every research task. Grounded in this principle, MME conducts quantitative market and pricing research that is designed to answer the unique strategic and tactical questions for your product or service. MME uses quantitative research to identify and predict the behavior of specific segments of providers, payers, patients, and other participants to define and characterize the response of market decision makers to various factors (including price); to understand patient flow through the health care system; and to determine decision-maker responses to promotion, product, and price differences.

Monadic Sampling

At MME we are proponents of monadic research designs. Unlike most approaches to commercial market research, monadic designs avoid the problems of “anchoring” and “framing,” that result in biases being built into the research, biases that find sensitivities to price, reimbursement, or product differences that may not be real.

Most research approaches expose respondents to several different levels of price, reimbursement, or product differences and ask how these differences will affect decisions – effectively forcing respondents to declare differences. In a monadic research design, the total sample is divided into as many subsamples as there are different options being tested, with each group being exposed only to a single price, reimbursement level, or product profile. Any differences in response among the groups can be attributed to differences among those options. The term monadic research refers to a sampling and design method rather than a statistical approach. Similar in design to a traditional clinical trial, each group is exposed to a single “treatment.” The “treatment” in this case could be a single price, co-pay, or a complete description of the product, including attributes and positioning statements.

Although we can, and do, employ other methods, when appropriate, we discourage the overuse of the traditional “one size fits all” conjoint or choice models because research has shown that, while the results are usually precise, they are often precisely wrong.