Case Studies

Our client asked us to take a look at the survey instrument for a segmentation study. Like so many that we see, it was a laundry list of agree/disagree statements, which are known to be poor measures with a strong positive bias. We rewrote the questionnaire with different scales, and used forced-choice exercises to make respondents choose between benefits. This allowed the company to understand each segment better and to focus on its two most profitable segments.

The outcome was one of “the most stellar segmentations” our client had ever seen.

We used a mixed-mode approach with online activities and interviews to understand the patient experience in a rare disease. We presented our results to a consortium of patient advocacy, and the response was overwhelming. They said that this was “the first time a company has gotten it exactly right,” and that “this was the patient experience.”

Further, we helped our client define key segments so that it could develop plans to help each segment get better treatments.

A health sciences client needed to submit a plan to the FDA for a clinical trial. They had a questionnaire that patients were going to take that would tell them if the treatment worked, or not. In concert with FDA guidelines, we conducted cognitive interviews with patients to see if the questions worked as intended. Of course, they didn’t! No one had tested these questions with patients. We found the question stems were ambiguous and misleading, and that the scales did not allow patients to represent how they felt on just about every symptom. We redesigned the patient survey instrument and delivered the report and transcripts to the company to submit to the FDA. Bottom line? They are now in a position to accurately measure patient experience and in a much better position to get approval for their treatment.

Our client was using one of those tracking studies that the big marketing research firms sell. It took respondents 45 minutes to complete. The data was inaccurate. We rewrote the tracking study to accurately measure the metrics that really mattered to strategy. Bottom line? We reduced the cost of the study by over 60 percent! And the key metrics of interest? Their inflated measures of a particular type of usage were now accurate, and this had a dramatic effect on strategy. Why not get accurate information at a lesser cost?