I’ve been working with pharma brand tracking data, used to calibrate a part of an integrated model of prescriptions in a disease class. Understanding docs’ perceptions of drugs is pretty important, because it’s the major driver of rx. Drug companies spend a lot of money collecting this data; vendors work hard to collect it by conducting quarterly interviews with doctors in a variety of specialties.
Unfortunately, most of the data is poorly targeted for dynamic modeling. It seems to be collected to track and guide ad messaging, but that leads to turbulence that prevents drawing any long term conclusions from the data. That’s likely to lead to reactive decision making. Here’s how to minimize strategic information content:
- Ask a zillion questions. Be sure that interviewees have thorough decision fatigue by the time you get to anything important.
- Ask numerical questions that require recall of facts no one can remember (how many patients did you treat with X in the last 3 months?).
- Change the questions as often as possible, to ensure that you never revisit the same topic twice. (Consistency is so 2015.)
- Don’t document those changes.
- Avoid cardinal scales. Use vague nominal categories wherever possible. Don’t waste time documenting those categories.
- Keep the sample small, but report results in lots of segments.
- Confidence bounds? Bah! Never show weakness.
- Archive the data in PowerPoint.
On the other hand, please don’t! A few consistent, well-quantified questions are pure gold if you want to untangle causality that plays out over more than a quarter.