I was looking through some customer research reports yesterday. I remember lots of graphs, with tables underneath with small numbers showing significance values. I think it was about product messages … but man, it was dull!
It struck me that customer research, and especially quantitative research, is often missing the point. Most pharma companies and many medtech companies spend decent amounts of money on market research. Often there are ongoing tracking studies to assess awareness of products or perception of product messages. But so many times these studies are set up with big sample sizes and simple questions to keep things efficient and to chase the holy grail of statistical significance—but they miss out on insights altogether. “Message A makes customers 1% more likely to use your product, and there’s a p-value to prove it!” “Compared with last time we checked, our sales team is rated 0.5 points higher than the competition on value to the customer!” Or it’s a qualitative study, and more than 200 interventional cardiologists, for example, have described exactly the same needs, but in five different languages. So what?
My plea is to ask the under-asked question: Why? For those with small children, this may be a question you hear all too often, but that natural curiosity is something we can apply much, much more often in our customer research.
To be clear, I’m not saying there is no place for quant research, large samples or stat testing. But based on what I’ve seen in medtech, I do believe that that we don’t ask why enough. And when we do, we get so much more value from our research. Here are just three examples:
- A company was struggling to gain traction with a new implantable pump device for patients with advanced liver disease. Its customer research with clinicians was overwhelmingly positive, and the cost of treatment was reduced compared with the more invasive standard of care. Hospital payers, however, were telling the company that its product was just too expensive. In a small number of follow-up interviews with payers, this cost barrier was probed more deeply: Why do you consider that price point expensive? It turned out that among other factors, there was a stigma attached to spending money on patients with alcoholic liver damage, coupled with a concern that these patients might die relatively soon after the pump was implanted. Knowing this, the company was able to develop a risk-sharing approach with a monthly pump “rental” cost model.
- Another company had a complex and expensive offering involving lots of different customer roles, services upon discharge, services in the community, promotion, nurse training, asset tracking and so on. The company wanted to reduce its cost to serve this market as it came under pressure. It commissioned a massive quant study with surgeons, administrators, nurses and discharge planners across three countries, with some quantitatively complex methodology to try to learn the value of different parts of the offering. But this methodology completely missed the point—the huge study came up with lots of numbers and complex scores with great precision but failed to answer the simple question of why accounts choose what they do. In the end, we got much more from 20 qualitative interviews and a few focus groups, and were able to recommend a value proposition and delivery model for the future.
- A company launching an innovative implantable device wanted to know what price to launch with across European countries, and was planning a large-scale quantitative study. In fact, there were a lot of variables around which patients were selected for the procedure, what level the existing DRG code was set at in each country and the specifics of the purchasing process for different types of hospital. Add to that the fact that each respondent needs to speak for an entire hospital decision process, so just asking individual opinions is woefully insufficient; we need to know not just their answers but why. ZS used a technique called QualJoint,™ which combines the choice trade-offs of a conjoint study with the real-time probing on the reasons behind those choices. This enabled the company to set a revenue-maximizing price based on a much better understanding of pricing dynamics in these markets.
Clearly there are many benefits of asking why. One of the most common reasons for not doing so is cost. Qual interviews are more expensive than quant surveys, and coding and interpreting free text responses can’t be done by a computer (well, actually it can—but that’s another blog post). In general, there’s a belief that getting to why costs more money.
When you next commission or approve some customer research, please stop and consider:
- What do I really need to know? Is proof more important than insight?
- Can I live with a smaller sample size with more why questions?
- Can I use a hybrid qual-quant approach?
Not getting to why could be costing you more than you think.