Grass Roots Data vs. Formal Data

I had the pleasure of presenting today at the A.C. Nielsen market research conference at the University of Wisconsin in Madison. Along with one of our consumer product company clients, we presented an overview of prediction markets, how they are being used in companies, and the results of some experimentation we did with them. Good questions during our talk, good conversations with academics and market research practitioners. A productive day all around.

What I wanted to focus this post on however was a moment during the first plenary session I found quite interesting:

The session was led by Pete Rose from Yankelovich who reviewed the major findings of their Monitor report, the " longest-running, most in-depth study of consumer value and lifestyle trends." It was fascinating to sit in a room of market research practitioners and students as they clamored for new information and guidance on how they should interpret the latest U.S. financial crunch in the context of how to market their products. In fact at the conclusion of Pete's excellent presentation, the first questioner asked how the research data we had just been presented changes because of the events of the last two weeks on Wall Street and in Congress. In other words, if I'm a market researcher at a luxury hotel or fancy car company, should I be quaking in my boots? And vice versa, if I'm a market researcher at a budget hotel company, should I be ramping up the marketing budget?

In response, Pete, as a market researcher firmly planted in an empirical research methodology, gave a vague answer and told everyone to wait for their next report to come out for more details. No problem, but wearing my prediction market hat I had a simple thought: why not augment this traditional methodology with a constant stream of questions posed in a prediction market? Grass roots data vs. formal data. We talk about this all the time in other contexts, but for market researchers, is such an approach blasphemy?

Exploring this thought further, let's say we did work for a budget hotel company. We could first identify what the levers are that drive our business. Business travel patterns. Gas prices. Stock market performance. Competitive activity. Then we could recruit people who have insight in to these factors and ask them to participate in an ongoing prediction marketplace. These aren't necessarily just hotel goers, they could be economists, wall street traders, bankers, and professionals from the energy industry. We could run a survey with this same group of people, but how would we know to run it until after the fact? We would have to be conducting surveys on a weekly basis - an expensive proposition. In contrast, a prediction market could be running 24/7 and serve as the early warning indicator that allows our hotel company to be more proactive in light of the changing levers that positively or negatively affect our business. We could even ask this group about the performance metrics we care about directly: "What will our occupancy rate be in Q3?" Even if the market prices didn't provide a clear signal of change we should be aware of, we may see outliers among the traders or be able to gather qualitative data in the discussions accompanying the questions, leading us to follow up with more detailed research or additional questions in the marketplace.

Formal market research reports will always have their value, but why not supplement them with ongoing mechanisms like prediction markets. They may be unconventional but could also serve as a more timely indicator of what's coming down the road, giving us precious time to act.