In his January 2012 article in Business Week, entitled “For Successful Innovation, Sell Imperfect Products” author Larry Popelka makes the case for getting a minimum viable product (MVP) to market quickly and then improving it based on the reactions of users and prospective buyers.
Traditional market research—using focus groups, surveys, or other primary data sources to test concepts before they hit the market—can be seen as either a help or a hindrance to the MVP concept. Popelka quotes Steve Jobs, “It’s hard to design products by focus groups. People don’t know what they want until you show it to them”. In many product development organizations, new concepts are market-researched to the nth degree before being brought to market. And it is true that market research results can kill good ideas because, sometimes, people don’t know what they want until you show it to them.
While all of these problems exist, market research can still play a critical role in getting new offerings to market more quickly and cost effectively. Here are two recommendations based on our experience at Isurus:
Research the problem, not the solution. It is certainly true that focus group participants are not going to tell you, verbatim, how to solve their problem. But market research can give the product team a very good idea of the market’s unmet needs and the problems that need solving, and these are some of the key building blocks for meaningful innovation. Use research early in the process to understand those existing needs and pains, and then innovate around those.
Listen effectively to early user’s feedback. The key to the MVP approach is listening to user feedback, and then applying that feedback to improve the product. Effective listening is not a one-size fits all approach. It requires asking the right questions, hearing the feedback in an objective and unbiased way, and making sure the feedback comes from a range of user perspectives (and not just a few really vocal or articulate users). Market research is designed to do all of these things. Although it may slow the process down somewhat, it saves time in the long run by improving the likelihood that the next iteration maximizes its potential to improve on the previous one.