Steve Jobs’ quote suggests that we might do better getting a reaction to an actual product. You tell me that price is not particularly important to you, yet this one here costs too much. You claim that design is not an issue, except you love the look of the product shown. In casual discussion color does not matter, but that shade of aqua is ugly and you will not buy it.
Although Steve Jobs was speaking of product design using focus groups, we are free to apply his rule to all decontextualized research. “Show it to them” provides the context for product design when we embed the showing within a usage occasion. On the other hand, if you seek incremental improvements to current products and services, you ask about problems experienced or extensions desired in concrete situations because that is the context within which these needs arise. Of course, we end up with a lot more variables in our datasets as soon as we start asking about the details of feature preference or product usage.
For example, instead of rating the importance of color in your next purchase of a car, suppose that you are shown a color array with numerous alternatives to which many of your responses are likely to be “no” or marked “not applicable” because some colors are associated with options you are not buying. Yet, this is the context within which cars are purchased, and the manufacturer must be careful not to lose a customer when no color option is acceptable. In order to respond to the rating question, the car buyer searches memory for instances of “color problems” in the past. The manufacturer, on the other hand, is concerned about “color problems” in the future when only a handful of specific color combinations are available. Importance is simply the wrong question given the strategic issues.
Because the resulting data are high dimensional and sparse, it will be difficult to analyze with traditional multivariate techniques. This is where R makes it contribution by offering tools from machine and statistical learning designed for sparse and high dimensional data that are produced whenever we provide a context.
We find such analyses in the data from fragmented product categories, where diverse consumer segments shop within distinct distribution channels for non-overlapping products and features (e.g., music purchases by young teens and older retirees). We can turn to R packages for nonnegative matrix factorization (NMF) and matrix completion (softImpute) to exploit such fragmentation and explain the observed high-dimensional and sparse data in terms of a much smaller set of inferred benefits.
What does your car color say about you? It’s a topic discussed in the media and among friends. It is a type of collaboration among purchasers who may have never met yet find themselves in similar situations and satisfy their needs in much the same manner. A particular pattern of color preferences has meaning only because it is shared by some community. Matrix factorization reveals that hidden structure by identifying the latent benefits responsible for the observed color choices.
I may be mistaken, but I imagine that Steve Jobs might find all of this helpful.