For example, a group of data analysts I know were working with a direct mail retailer and were attempting to determine why some of the retailer's catalogs generated more sales than others, even though the contents of the catalogs were very similar. One of the analysts noticed differences in the visual appeal of each catalog. As it turned out, different production crews were employed on different catalogs and each had its own particular visual style. After subjectively ranking the catalogs by visual appeal and entering the ranking as an additional field in their database, the analysts discovered the ranking was correlated with the variations in sales figures. The ranking had predictive value.
Subjective information is important for predicting human behavior.
However, knowing that subjective information is important does not answer the question as to which subjective aspects should be represented. As mentioned, machine-learning techniques can help answer this question, but to use learning techniques for this purpose one must first represent all subjective aspects that have potential predictive value. To decide which subjective aspects to represent, one needs to think about the different kinds of distinctions drawn among the various ways of presenting information that might provide a basis for distinguishing one type of user from another. Imagine how each site might look and feel. Clearly, a single data entry specifying the category of music would fail to capture the richness of the differences one would expect to find among the four Web sites.