By Joel Cadwell
We have been talking about design thinking in marketing since Tim Brown’s Harvard Business Review article in 2008. It might be easy for the data scientist to dismiss the approach as merely a type of brainstorming for new products or services. Yet, design issues do arise in data visualization where we are concerned with communicating our findings. However, my interest is model selection: Should the analyst select one statistical model over another because the user might find it more helpful in planning interventions or designing new products and services?
For example, the marketing manager who wants to retain current customers seeks guidance from customer satisfaction questionnaires filled with performance ratings and intentions to recommend or purchase again. Motivated by the desire to keep it simple, common practice tends to focus attention on only the most important “causes” of customer retention. As I noted in my first post, Network Visualization of Key Driver Analysis, a more complete picture can be revealed by a correlation graph displaying all the interconnections among all the ratings. The edges or links are colored green or red so that we know if the relationship is positive or negative. The thickest of the path indicates the strength of the correlation. But correlations measure total effects, both those that are direct and those obtained through associations with other ratings.
The designer of intervention strategies aimed at preventing churn could acquire additional insights from the partial correlation graph depicting the effects between all pairs of ratings controlling for all the other ratings in the model. While the correlation map reveals total effects, the partial correlation map removes all but the direct effects. The graph below was created using the R code from my first post to simulate a data set that mimics what is often found when airline passengers complete satisfaction …read more
Source:: r-bloggers.com