The notional company Blue Bikes is a ride-sharing company with 80 thousand customers.
The competitive nature of the industry demands they segment and engage their customers in
a unique manner.
But creating a set of actionable customer personas is tough.
Too many personas is not practical.
Too few is not effective.
The problem is that the method of choice to define these personas, clustering, is too
complex.
Enter interpretable clustering.
First, leverage traditional clustering techniques to segment the 80 thousand rider base.
Second, leverage an interpretable predictive algorithm to predict the cluster of each rider
to provide insight and definition to each cluster.
Third, assess the new model.
We found five clusters that were both granular and accessible: female commuter riders, short
rides, weekend riders, male commuter riders, and long rides.
With these clusters, Blue Bikes can delight their customers.