More Thoughts on Visualizing the Customer Experience

I did end up creating a version of the Matrix demonstration system I described yesterday, using a very neat tool from Business Objects called Crystal Xcelsius (www.xcelsius.com). If you’d like a look, send me an email at draab@clientxclient.com. You’ll get an interactive Matrix embedded within an Adobe pdf.

The demonstration does what I wanted, but I’m not pleased with the results. I think the problem is that it violates the central Matrix promise of displaying information on a single page. Sliding through time periods, like frames in a movie, doesn’t show relationships among different interactions at a glance. The demonstration system attempts to overcome this by showing current and future interactions in each cell. But because the future interactions could occur tomorrow or next year, this still doesn’t give a meaningful representation of the relationships among the events.

I’m toying with an alternative approach similar to the “swim lanes” frequently used to diagram business processes. We’d have to make time period an explicit dimension of the new Matrix, and let the other dimension be either channel, contact category, or both combined. (The combination could be treated by defining a column or “lane” for each contact category, and using different colored bubbles within each lane to represent different channels.) I don’t know whether I’ll have time to actually build a sample version of this and can’t quite prejudge whether it will work: it sounds like it might be too complicated to understand at a glance.

Of course, whether any solution “works” depends on the goal. Client X Client CEO Michael Hoffman was actually happy with the version I created yesterday, since only wanted to illustrate the point that it’s possible to predict what customers at one stage in their lifecycle are likely to do next. The details of timing are not important in that context.

We’ve also been discussing whether the Matrix should illustrate contacts with a single individual (presumably an ‘average’ customer or segment member) or should show contacts for a group of customers. In case that distinction isn’t clear: following a single customer might show, one store visit, one purchase and one service call, while a group of fifty customers might make fifty store visits, ten purchases and three service calls. Lifetime value results would be dramatically different in the two cases.

I’ve also toyed with a display that adjusts the contact probabilities based on the selected time period: to continue the previous example, the probability of any one customer making a service call is 3 in 50 at the time of a store visit, but 3 in 10 at the time of a purchase. Decisions made at different points in time need to reflect the information available at that time. Adjusting the probabilities in the Matrix as the time period changes would illustrate this nicely.

Note that all these different approaches could be derived from the same database of transactions classified by channel, contact type, and time period.

Obviously we can’t pursue all these paths, but it’s worth listing a few just as a reminder that there are many options and we need to consciously choose the ones that make sense for a particular situation.