Why Customer Experience Management Depends on Metadata

I’ve written a great deal recently about the importance of Lifetime Value as a measure to guide customer experience management. Let’s assume I’ve made my case, or at least no one has any interest in arguing about it. The next question would be, what blocks companies from making Lifetime Value calculations?

In one sense, the answer is nothing. You can generate a crude LTV figure with nothing more than annual profit per customer and attrition rate. But that type of calculation isn’t precise enough to measure the effect of changing a particular customer treatment. If we want to use LTV as a customer experience metric, we need a LTV calculation that works at the level of customer experiences.

This calculation has two components: the model and the data used as input. Designing the model takes some skill but isn’t that hard for people who do such things. The real challenge is assembling the experience data itself.

Some of this data just isn’t directly available. Experiences such as cash purchases at retail, anonymous Web site visits, and viewing of TV advertisements can’t be linked to individual customers. They must either be inferred or left out of the model altogether.

But in many industries today, the majority of significant experiences are captured in a computer system. The information might be in structured data such as a purchase record, or it might be something less structured such as an email message or Web page. Taking these together, I think most businesses capture enough experience data to build a detailed LTV model.

This data must be processed before it is usable. The processing involves three basic tasks: extracting the data from the source systems; linking records that belong to the same customer; and classifying the data so it can be used in a model.

None of these tasks is trivial. But the first two are well understood problems with a long history of effort at solving them. In comparison, classification for LTV models has received relatively little attention. This is simply because the models themselves have not been a priority. Other types of classification—say, for regulatory compliance or fraud detection—are quite common.

The classification issue boils down to tagging. Each experience record must be assigned attributes that fit into the LTV model input categories. At Client X Client, we do this in terms of the Customer Experience Matrix. The Matrix has channel and life stage as its two primary dimensions, although the underlying structure also includes locations, systems, slots, products, offers, messages, customer, and context. Tagging each event with these attributes lets us build the Lifetime Value model and make other analyses to understand and optimize the customer experience. (Incidentally, although I’ve used the terms classification and tagging here, you can also think of this as application of metadata.)

My point is that while tagging may seem a trivial technical issue, it is actually a critical missing link in the chain of customer experience management success. And that’s why I just spent 500 words writing about it.

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