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.
Friday, February 23, 2007
Subscribe to:
Post Comments (Atom)
2 comments:
David, I will take the bait and add a little controversy to your LTV discussion. It seems to me you are proposing a system that tracks what the customer does and then propose to use that information to change or reinforce the treatment of that customer. I like the idea that you are trying connect your actions to that of the customer. This is a big step forward from companies that define what they thing is a customer experience and then act as if that’s all there is to it.
I think the starting point for any measurement should be the end-game of customer experiences. In my opinion this is building customer equity. One aspect of customer equity is the advocacy dimension captured by instruments like netpromoter. But at least as important, if not more important, is the shift in the focus of the relationship from buying “things” to a focus on “issues or outcomes”. Underlying this shift is a dramatic shift in trust. The shift is from sufficient trust to make a transaction and buy a thing, to leap-of-faith trust that enables future-oriented change. The former is competitive the latter is collaboration and cooperative.
This building of customer equity is a journey, not an event. The customer experiences are the vehicle that underlies the shift. The journey should not just be a series of delightful experiences, rather it should be one where the experiences engage the customer, build desire and increasingly shift them from a win-lose to win-win relationship with the company. Since the process is deliberate, measurements along the way can be used to determine whether progress is being made and to determine the next step. John I. Todor, Ph.D., author of Addicted Customers: How to Get Them Hooked on Your Company. (www.AddictedCustomers.com)
Thanks for your comment John. I'm not sure I understand you correctly but think you are suggesting that measurements should capture more than just the financial events of LTV to include other events that indicate development of customer equity. That sounds good to me, and I presume Addicted Customers tells how to do it? I would argue that, in theory, an LTV model should be able to measure the impact of those events on LTV, but do concede this is easier said than done.
Post a Comment