Consultants love 2x2 matrices. So in organizing my thoughts on the topic of lifetime value modeling, it’s natural that I ended up building one.
The question I’m wrestling with is, just how detailed must a lifetime value model must be to be useful? This is raised by my claim last Friday (here) that lifetime value is the essential measure needed to manage customer experience. My logic in a nutshell: the only way to judge whether an experience change is working is whether it improves lifetime value. Nothing else really counts.
You may or may not agree, but let’s assume that’s true for sake of discussion. The question then becomes, what does it take to build a lifetime value model that’s adequate for the purpose? Actually, the answer is obvious: the model must be able to estimate the impact of any experience change on final lifetime value. But this just leads to the equally obvious observation that “any” experience is too broad a goal, and what we really need is make tough choices about which experiences to include or exclude.
Now, being a consultant, I don’t make tough choices unless someone pays me a lot of money. But 2x2 matrices? Those you can have for free.
So let’s think about lifetime value models in two dimensions. The first is complexity. This ranges from simple to, um, complex. A simple model would be something you could do with math functions in a spreadsheet. A complex model involves polynomial formulas and multi-variate regression and such. It can incorporate many more factors than a simple model and allows for subtle relationships among them. In practical terms, a simple model is something a business analyst can create while a complex model needs a statistician.
The second dimension is scope. This indicates which experiences are included in the model and ranges from partial to full. A partial model might include only one experience such as acquisition or renewal, while a full model would include all experiences from prospecting through product use to customer service. In general, experiences map to business functions (marketing, sales, service, operations, etc.) which in turn map to departments. So even though what we really care about is experiences, we can think of a partial model as dealing with activities in one or several company departments, while a full-scope model deals with all departments. This departmental orientation makes sense because the input data will usually be held in departmental systems. So expanding the scope of a model will usually be done on a department-by-department basis.
Now we have a nice 2x2 matrix, with four types of models: simple/partial, simple/full, complex/partial and complex/full. What use can you make of each type? I think I’ll save the answer for tomorrow.
Monday, January 15, 2007
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