Tag Archives: LifeCycle

Behavior Profiling for Long Sales Cycle B2B Customers

Customer Experience, DataBase Marketing, Driller Q & A, Marketing / Tech Interface, Marketing thru Operations, Measuring Engagement

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Topic Overview

Hi again folks, Jim Novo here.

So Jim, this customer behavior profiling / prediction is great for consumer businesses, but what happens if you’re running a long sales cycle B2B biz where buying decisions take months if not years, and may involve a dozen decision makers? Well fellow Drillers, the answer is not as complicated as you might think – it’s about where to look for the predictive behavior outside of the sale transaction. Interested? Let’s get to the Drillin’ …

Q:  I read your section about how “R” and “F” are better indicators than “M” which I agree. But for the problem I face, do you have any ideas on how I can redefine “F” for my purpose?  If not, I can always use RM, but will face the drawbacks you mentioned in the book which I think are legitimate concerns for predicting potential value. 

(Jim’s note: this Driller is referring to the modified RFM model used in the Drilling Down book.  For an overview of what he is talking about see this description of what is in the book and this outline of RFM.)

A: Just to ground this discussion, I assume you are talking about Company XXX …
(a major enterprise software company with many products. He said Yes)

You should look for R and F in other places, if “short term” prediction is what you are after  (I’ll discuss long term in a minute).  Long cycle businesses like enterprise software can be more difficult to model because the variables you are looking to do an RF scoring on are not as obvious.  The sales activity may not be particularly predictive of customer behavior because the nature of the business precludes frequency of purchase.

For example, think customer service.  Where in your organization would you see RF show up relative to customer satisfaction?  Perhaps at the call center, help desk, or “outstanding issue” logs of the implementation team?  There could certainly be other areas, depending on how customer care is set up.  The question is: how does the Recency and Frequency of customer care predict the likelihood of customer defection?

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Segmentation by LTD & LifeCycle

Customer Experience, DataBase Marketing, Driller Q & A, Measuring Engagement, Newsletters

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Q: One of the first things I am doing in my new job is to identify the Customer Lifecycle pattern – how many periods (month, year) will it be before a customer is likely buy again.  In enterprise software industry, where software cost easily 6 figures, # of years is a reasonable time frame.

A: Yes, one would assume this.  But these notions would most likely be based on a feeling of the “average” behavior, and on average, it probably does take a long time.

What is not known is this:  if the “average” is composed of short-cycle and long-cycle buyers, who are the short cycle buyers, and what are they like?  What industry SIC code, for example?  And can we get more of them, or at least focus more resources on them, if they are the most profitable?  So the challenge is not only to look for the “average”, but then understand how this average is composed.  If you can break down the average by industry, or by salesperson, for example, this might be highly directional information.

Q: From my internal analysis, however, I discerned from the sales figures something quite counterintuitive – the period between first and next sale is much shorter than I would have thought for the SW industry in general.

Continue reading Segmentation by LTD & LifeCycle →