Monthly Archives: August 2023

RF(M) Scoring for Offline Service Businesses

Analytics Education, Customer Experience, DataBase Marketing, Driller Q & A, 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.

Yea, I know, so much talk about digital … but does this stuff work for offline businesses? It sure does, in fact, these models were originally developed for offline – way before online was even a thing. But because of the ease of data collection, they tend to work even better online! How about for a natural healing center or an accounting practice? Sure thing! Let’s do the Drillin’ …

Q: I stumbled across your Web site some time ago and have been a regular visitor since.  I find your information very useful.  You will be pleased to know that I purchased your book (Drilling Down) and have just finished going through it.  It all sounds so easy!  Your explanations and examples were wonderful and easy to understand. 

A:   Well, thanks for the kind words.  Would you mind if I used the paragraph above as a testimonial on my web site? 

Q: Now I will attempt to put it all into practice for two businesses – a Natural Healing Centre (massage, natural medicine etc.), and an Accounting practice. 

A: The healing centre is a pretty straight-up situation; should work very well for them just as described in the book.  The accountant, as a service business with a built-in “forced” cycle (the tax year), a little more complex.  More on this below.

Q:   I have 2 questions though, if I can.

A: Sure!  The two questions below are related, so I will answer them as one.  Only one to a customer!  Just kidding…

Q1: Neither business has a Web Site, so a visit to the workplace, usually means a purchase.  I was intending to have R = last visit, and F = visits over past 12 months.  Will this work?

Q2:   Should I put a timeframe on F?  The way I see it, if I don’t, F will continue to grow for each customer as long as they are a customer.  Whereas if I put a timeframe it will give a better picture of behaviour patterns.

Continue reading RF(M) Scoring for Offline Service Businesses →

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?

Continue reading Behavior Profiling for Long Sales Cycle B2B Customers →