Category Archives: Measuring Engagement

New RFM: Snapshots versus Movies of Behavior

Analytical Culture, Analytics Education, 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.

The standard RFM customer model is essentially a “snapshot” of likelihood to purchase (and so perhaps also profitability of campaign) at a given point in time. But what if you took these snapshots and turned them into a movie, looking at likelihood to purchase over time, and what specific inputs affected these changes? Then you’d have a LifeCycle movie / model, which amplifies the power of RFM substantially. Ready to find out more? The Drillin’, the Drillin’ …

Q:  I am in catalog circulation.  We currently use RFM to segment the file and then roll the RFM cells into more manageable segments (this is a new technique to me, I am new to this company, in my former company we mailed by RFM segment).

A:  Hmm…this sounds like a “dumbing down” approach to RFM, but hey, if it works, why not.  Sometimes this is done because the customer base is not really large enough to support 125 segments, and the differences between the segments can become unstable and less predictive unless they are aggregated.

Q:  Because we are in a niche market and we saturate it pretty well, I would like to see which customers are on the edge or falling off (the Latency stuff) and which ones we can “reward” for being the best.  I do not think the RFM analysis shows me that amount of detail.

Continue reading New RFM: Snapshots versus Movies of Behavior →

Are Quitters of Club Likely Still Good Customers?

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

How do you handle the measurement of “likely to purchase” when there’s a built in cycle of purchase as a “member”, like in a book club or other auto-delivery scheme? And what if a member quits membership but keeps buying, what does that mean for predicting future buyer behavior? Oh, the complexity of it all! Let’s do the Drillin’ …

Q:  I just ordered the book too, so I am eager to learn more about SIMPLE ways to implement RFM-based strategies.

A:  Well, thank you for ordering!  I hope it fulfills your expectations.

Q:  In the continuity club (Jim’s Note: flower of the month, book of the month, beer of the month) club business though, a little of the RFM process looks tricky because everyone has a certain Frequency built-in, because of the “repeat” nature of clubs.  Also, we’re starting to see a  phenomenon where customers that drop out of our club continue to order from us.

A:  This is quite normal, depending on how the club is set up and whether or not you make it “easy” for people to continue.  In some clubs, you are either in or not (books, CD’s, credit cards).  Most catalog-type clubs (pay a fee in exchange for ongoing discounts / added services) see continuation beyond club membership.  It’s a volume-based thing and a “rational” decision by the consumer – if you need to buy a lot of stuff, joining the club makes sense, because the discount pays for the membership.  

In your case, it might be more attached to education, for example – you join the club to educate yourself about the products, then quit when you can “do it on your own.”  Or, you get lots of  product to experience the variety, and settle into a specific usage pattern.  This is the Customer LifeCycle at work.  If you can recognize these patterns, you can use them to predict what customers are likely to do next.  If you can predict behavior, you can create very high ROI customer marketing programs.

Continue reading Are Quitters of Club Likely Still Good Customers? →

Retention and Defection Scoring in Travel

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

What happens when your business doesn’t really fit the mold of traditional customer scoring that retail and similar businesses use? Well, you can certainly come up with a version of the tradional models that is customized for your business, as we do below for a fellow Driller’s travel agency. Wanna see how that’s done? Then, to the Drillin’ …

Q:  Just read your book and I say full marks for such a practical and sensible approach!!! Start small and grow is the way to go. 

A:  Well, thanks for the kind words!

Q:  I am a part owner of a travel agency (not been the best area to be in during pandemic).

A:  Eeeeek!

Q:  My first focus for Drilling Down is on our leisure customers.  But my head is spinning a bit with all the ideas I have from your book.  I can electronically access our: customer names etc., an ID number, when they purchased, how much the product cost, the supplier, the category (i.e. air only, cruise, tour etc.) and the final destination.  If you would be so kind as to give me a little steer in the right direction  in setting up the metrics and scores.

A:  Hmmm…  I of course don’t know your business but would think that particularly in leisure, there is a natural cyclicality caused by vacation timing, anniversary events, and such.  So in terms of timing, you use a classic Latency approach, e.g. if a customer took a trip last July they are somewhat likely to take one this July.  If they took one last July AND the July before, they are very likely to take one this July.  If they have taken a trip the last 5 July’s in a row, they are extremely likely to take one this July.  

So you can rank customers by likelihood to travel each month, and if you want, could assign them a “score” to represent this likelihood, in the case above, extremely likely = 5, very likely = 4, etc.  People who have not booked with you for a year might be a 2, not for 2 years a 1. 

Continue reading Retention and Defection Scoring in Travel →