Category Archives: Measuring Engagement

New RFM: Using RF or RM Instead of RFM

Analytical Culture, Customer Experience, DataBase Marketing, Driller Q & A, Marketing thru Operations, Measuring Engagement

Jim answers questions from fellow Drillers
(More questions with answers gathered together here)

Topic Overview

Hi again folks, Jim Novo here.

Sometimes the traditional RFM model does not work very well for a specific business model. For example, small business databases can be too small to fill out all 125 RFM segments properly, resuslting in distortions of predictive capability. Optimizing the traditional RFM approach for unique business model criteria is a very useful skill, and it’s actually not difficult if you understand the levers of the business model. To Drill or not to Drill, that is the question …

Q:  I’ve used your site a lot and found it to be very informative.

A: Thanks for the kind words!

Q:  I have a question about the use of RFM analysis for a low margin, eCommerce business.  I read that for a relatively small customer list (<50k) using just the “RF” of the RFM analysis would be preferred since the “M” tends to hide shifts in behavior.

A:  Well, the M tends to smooth shifts regardless of the size of your list.  In addition, if you have a small list, 125 segments is too many to be really useful, so RF at 25 segments in more intuitive.  The real issue with M or Monetary Value is up and coming, accelerating customers.  If you use total spend (M), it will “punish” them with a lower rank.  But the fact is they have more future potential because Recency is low and Frequency is ramping.  Inversely, M tends to reward customers who have spent a lot in the past with a higher rank, though they may actually be declining or defected customers.  Predicting the future is more profitable than reporting on the past, so given a choice, I would drop “M.”  This is especially true on the web, where communication costs are low and changes in behavior can be very rapid.

Q:  My question to you is, since I’m talking about a low margin business, wouldn’t “M” actually be more valuable than “F” for the analysis?  For example, if 40% of my customers are driving 70% of my sales and 100% of my profits, that says that 60% of my customer base is losing me money.  I don’t want them to be given a higher value rating because they’re placing MORE unprofitable orders than someone placing fewer but profitable orders.  You see what I’m saying???

Continue reading New RFM: Using RF or RM Instead of RFM →

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 gathered together 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 gathered together 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? →