Category Archives: Measuring Engagement

New RFM: Customer Retention in “Subscription” Businesses

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

Jim answers questions from fellow Drillers

Topic Overview

Hi again folks, Jim Novo here.

How do you measure likelihood of customer defection when purchase behavior is highly orchestrated or executed due to repetitive billings? Yea, it’s a bit more complicated because “orders” really can’t express any kind of behavioral change, can they? So, you have to find indicators other than sales to provide the triggers. The Drillin’ the Drillin’ …

Q:  Jim, first let me say that I am enjoying your book VERY MUCH!!  Nicely done, and a nice job of integrating it with the CRM paradigm, 1-to-1 etc… I’m reading very slowly and finished the Latency Metric Toolkit.

A:  Great!  Thanks for the kind words.

Q:  I had a couple of questions on the Latency toolkit and the Latency tripwire, especially as it applies to environments with built in cycles for repeat purchases.

I am in a business where our resources are quarterly based, i.e. customers purchase our resource use them for a quarter and re-purchase the next quarter’s resource.  That is, we have a built in pattern, where customers would purchase our resources each quarter.  I was wondering how well I can use Latency with this type of built in cycle or if I would have any problems applying your Latency concepts to it, maybe they apply that much more readily?   In our case we try to call most folks who haven’t purchased within 2 weeks of a new quarter beginning.

A:  Right, a subscription-type business.  This is also an issue with utilities and other like businesses who bill about the same amount each month or have contracts for service (like wireless).  The answer is if the revenue generation really doesn’t represent anything to do with the behavior, then you simply look for other parameters to profile.  For example, a friend of mine was responsible for analyzing the likelihood of subscription renewal in a business that provided the content online.   Increasing Latency of visit was a warning flag for pending defection, and they triggered their most profitable campaigns based on last visit Recency.  In wireless, the correlations are found in payment Latency and age of phone.

Continue reading New RFM: Customer Retention in “Subscription” Businesses →

New RFM: Using RFM to Improve Email Profit

Analytical Culture, DataBase Marketing, Digital Analytics, Driller Q & A, Marketing thru Operations, Measuring Engagement

Jim answers questions from fellow Drillers

Topic Overview

Hi again folks, Jim Novo here.

Traditional RFM execution is focused on giving a snapshot view of customer likelihood to respond / campaign profitability across large and varied customer databases. But is that all it can be used for? Heck no! If you understand the basics of how and why RFM works, and you understand your customer database, there’s a ton of different very valuable customer scoring operations you can accomplish. Interested? Get out the Drillin’ tools …

Q: I recently purchase your book “Drilling Down.” Really enjoying reading it!

A: Well, thanks for the kind words!

Q: I had a question about the implementation of the RFM model against email campaigns. Say we have a client that has done this:

  • Sent out 2 emails to entire database – in June and July
  • Sent out 3 targeted emails to a specific segment of database – in June, July and Aug

From my CTR and Open Rates I know that the targeted segment performance is better. For my scoring I am using the following:

  • Recency, last email responded to, and
  • Frequency, number of emails where an action (a click-thru) was taken

So the question is when trying to apply an Recency / Frequency RF score to the entire database, do you / can you use all 5 email programs? Would Recency include the email to the specific segment in August? Would frequency include the segment that received the email in August?

A:  The fact you are asking this question tells me you understand the methods better than you think you do.  The correct answer is yes, and no, depending on the objective of the scoring. As long as you **understand** that there is the potential for the marketing to the target segment to skew the scoring of the overall group, then you are thinking about the problem correctly.  Whether you decide to do the scoring as “everybody” or you score the targeted segment and then score “everybody else” separately really depends on what you are trying to accomplish / the objective of the effort.

Continue reading New RFM: Using RFM to Improve Email Profit →

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

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 →