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RFM versus LifeCycle Grids

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: First of all, thank you for the excellent book! I’m really excited about digging into our own customer data to see what we’ll learn.

A: Thank you for the kind words!

Q: However, when you’re creating the RF Scores, what is the standard timeframe you should use? I have access to about 5 years worth of purchase data – should I create RF scores based on the last 5 years, 3 years, 2 years, 6 months?

Our sales are quite cyclical, so I think the baseline should probably be at least a year, and I’m considering doing two years. It seems as though if I get too much larger than that, my results will be too watered down.

I’m also planning on generating “historical” RF scores by filtering my data to reflect the purchases only up to a certain point. So, to generate a Q1-09 score, I’d create it from sales data of Q1-07 through Q1-09. The Q2-09 score would be from Q2-07 through Q2-09, etc. Does this make sense? It will allow us to see the changes that have been happening in our company even though we’re only just now looking at the data. It will give me a picture of what it would have looked like, had I looked at it back then.

A:  I think you have accurately understood the situation and have the right approach! This type of analysis is very sensitive to time frame.

There are really 2 broad types of customer analysis. There is analysis for action in the present, a Tactical approach driving towards a “we should do this now” result, and the more Strategic analysis, which is informational and says “this is what we should have done then” and / or “this is why we should make these business changes”. The shorter time frame is Tactical, the longer timeframe Strategic.

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Adoption and Abandonment

DataBase Marketing, Marketing Research, Measuring Engagement, Web Analytics

Out of the Wharton School we have a nice piece of behavioral research on the effect speed of Adoption has on longer-term commitment.  The article, The Long-term Downside of Overnight Success, describes research finding “the adoption velocity has a negative effect on the cumulative number of adopters”. 

This research dovetails nicely with a lot of the topics discussed here on the blog lately, so I thought I’d use it (with a nod to Godin’s post on Strategy vs. Tactics today) to provide some fodder for thought.

First, the importance of Psychology in Marketing.  So many of the “discoveries” arrived at through  brute force testing of Online Advertising are already well known in the greater discipline of Marketing through Psychology.  For more on this read “The Other 3P’s” and if you’d like to do something about lack of knowledge in this area, make sure to read this comment on source books.

Second, this research is a great example of isolating the true drivers of behavior.  The idea of looking at baby names to isolate the real behavior from “technology and other commercial effects” while including “symbolic meaning about identity” results in a broad, Strategic-level answer to the question, not a Tactical one. 

Why is this important?  It means the results can be applied across a host of different Marketing situations, rather than only a specific one. 

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Loyalty Program Structure & Tracking

DataBase Marketing, Driller Q & A, Newsletters

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

Q: I’m involved in a loyalty program analytics project.  This client is a local pharmacy. All sales are done directly in store, the web site is just for communication purposes. The general problem we are trying to solve is the manager doesn’t have any detailed ideas about shoppers behavior apart from human observation.

The idea is to launch a card-based loyalty program which will track sales activity and give insight into customer behavior. The program will be points-based calculated on amount spent.  Points can be redeemed as rebates, coupons, gift certificates, or use points to buy items in loyalty program catalog.

The task is to segment customers according to their recent purchase behavior and determine the customer lifecycle. I’ve been able to do some basic analysis using the R package and MySQL database, but am unable to detect customer lifecycle.

Can you please give me guidance on this?

A: What is the Objective of detecting the LifeCycle, to create a more “active” customer retention program? Loyalty programs can be quite “passive” and often benefit from a more active overlay. But there can be many reasons to want to understand the LifeCycle…

Q: My 2nd task is to use the behavioral data with demographics to build a direct marketing strategy and provide management with insight into the customer base, for example: percent new customers, % of Gold customers who passed to Silver in last quarter.

A: Again, it would be helpful to understand how management would take action on this data. But I suppose you are in the common position of not knowing the tactical approach, and nobody will lay it out for you (a.k.a. they are clueless)…and you don’t know the right questions to ask or how to ask them.

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