Tag Archives: Relationship Marketing

Modeling Customer Behavior with Small Databases

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.

We’re about to take a trip into the world of small scale databases. In particular, how does a not-for-profit with a small database of donors go about using predictive models? Answer: Keep it simple. Try to avoid using a lot of variables; look for the most powerful and stick with those until you are able to uncover additonal info and grow the database. Ready?

Q: I am new in the NFP (Jim’s note – Not For Profit) sector and would like some advice re:  segmentation models to optimize campaign results – both response and value (Short Term  and Long Term).  Do you know or is there any knowledgebase of how the various techniques – behavioural, RFM, demographic, geographic – generally rate against each other?

A: Not other than my web site / book, which generally covers all the simple models. There is plenty of info around on the web though.

Assuming the end Objective is a donation, the behavioral stuff is going to be much more productive than the geo / demographics are. It’s like a pyramid.  My friend Avinash “stole” (with my permission) a slide from my presentation on this topic and put it on the web, you can see it here.  You’re looking for an “action” (donation), so actions (behavior) will be the most useful segmentation, at least as a primary cut.  Then you can get into geo /  demo stuff if it improves the model.

Q: As my database is small I don’t have the luxury of testing multiple techniques and causal factors.  I will probably run tests in series but would like a general idea of which ones to test first to cut down the time.

A:  Not sure what you mean by “small”, but in general, the more complex a behavioral segmentation approach is the larger the database it needs to be useful.  So for example, with classic RFM (125 segment scores), the bare minimum for it to make any sense is probably 5000 records, and you should really have at least 10,000.

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Difference between RF(M) Scores & LifeCycle Grids?

Analytical Culture, Analytics Education, 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.

Both RF(M) scoring and Lifecycle Grids use the same key predictive metrics – Recency and Frequency. So what’s the difference? RFM is a predictive “snapshot” at a specific point in time; LifeCycle Grids are more like a “movie” designed to be predictive over different periods of time. Another way to think of this: RFM is tactical, LifeCycle Grids are strategic.

You dig? Let’s Drill …

Q:  We’re a telecom company trying to get a handle on customer churn and defection, so we can come up with some programs that will hopefully extend customer participation.  We live in the no contract space, offering a service that’s an add on to wireless phone service, so we don’t have a good indicator as to when the customer relationship might end.

A:  Ah, yes.  Your business model is “built for churn”, as I said on my blog the other day.  The behavior then is more like retail, where independent decisions are made in an ongoing way, deciding again and again to purchase.

Q:  I think your LifeCycle Grids method will show best what is happening to our customers.  If using this method, there doesn’t seem to be any reason to do the RF scoring as customers are just going into cells based on where they fall in the Recency and Frequency spectrum.  Is that correct?  Is there any real  difference between RF scoring and the LifeCycle Grids approach?

A:  You are partially correct, they are two versions of the same idea – both are scoring using Recency and Frequency. The traditional RF(M) scoring where customers are ranked against each other is a “relative” scoring method used primarily for campaigns – it is tactical, an allocation of resources model. 

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Behavioral versus Demographic Data

Analytical Culture, 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.

Most businesses want their visitors or customers to “do something” – to take an action of some kind. Trying to drive action, businesses engage in marketing / advertising to reach “audiences” with their message.

These audiences can be quantified in a number of ways using Demographics, Sociographics, and Psychographics for the purpose of “targeting” the campaign. The idea is to make the campaigns more efficient by focusing resources on the types of people thought to be more interested in the product or service.

This is fine. But from psychology and actual practice, we know behavior predicts behavior and demographics do not. So given you want people to engage in a behavior, why would you not use behavior to target campaigns? OK? Let’s do some Drillin’!

Q:  Just finished my print out version of the latest Drilling Down newsletter, and came across what is probably your best quote ever: “You should be really most interested in what people do and why, rather than who they are, because behavior predicts behavior, demographics do not”.

A:  “Print out” version?  Are you implying my newsletter is too long?  You’re not alone… :0

Q:  Man !… I’m having the design department make a big banner and hang it next to the web analytics team cubicles…

A:  My favorite story on this issue: for years we thought the “best buyer demo” at Home Shopping Network was affluent women 50+.  I mean, you hear their voices on TV, you see their letters, you just know, right?  Then we did an enhancement of the database with what was then the most comprehensive and powerful demo package available.  And it didn’t look right, there were “too many young people”.  So we rejected it.

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