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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. 

Continue reading Difference between RF(M) Scores & LifeCycle Grids? →

Problems Calculating Retention Rate

Analytics Education, 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.

What is your customer retention rate? Well, that kinda depends on how you define the customer. Have you had an internal discussion, and more importantly, solidified agreement across divisions / functions on the definition of an (active?) customer? Please do.

For example, is someone who hasn’t interacted with your company in any way for over 5 years still a customer? You see, if you don’t specifically define a customer, then you can’t have discussions around topics like reactivation, retention, Lifetime Value (LTV) and so forth. Where to start? With segmentation. Create segments of similar customers, then try to decide which segments are still customers; this exercise will get you going down the right track. The Drillin’?

Q:  Seasonality has great effects on customers’ purchasing activities in the retailing industry, as you may easily understand.

A:  Yes…

Q:  Furthermore, what you call Latency has also great effects on their purchasing activities, (I mean, for example, the customer who purchased a coat in one winter season are not expected to purchase another until the next winter season and so forth.)

A:  Yes, but you are profiling customers, not products, right?  The customer who bought the coat may also buy a dress, shoes, pants in other seasons?  Your approach so far sounds a bit too product centric…

Q:  Here is the problem, how these issues of seasonality and Latency must be taken into consideration for calculating retention rate?

A:  Well, you can take it into account or not, depending on your objectives.  What is the objective of the analysis?  If the objective means you should take these issues into account, then you probably should segment the customer base to do so.

Continue reading Problems Calculating Retention Rate →

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.

Continue reading Behavioral versus Demographic Data →