Tag Archives: Customer State

How Much is Promotional Proneness Costing You?

Analytics Education, Customer Experience, 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.

Is your mission to increase Sales or Net Margin dollars? Worth getting some clarity on if you’re not sure, and if it’s Margin dollars you are after, watch out for Promotional Proneness. What’s that? The tendency of customers to learn promotional patterns and “wait for a discount”, which can significantly impact campaign profitability. Got Proneness? Read on Fellow Driller, you will learn how to find out – by measuring it using control groups!

Q:  I really have enjoyed your book.

A:  Thanks for buying it, and for taking the time to tell me you enjoyed it!

Q:  I’ve created a first draft of a customer retention strategy that outlines proposed offers at various trip wire stages, and based on your order frequency.  So, if you are a one time buyer, and you are 8 weeks over your average buying frequency, you get a certain offer, and this would differ if you were a 4-time buyer, and are just one-week over your average buying Frequency.  As you suggested, the offers increase in value the longer it’s been since you’ve purchased.

A:  So you are segmenting by Frequency and Latency and then using Recency as a trigger.  You must have really learned something from the book, I don’t think I ever covered that one specifically!  But it makes a lot of sense to use Latency instead of Recency to segment in a category with a high percentage of consumable products (FYI Dear Reader – office supplies), since there is some expectation for re-supply and the purchase rate should be relatively constant for paper, toner, pens, etc.

Q:  But how do you prevent teaching behavior that causes the customer to wait until the better offers come?  These offers would only be sent to people that hit the trip wire (not individuals buying on their own).  How do we not teach a behavior that encourages the customer to wait for the better offer?

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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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