Category Archives: Analytics Education

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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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.

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