Category Archives: Analytics Education

Retention and Defection Scoring in Travel

Analytical Culture, 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.

What happens when your business doesn’t really fit the mold of traditional customer scoring that retail and similar businesses use? Well, you can certainly come up with a version of the tradional models that is customized for your business, as we do below for a fellow Driller’s travel agency. Wanna see how that’s done? Then, to the Drillin’ …

Q:  Just read your book and I say full marks for such a practical and sensible approach!!! Start small and grow is the way to go. 

A:  Well, thanks for the kind words!

Q:  I am a part owner of a travel agency (not been the best area to be in during pandemic).

A:  Eeeeek!

Q:  My first focus for Drilling Down is on our leisure customers.  But my head is spinning a bit with all the ideas I have from your book.  I can electronically access our: customer names etc., an ID number, when they purchased, how much the product cost, the supplier, the category (i.e. air only, cruise, tour etc.) and the final destination.  If you would be so kind as to give me a little steer in the right direction  in setting up the metrics and scores.

A:  Hmmm…  I of course don’t know your business but would think that particularly in leisure, there is a natural cyclicality caused by vacation timing, anniversary events, and such.  So in terms of timing, you use a classic Latency approach, e.g. if a customer took a trip last July they are somewhat likely to take one this July.  If they took one last July AND the July before, they are very likely to take one this July.  If they have taken a trip the last 5 July’s in a row, they are extremely likely to take one this July.  

So you can rank customers by likelihood to travel each month, and if you want, could assign them a “score” to represent this likelihood, in the case above, extremely likely = 5, very likely = 4, etc.  People who have not booked with you for a year might be a 2, not for 2 years a 1. 

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Predicting CRM Payback

Analytical Culture, Analytics Education, Customer Experience, DataBase Marketing, Driller Q & A, Marketing / Tech Interface

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.

Yea, I know. Boss wants to do smart customer marketing, but also wants to know what the payback will be for the “CRM investment”. Geesh, can’t we just figure cost out later? Sure you can, but might be better to make sure you understand what the “CRM success levers” are before you go spend a ton of money. You mean Drillin’, Jim? Sure, on to The Drillin’ …

Q:  Hi Jim,

Our industry is facility management services where a headquarters with chain locations contracts with us to manage their facilities in all their markets.  The President is interested in a “CRM Solution” but is concerned about the ROI he might expect from implementation.  Do you know of any number that I can pass along to him that would placate his insistence on knowing in advance what the ROI will be?

A:  Bad news: No, not really.

Good news: You can figure it out, which is something often not done.  You might not even need any new software to “do CRM,” though it depends on what you have now and what the objective of the CRM program is (you do have an objective, correct?).  But the software required is likely not millions of dollars and if you only have 100’s of clients you could probably do it with some combination of contact software, MS Access / Excel. 

The key question to ask: do you really know how your customers behave?  In this kind of contract business, I imagine the central issue is this: Can you predict which customers are likely to re-up a contract, and which ones are not?  And then can you use this information to focus on the ones less likely to re-up, and take steps to make them more likely to re-up?

Sometimes it is just a matter of better customer service.  In this case, what you need is better service practices, not “CRM.”  From a distance, it is very difficult to know what the issues might be in your company.

Here’s a test you can do to find out where you might be on the road to answering the CRM question.  If you cannot accomplish one or more parts of the following, you are not ready to even talk about “CRM,” and need to do some more internal research.  These steps, by the way, are the ones everybody skipped on the early rounds of CRM that created so many bad outcomes. Research / Implement / Discuss the following ideas to pave the way for a successful implementation if you decide to go with a CRM approach:

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RFM and Customer LifeCycles

Analytical Culture, Analytics Education, Customer Experience, Digital Analytics, 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.

Today we have a bit of confusion between RFM modeling and tracking Customer Lifecycles. Each has benefits and downsides, but the most important idea is to make sure you know what each is best at. Make sense? Let’s do the Drillin’ …

Q:  I have a small sampling of the RFM scores that correspond to the various lifecycle stages.  For instance, 111 & 112 correspond to the acquisition stage, 333 & 443 to the growth stage, etc.  However, I’m looking for a complete listing of all 125 possible RFM scores and their corresponding lifecycle stages.

Can you please send this my way?

A: Wow, I certainly hope you didn’t get this idea from me; if you did, I have done a terrible job of explaining something somewhere. I would be very interested in the source of this idea, that a LifeCycle stage can correspond to a single RFM code or score.

An RFM code or score is the ranking of a single customer against all other customers for likelihood to respond and future value at a specific point in time. High scores equal high future value; low scores equal low future value.

A single RFM score represents this ranking at a fixed point in time – the day the scores were created. There is no “cycle,” which implies “over time,” inherent in an RFM code. Only if you knew the previous RFM code or sequence of codes could you imply a “LifeCycle stage”. This is, of course, what my book is about – using a modified version of RFM to track and profitably act on customer LifeCycle behavior. If you know the LifeCycle, you can predict behavior. If you can predict behavior, you can dramatically improve marketing ROI.

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