Category Archives: Analytics Education

Are Quitters of Club Likely Still Good Customers?

Analytics Education, Brand Management, 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.

How do you handle the measurement of “likely to purchase” when there’s a built in cycle of purchase as a “member”, like in a book club or other auto-delivery scheme? And what if a member quits membership but keeps buying, what does that mean for predicting future buyer behavior? Oh, the complexity of it all! Let’s do the Drillin’ …

Q:  I just ordered the book too, so I am eager to learn more about SIMPLE ways to implement RFM-based strategies.

A:  Well, thank you for ordering!  I hope it fulfills your expectations.

Q:  In the continuity club (Jim’s Note: flower of the month, book of the month, beer of the month) club business though, a little of the RFM process looks tricky because everyone has a certain Frequency built-in, because of the “repeat” nature of clubs.  Also, we’re starting to see a  phenomenon where customers that drop out of our club continue to order from us.

A:  This is quite normal, depending on how the club is set up and whether or not you make it “easy” for people to continue.  In some clubs, you are either in or not (books, CD’s, credit cards).  Most catalog-type clubs (pay a fee in exchange for ongoing discounts / added services) see continuation beyond club membership.  It’s a volume-based thing and a “rational” decision by the consumer – if you need to buy a lot of stuff, joining the club makes sense, because the discount pays for the membership.  

In your case, it might be more attached to education, for example – you join the club to educate yourself about the products, then quit when you can “do it on your own.”  Or, you get lots of  product to experience the variety, and settle into a specific usage pattern.  This is the Customer LifeCycle at work.  If you can recognize these patterns, you can use them to predict what customers are likely to do next.  If you can predict behavior, you can create very high ROI customer marketing programs.

Continue reading Are Quitters of Club Likely Still Good Customers? →

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. 

Continue reading Retention and Defection Scoring in Travel →

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:

Continue reading Predicting CRM Payback →