Category Archives: Analytics Education

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.

Continue reading RFM and Customer LifeCycles →

When Do Former Best Customers Become a Lost Cause?

Analytical Culture, Analytics Education, DataBase Marketing, Driller Q & A, Lab Store, 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.

This time, a Real World question from a practitioner who wants to prove to management they have to spend less to make more money. Spend less to make more? How could that be, and what kind of person would want to go down this road? A real world Driller, of course …

Q: I’m a “long time listener, first time caller,” and a big fan of your site and your approach to data-driven marketing.  I also have two copies of your book – one was not enough.

A: Well, thanks for your kind words. I love the talk radio reference, that is so funny.  Never though about it like that, but makes perfect sense!  Glad to know I’m actually helping people with the book too.

Q: I have a question relating to some work I am doing now with our best customers that other users of your site may have.

I work for a medium sized DTC company selling skincare products (high margin) via space ads, direct mail, and online. Our best customer “Gold Club” has about 8000 members at the moment, although members are being promoted and demoted all the time.  

According to my initial analysis, if a member does not purchase a product for more than 60 days, the chances are that they are defecting. I would like to attempt to bring them back with an offer, and leave those that don’t reply for at least 6 months for a deeply discounted “kickstart” offer (although the logistics of sending out very small mailings are a pain.) 

A: This is a common and logical approach, particularly for “renewable products.”  You don’t say what the product is, but if it is “typical” skincare product, it has a sales cycle very tightly tied to product use.  In this case, Latency usually makes more sense to use than Recency as the primary trigger for a campaign.

Continue reading When Do Former Best Customers Become a Lost Cause? →

RF(M) Scoring for Offline Service Businesses

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.

Yea, I know, so much talk about digital … but does this stuff work for offline businesses? It sure does, in fact, these models were originally developed for offline – way before online was even a thing. But because of the ease of data collection, they tend to work even better online! How about for a natural healing center or an accounting practice? Sure thing! Let’s do the Drillin’ …

Q: I stumbled across your Web site some time ago and have been a regular visitor since.  I find your information very useful.  You will be pleased to know that I purchased your book (Drilling Down) and have just finished going through it.  It all sounds so easy!  Your explanations and examples were wonderful and easy to understand. 

A:   Well, thanks for the kind words.  Would you mind if I used the paragraph above as a testimonial on my web site? 

Q: Now I will attempt to put it all into practice for two businesses – a Natural Healing Centre (massage, natural medicine etc.), and an Accounting practice. 

A: The healing centre is a pretty straight-up situation; should work very well for them just as described in the book.  The accountant, as a service business with a built-in “forced” cycle (the tax year), a little more complex.  More on this below.

Q:   I have 2 questions though, if I can.

A: Sure!  The two questions below are related, so I will answer them as one.  Only one to a customer!  Just kidding…

Q1: Neither business has a Web Site, so a visit to the workplace, usually means a purchase.  I was intending to have R = last visit, and F = visits over past 12 months.  Will this work?

Q2:   Should I put a timeframe on F?  The way I see it, if I don’t, F will continue to grow for each customer as long as they are a customer.  Whereas if I put a timeframe it will give a better picture of behaviour patterns.

Continue reading RF(M) Scoring for Offline Service Businesses →