Monthly Archives: April 2024

How to Define “Frequency” Metric in B2B

Analytics Education, Customer Experience, DataBase Marketing, Driller Q & A, Marketing Research, 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.

If you’re not really clear on what you’re trying to accomplish, designing a successful customer retention program can be a bit of a struggle. Hey, maybe you just don’t know what to look for / what needs fixing / where to start? Gotcha, fellow Driller, the current value / potential value matrix is a great place to start – for you, and perhaps more importantly, your boss / the CFO. Ready to try on some focus? Let’s get to the Drillin’ …

Q:  I am totally getting into your book.  I am up through chapter 17 and have completed my RF Scoring.  My company [my day job] is a custom software company.  It was difficult for me to get my head around the units thing yet, so I just used the “M” as you put it.

A:  Thanks for the kind words, I’m glad it’s working for you!

Q:  In term of companies, we are probably like the B2B example you used in Chapter 8.  So, I could not get my head around the units deal yet because I have not studied the data enough to see if there is a progression.  I think I would need to look at it year to year; but should I stop now and do it first?

A:  Well, customer analysis always starts with an objective…what are you trying to look at / prove / do?  It’s hard to comment without knowing the business problem or issue you are facing…and without any information on how your business really works.  I can rarely find that out from looking at a web site…

“Units” would probably be the total number of “jobs” you have completed for a client.  It also could be the total number of hours the client has used, if that is more logical for the business.  It’s hard to tell without a bit more information.  The point of the “units” variable is to look at the Frequency of commitment, so use whatever makes sense for the business.

Q:  So, my question is, should I go back and do what you suggest in chapter 9 – setting up a look at Latency by customer to get the progression before I continue with Chapter 18.

Continue reading How to Define “Frequency” Metric in B2B →

Context Parameters for Best Use of Recency Metric

Analytics Education, Customer Experience, DataBase Marketing, Driller Q & A, Lab Store

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.

Time to take a look at some basic strategy framework ideas in a customer retention program. You have to know where you are first before you can decide what actions to take, and this initial analysis will prompt ideas for action. Trust me, finding out specifically what is happening in an actionable way is the most critical step to the design and execution of a customer retention program. Not doing this is why so many of the programs fail. Ready, Driller? Let’s do it.

Q: I’m reading some of your information you have on your web site, regarding Recency / Frequency. I’m curious about the statement that Recency is the number one most powerful predictor of future behavior – if you did some thing recently you’re more likely to do it again.

A: Yes. Funny thing about web sites, it’s hard to control what sequence people read things in. From the questions below, I believe I have failed to introduce you to the Recency metric in the right context. Shame on me!

Q:  With regards to purchases, how is this so?  I can think of numerous instances where this might not be true.  In fact, I would guess that price of purchase would be a more likely indicator of whether or not someone would purchase again.  If I’m running Best Buy, and someone comes and buys a washer / dryer, I would not expect they’d be buying another one anytime soon.  Ditto furniture, cars, travel bookings, etc.

A:  Two important “context” issues surrounding Recency.  First, Recency is a “relative” metric, it doesn’t exist by itself, but “relative” to other data points.  In the case of customers, Recency and the “likelihood” is a relative comparison of two customers, two customer segments, or a customer versus the average customer, for example.  So for a washer / dryer purchase, looking at the customer in question, Recency answers the question, “how likely is this person to purchase relative to another customer”.  It’s a scoring system, a ranking of likelihoods to (in this case) buy, or visit, or download, or whatever.

Continue reading Context Parameters for Best Use of Recency Metric →