Category Archives: Digital Analytics

New RFM: Using RFM to Improve Email Profit

Analytical Culture, DataBase Marketing, Digital Analytics, 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.

Traditional RFM execution is focused on giving a snapshot view of customer likelihood to respond / campaign profitability across large and varied customer databases. But is that all it can be used for? Heck no! If you understand the basics of how and why RFM works, and you understand your customer database, there’s a ton of different very valuable customer scoring operations you can accomplish. Interested? Get out the Drillin’ tools …

Q: I recently purchase your book “Drilling Down.” Really enjoying reading it!

A: Well, thanks for the kind words!

Q: I had a question about the implementation of the RFM model against email campaigns. Say we have a client that has done this:

  • Sent out 2 emails to entire database – in June and July
  • Sent out 3 targeted emails to a specific segment of database – in June, July and Aug

From my CTR and Open Rates I know that the targeted segment performance is better. For my scoring I am using the following:

  • Recency, last email responded to, and
  • Frequency, number of emails where an action (a click-thru) was taken

So the question is when trying to apply an Recency / Frequency RF score to the entire database, do you / can you use all 5 email programs? Would Recency include the email to the specific segment in August? Would frequency include the segment that received the email in August?

A:  The fact you are asking this question tells me you understand the methods better than you think you do.  The correct answer is yes, and no, depending on the objective of the scoring. As long as you **understand** that there is the potential for the marketing to the target segment to skew the scoring of the overall group, then you are thinking about the problem correctly.  Whether you decide to do the scoring as “everybody” or you score the targeted segment and then score “everybody else” separately really depends on what you are trying to accomplish / the objective of the effort.

Continue reading New RFM: Using RFM to Improve Email Profit →

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 →

Free / Pay Web Site Optimization

Analytical Culture, Customer Experience, DataBase Marketing, Digital Analytics, 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.

How do we make money on a content site? Free? Pay? Some combination of both? There’s been a lot of guessing and testing by the big media guys on how to work this, but how do the the small segments / little guys make this work, what can be measured to help? What about newer platforms like Substack, how do you measure optimization? On with the Drillin’ …

Jim’s Note: If you don’t know what Recency and Frequency are they are explained here, and RFM is covered here.  “Intensity” is Views per Session, in this case a “proxy” for visitor value.

Q: Hi Jim,

Should we use:

RFI – Recency, Frequency, Intensity
RFM – Recency, Frequency, and Monetary
or
RF – Recency, Frequency

to measure visitor value, and what should these terms ideally mean?  Total Sessions, Total page views, etc.  Also, when you measure Frequency, do you only include the Frequency during a specific period of time (i.e. one month, or one week), or do you include total lifetime activity per user?

A: On the advertising side of the business, I think the page views/session stat is probably the best to use.  The reality of the ad-based business is it doesn’t matter if they come back, you are selling impressions, not people.  I don’t think you have to overcomplicate it with formulas like RF or RFM, because you are primarily dealing with audiences, not individuals.  RF and RFM are about predicting if individuals will come back.

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