Category Archives: Measuring Engagement

New RFM: Managing Customer Value Like an Investment Portfolio

Jim answers questions from fellow Drillers

Topic Overview

Hi again folks, Jim Novo here.

Do you manage your own investments in the stock market? If you do, you probably have used technical indicators like moving average of prices or up / down volume balances or similar to make investment decisions. And if so, guess what? This approach to investment portfolio management is very similar to the management of customer value, it’s really all about the metrics and the source of changes to those metrics. We can so some Drilling’ if you like …

Q:  I have been enjoying reading your tutorials.  I am interested in the financial planning market particularly and have developed an application for segmentation of market and clients by attitudinal factors.  Having provided my clients (advisers) with the tools to turn the qualitative data into quantitative measures and slice and dice their client base appropriately, the next question from them is “How do I use this and what to do with the information?.”

A:  You betcha, that’s the hard part.  A common question when people get into analysis; the “what do I do with this” should come first so the metrics produce an actionable outcome…

Q:  I would be interested in providing links on my web space to access your papers and content. Do you have any content or case study examples for marketing and client servicing for the financial planning industry?

A:  Well, I don’t think I have a page on my site specifically on this area, but let’s create one, OK?  I’ll include this example on my blog and it will go up on my site.

Characteristics and attitudes are interesting but frequently not particularly actionable because they are not “behaviors.”  When people speak of “doing something,” they are typically thinking of increasing or decreasing a behavior of the customer.  If you are trying to figure out what to do about a behavior, you really need to use behavioral metrics, which will tell you “who” to do something to and “when” you should do it for best results.

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New RFM: Customer Retention in “Subscription” Businesses

Jim answers questions from fellow Drillers

Topic Overview

Hi again folks, Jim Novo here.

How do you measure likelihood of customer defection when purchase behavior is highly orchestrated or executed due to repetitive billings? Yea, it’s a bit more complicated because “orders” really can’t express any kind of behavioral change, can they? So, you have to find indicators other than sales to provide the triggers. The Drillin’ the Drillin’ …

Q:  Jim, first let me say that I am enjoying your book VERY MUCH!!  Nicely done, and a nice job of integrating it with the CRM paradigm, 1-to-1 etc… I’m reading very slowly and finished the Latency Metric Toolkit.

A:  Great!  Thanks for the kind words.

Q:  I had a couple of questions on the Latency toolkit and the Latency tripwire, especially as it applies to environments with built in cycles for repeat purchases.

I am in a business where our resources are quarterly based, i.e. customers purchase our resource use them for a quarter and re-purchase the next quarter’s resource.  That is, we have a built in pattern, where customers would purchase our resources each quarter.  I was wondering how well I can use Latency with this type of built in cycle or if I would have any problems applying your Latency concepts to it, maybe they apply that much more readily?   In our case we try to call most folks who haven’t purchased within 2 weeks of a new quarter beginning.

A:  Right, a subscription-type business.  This is also an issue with utilities and other like businesses who bill about the same amount each month or have contracts for service (like wireless).  The answer is if the revenue generation really doesn’t represent anything to do with the behavior, then you simply look for other parameters to profile.  For example, a friend of mine was responsible for analyzing the likelihood of subscription renewal in a business that provided the content online.   Increasing Latency of visit was a warning flag for pending defection, and they triggered their most profitable campaigns based on last visit Recency.  In wireless, the correlations are found in payment Latency and age of phone.

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New RFM: Using RFM to Improve Email Profit

Jim answers questions from fellow Drillers

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.

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