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Current Value / Potential Value Matrix
Drilling Down Newsletter # 51: 11/2004

Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention, 
Loyalty, Defection

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Prior Newsletters:

In This Issue:
# Topics Overview
# Best Customer Retention Articles
# Recency Metric Needs Context
# How to Define Frequency in B2B?

Topics Overview

Hi again folks, Jim Novo here.

This month we're looking at the basic strategy framework of 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.

We also have a couple of great article links, one on a new tracking technology and a loftier piece on what might be described as the "new marketing discipline" - though those of us familiar with database marketing have been living this life already for a very long time.

Let's do some Drillin'...

Best Customer Retention Articles

From 460% to 1165%: Analytics shine a Light on Select Comfort’s search ROI
November 2, 2004  Internet Retailer
There has always been a lot of speculation that online research drives offline sales, though I've always had problems with the methodology used in prior studies.  This one conclusively links online search behavior to offline sales using - get this - dynamically generated toll-free numbers for each search phrase.  Claude Hopkins would be proud of this one.

Connecting Marketing Metrics
to Financial Consequences
November 9, 2004  Knowledge@Wharton
Now that's a wild idea, huh?  Don't let those marketing freaks get away with spending money and not proving what the ROI is, I mean, every other "C-level" has to.  Start the process by sending the CFO this link.

If you are in SEO and the client isn't converting the additional visitors you generate, you can help them make it happen - click here.

Questions from Fellow Drillers

Recency Metric Needs Context

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.

Second, Recency is a customer-based metric, not a product-based metric; it describes the behavior of the customer and likelihood to purchase, not likelihood to purchase a specific product.  I agree a customer who bought a washer / dryer Recently isn't very likely to buy another one.  This doesn't mean they are not likely to buy a stove or microwave though.

So putting these two context bits together:

Looking at a customer who just bought a washer / dryer and comparing them to a customer whose last purchase was a washer / dryer 6 months ago, the more Recent customer is more likely to purchase from Best Buy again relative to the customer who bought the washer / dryer 6 months ago, without regard to what they might purchase.

Q:  I would think that you'd really have to intersect the Recency with Frequency in order to truly predict the future behavior.  So if I bought 5 times on your site, and the most Recent was a week ago, I would think that person would be a higher value than someone who has only bought once, but 2 days ago.

A:  Well, value is a different story, Recency only predicts likelihood to buy, it speaks to potential value.  Frequency speaks to current value, this is a different concept.  What you said is true, the former person has a higher current value, but the latter person has higher potential value, is more likely to create value in the future, than the former person.  This is the customer value model, you can check it out graphically here; all customers have some mix of current and potential value.

In fact, you can create a two-digit score, in this case, Recency and Frequency or what I call an RF score, and rank all customers by a mix of current and potential value.  This can be used for many things, for example, predicting the response rate to promotions - the higher the score, the higher the response rate.

So adding Frequency to Recency does in fact make a Recency model even more powerful than Recency alone.  However, the reverse is not true.  Frequency alone is not a reliable predictor of likelihood to act in the future.

High Frequency and long Recency indicates an already defected best customer, again, relative to other customers with higher Recency.  Low Frequency / short Recency is a new customer who is a potential best customer.  All of this can be plotted on the customer value model grid to create a "map" for managing customer value.  Frequency by itself is not nearly as predictive as Recency by itself, though many people base segmentation strategies on Frequency because it is easier to count transactions than to measure "time since last activity".  All Frequency tells you is what the customer is worth today, it does not speak to future value.  If you are talking about "likely to buy again", you are talking about the future, not the present.

The best web-oriented story I have seen on this subject is from Amazon.  They used to announce the total number of customers who had purchased over 10 times (or was it 100?) each quarter.  It was the main number people focused on.  Then a retail analyst asked how many of these people had bought in the last 12 months (12 month Recency)?  The answer was a good deal less than 100%, and the stock absolutely tanked that day, because the retail analysts knew that many of those 10x customers had very low future value, and the future outlook is what drives stock prices.

If you are a consultant, agency, or software developer with clients needing action-oriented customer intelligence or High ROI Customer
Marketing program designs, click here.

How to Define "Frequency" in B2B?

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.

A:  Oh my, I think I have failed you.  It seems like you are just searching for answers without having a question first, which would be my fault.  Or, are you just trying to build a "profile" of your customer base for further study?  What is your objective?

Let's say you are trying to look at a basic  retention idea - the current value / potential value 2 x 2 matrix.  In other words, you have customers who are "best" and customers who are not, and you want to know, how are we doing on keeping the different types of customers active with us?  Have high value customers stopped doing business with us?  Are we growing low value customers?  How likely is it we can expect future business from these customers?

So you take your clients and make sure you have 2 numbers available for each - total spend and last job date.  Put them in a spreadsheet with these numbers and sort by current value - total billing.  Then start looking at last job date - do you have high current value clients that have not completed a job lately (low future value)?  Why?  Should somebody call them and find out?  The longer it has been since the last job, the less likely it is they will be booking another.

Or, you might look at job Latency as you suggested, if you think that is more relevant.  For each client, how many weeks or months go by before they book their next job?  If the average for a particular client is 6 weeks, and they are now at 10 weeks since the last job, should somebody contact them and find out if there is work to be done?  Find out if something went wrong with the last job that needs attention or correction?

I hope the above has helped you frame the question you are trying to answer.  If you want to supply some more specifics I may be able to be more concrete with direction.  Is there a "problem" you are trying to solve, or are you just trying to create a "profile" of your customer base for further study?

Q:  Your book is the "bomb"!  And I am trying to get it.  Thanks for your help in advance.

A:  Glad to help.  You're a customer now!

That's it for this month's edition of the Drilling Down newsletter.  If you like the newsletter, please forward it to a friend!  Subscription instructions are top and bottom of this page.

Any comments on the newsletter (it's too long, too short, topic suggestions, etc.) please send them right along to me, along with any other questions on customer Valuation, Retention, Loyalty, and Defection here.

'Til next time, keep Drilling Down!

- Jim Novo

Copyright 2004, The Drilling Down Project by Jim Novo.  All rights reserved.  You are free to use material from this newsletter in whole or in part as long as you include complete credits, including live web site link and e-mail link.  Please tell me where the material will appear. 


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