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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http://www.booklocker.com/jimnovo
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http://www.jimnovo.com/newsletters.htm
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In This Issue:
# Topics Overview
# Best Customer Retention Articles
# Recency Metric Needs Context
# How to Define Frequency in B2B?
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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.
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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.
Jim
-------------------------------
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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!
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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
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tell me where the material will appear.
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