RFM versus LifeCycle Grids
Drilling Down Newsletter #103 8/2009
Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention, Loyalty, Defection
Get the Drilling Down Book!
Hi Folks, Jim Novo here.
As a follow-up to last month's piece on Loyalty
Program Structure and Tracking, we have the question - how do you
create standardized reporting that will show progress in a Loyalty or
Retention effort? As with any measurement project, the best
answer depends on your goals and what the data will be used for.
But there are some standards in this area, and this month we'll
examine a couple approaches to answering the standardized reporting
Over on the blog, we examine the implications for "grow
fast" web business models when there seems to be data out there
telling us the faster a web business grows, the less likely it is to
retain customers. We've seen this quality versus quantity
question many times offline - the "easy" customers are often
the least valuable. What could this mean for online?
Let's get to that Drillin'...
Sample Marketing Productivity Blog Posts
Adoption and Abandonment
Out of the Wharton School we have a nice piece of behavioral research on the effect speed of Adoption has on longer-term commitment.
The article, The Long-term Downside of Overnight Success, describes research finding “the adoption velocity has a negative effect on the cumulative number of adopters”.
Houston, a problem for many online business models, right?
August 7, 2009
I will respond to any comments you leave.
Questions from Fellow Drillers
RFM versus LifeCycle Grids
Q: First of all, thank you for the excellent book! I'm really excited about digging into our own customer
data to see what we'll learn.
A: Thank you for the kind words!
Q: However, when you're creating the RF Scores, what
is the standard timeframe you should use? I have access to about
5 years worth of purchase data - should I create RF scores based on
the last 5 years, 3 years, 2 years, 6 months?
Our sales are quite cyclical, so
I think the baseline should probably be at least a year, and I'm
considering doing two years. It seems as though if I get too
much larger than that, my results will be too watered down.
I'm also planning on generating "historical" RF scores by
filtering my data to reflect the purchases only up to a certain
point. So, to generate a Q1-09 score, I'd create it from sales
data of Q1-07 through Q1-09. The Q2-09 score would be from Q2-07
through Q2-09, etc. Does this make sense? It will allow us
to see the changes that have been happening in our company even though
we're only just now looking at the data. It will give me a
picture of what it would have looked like, had I looked at it back
A: I think you have accurately understood the situation and have the
right approach! There are really 2 broad types of customer analysis. There is
analysis for action in the present, a Tactical approach driving
towards a "we should do this now" result, and the more
Strategic analysis, which is informational and says "this is what
we should have done then" and / or "this is why we should
make these business changes". The shorter time frame is
Tactical, the longer timeframe Strategic.
So, for example, a 2 year timeframe could give you the answer to
this question: which of our best customers are becoming unlikely to
buy from us again? This leads to immediate activation of some
kind of marketing outreach or discount / incentive program to get another purchase
from this group. Add a timeframe that ends 4 years ago, then one ending 3 years ago, then one ending 2 years ago could highlight changes in the business over time, for example, best customers with high intent to purchase 3
years ago clustered in certain segments or SIC codes; now customers with this same definition are clustering in different segments or SIC codes. You will see migration of segment
focus, if any.
Another way to think about this is time frame for the RF analysis
determines sensitivity to new customers. Long time frames tend
to rank customers who have been with you a long time higher than new
customers; this is just a function of how the ranking methodology
works - these long-term customers have had more time to increase the
Frequency or Monetary component. This can mask important
rankings in Frequency with newer customers, what you might call
"future best customers" or "up-and-comers" who are
accelerating their purchase behavior.
You could even use this kind of analysis to prove the strengths (or
weaknesses) of the RFM methodology for your business: given an RFM
score of XXX 3 years ago, what behavior did the customer engage in
during the following years? Does score in one year predict
behavior next year?
Or, perhaps rather than a ranking approach, the fixed activity
threshold approach (like LifeCycle
Grids) is more appropriate to our
business. LifeCycle Grids are basically the same idea as RFM,
only sometimes more accurate for businesses with known cyclicality;
it's easier to build that cyclicality into the model if you abandon
"ranking" and use thresholds.
In fact, this idea was born from an exercise like the one you
propose: let's re-score and re-rank customers each quarter, and track
the RFM score over time. Nothing wrong with this really, except
there is the fundamental problem of scores changing due to outside
influences, for example, a large new customer campaign. When such a campaign is executed and then the database is
re-scored, the RFM scores of customers can change *even if their
behavior has not* because you are re-ranking a customer file that has
changed in composition. Due to the new customer campaign, it is now
"heavier" with Recency = 5 customers, which can push down
the scores of other customers, even though their behavior has not
This is the primary reason I invented the LifeCycle Grid
idea. If you use thresholds or Hurdles for behavioral segments
rather than ranking, the "score" of someone does not change
when the database composition changes. Someone deemed
"best" and likely to buy if R = 30 days and F >= 25
purchases is still "best", no matter how many records you
add to the database. These thresholds define the customer
status, not a ranking.
And that is why RFM tends to be used as the Tactical, "we are
doing a campaign right now" valuation method, and LifeCycle
Grids tend to be used for the more Strategic analytical
exercises. However, the Grids can also be used for Tactical
For example, any customer with F >= 25 over past 2 year period,
who drops in R past 90 days, automatically should receive a call from
their salesperson. These reports could get run on a weekly
basis, and of course can be segmented many different ways depending on
the population you run through the Grid. Because you're using
thresholds rather than "ranking", a customer will appear in
the Grid at the same location no matter what the size or segment of
the population used for input.
So, you can run only customers who responded to a
campaign and see where they end up in terms of Recency and Frequency
over time. With a series of such runs, say monthly, you can
create a "movie" that shows the evolution of the customers
over a time frame and begin to judge the long term effects of certain
campaigns. An example of this approach is here.
Overall, I like the Grid approach much better. Not only do
you avoid the "population problem" of ranking when using RFM,
but you can use the same approach over and over (good for management
understanding) for many different kind of analysis, both Strategic and
Tactical depending on needs. You can use all kinds of visual
aids such as color in the grid to represent different segments or
campaigns, making presentations much easier for management to
understand. Decision making with execs can be much more of a
challenge when all you have is RFM scores.
All that said, RFM is still probably the easiest approach for
specific, usually campaign-related tasks such as predicting campaign
response or profitability. Same data but a different, more
short-term oriented way to look at the world probably best kept out of
the boardroom but still has a place in the analyst's toolbox.
Hope that helps!
Have a question on Customer Valuation, Retention, Loyalty, or Defection?
Go ahead and send it to me here.
If you are a consultant, agency, or software developer with clients
needing action-oriented customer intelligence or High ROI Customer
Marketing program designs, click
That's it for this month's edition of the Drilling Down newsletter.
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'Til next time, keep Drilling Down!
- Jim Novo
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