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The Financial Impact of Predictive Modeling (Part 1)
By Jim Wheaton
Principal, Wheaton Group
Original version of an article that appeared in the
December 11, 1995 issue of "DM News"
A popular topic in the direct marketing industry is the use of statistics-based
predictive models to optimize mailing strategies. A common
thread throughout many of these discussions is that predictive models,
although powerful segmentation tools, are cost-justified only for
the largest of mailers.
This article will show how predictive models are appropriate even
for modestly sized direct marketers. We will accomplish this
by performing a financial simulation of a single promotion by a
fictitious catalog company with 600,000 active customers, using
This promotion will be segmented first by traditional RFM Cells
and then by a statistics-based predictive model. We will see
how an investment of, say, $25,000 in a predictive model will return
an incremental $18,000 in revenue and $68,000 in profit over traditional
RFM Cells after just one promotion. At $68,000 per promotion,
it is clear that the profitability gains over a full year's mailing
cycle are profound!
The starting point for our financial simulation is the underlying
promotion assumptions listed in Table 1:
Although most of these assumptions are self-explanatory, I will
elaborate on two:
- The overall response rate, assuming the promotion of all 600,000
customers, is 2.0%. As we will see in a moment, this indiscriminate
promotion strategy generates exactly $0 in profit.
- The company overhead allocated to our promotion is $90,000, which
is a fraction of the total overhead that must be covered throughout
an entire year.
Organizing these promotion assumptions into the simple worksheet
illustrated in Table 2 shows how mailing all 600,000 customers generates
$900,000 in sales but $0 in profit. With nothing left over
for critical functions such as customer acquisition, indiscriminate
mailing results in a business with no future. What is needed
to ensure long-term viability is some kind of segmentation strategy.
For many decades, direct marketers have understood the need for
segmentation. Traditionally, they have resorted to RFM Cells,
which entails the grouping of customers with similar recency, frequency
and monetary purchase history. (Many permutations of this
approach are used, some of which incorporate additional criteria
such as product category and inception media.)
The idea behind any RFM approach, as well as other segmentation
strategies, is to identify and eliminate from the promotion all
individuals whose predicted performance is below breakeven.
Therefore, it is imperative that we first establish the breakeven
response rate for our catalog promotion. And this, as outlined
in the Table 3 worksheet, is 1.54%:
Note that the breakeven response rate has been calculated without
regard to company overhead. Although the reason for this is
philosophical, I believe it to be sound:
Breakeven is a consideration only at the margin; that is, for those
customers who are borderline candidates for promotion. If
company overhead has not already been comfortably covered by the
more productive customer segments, then structural business problems
exist that even segmentation will not cure.
Table 4-A illustrates just how an RFM segmentation strategy works.
Before discussing this in detail, however, some background information
For the sake of simplicity, only 10 RFM Cells have been created.
In reality, I have seen RFM segmentation strategies that range from
a few simple cells to one that numbered in the thousands.
For our example, the absolute number of cells is not important.
Instead, the key issue is how well they differentiate those customers
who are likely to respond from those who are not. And the
measurement of this discriminatory power involves a concept called
Lift in Table 4-A is defined as the ratio of a given RFM Cell's
response rate to the overall response rate of 2.00%. For the
top 10% of the file, which is defined by Cell 1, the response rate
of 4.00% translates into a ratio-to-average of 2.00 (i.e., 4.00%
/ 2.00%). The bottom 10% or Cell 10, on the other hand, has
a ratio-to-average or "lift" of 0.40 (i.e., 0.80% / 2.00%).
Although in the real world one never sees RFM Cells of equal 10%
quantities, a lift of 2.00 for the cells that correspond to the
approximately-top 10% best customers is better-than-average performance
for a typical catalog. Although I've seen lifts higher than
the low 2's, it is not common.
(As an aside, some readers — mindful of the adage that 20%
of the customers generally account for 80% of the sales — will
be skeptical of this modest lift assumption. Keep in mind
that, after the fact, we can identify with absolute certainty the
best-performing 20% of a given customer base, and then measure its
What we are attempting to do here is both different and difficult,
and that is to predict the best customers. Unfortunately,
this can never be done with anything near absolute accuracy.
In other words, the composition of our magical 20% changes all the
time, which — in turn — degrades significantly the often-quoted
80% as we look to the future!)
Segmentation Strategy #1 — RFM Cells
(1.54% = Breakeven Response Rate)
As we see in Table 4-A, RFM Cells, as with any other segmentation
strategy, do nothing more than re-sequence the customer file from
most to least likely to respond. This is why, if our cataloger
mails all ten RFM Cells, the total revenue will remain at $900,000
and the total profit at $0.
Notice that the Contribution to Overhead and Profit for Cells 8,
9 and 10 is negative $2,700, $8,550 and $14,400 respectively, which
is not surprising given that their corresponding response rates
of 1.40%, 1.10% and 0.80% are below our 1.54% breakeven. Although
these cells are generating incremental revenue, overall profitability
is being lowered in the process. We would be better off sacrificing
this additional revenue for the sake of the bottom line.
Table 4-B illustrates the effect of mailing only the seven profitable
RFM Cells. Although revenue is down almost $150,000, to $751,500,
we now have a mailing that is $25,650 in the black, or 3.41% of
total sales. Although not outstanding, it is a significant
improvement over indiscriminately mailing the entire file.
RFM Cells (cont.)
(1.54% = Breakeven Response Rate)
(To be continued. Click here to continue.)
Jim Wheaton is a Principal at Wheaton Group, and can be reached
at 919-969-8859 or email@example.com. The firm
specializes in direct marketing consulting and data mining, data
quality assessment and assurance, and the delivery of cost-effective
data warehouses and marts. Jim is also a Co-Founder of Data