Monetary or Average Order – is RFA Better than RFM?
For decades direct marketers have used RFM as their primary
segmentation tool, and the M (Monetary) traditionally stood for “Lifetime
Spending to Date.”
Personal experience provided a couple of indications that
Monetary wasn’t what it was cracked up to be.
Back in the 90’s, a catalog client asked for a list of best catalog customers,
so he could send each one a thank you note. Upon reviewing the list, it became obvious that about a third of the
higher-dollar, recent, frequent customers had many, many orders. They ordered small items (in this case, a
paperback book) one-at-a-time, and spent hundreds of dollars. But each individual sale was so small, given the cost
of fulfillment, the catalog actually lost money on each order.
The list of “best customers” was really about 2/3rds best and 1/3rd worst customers.
Then by sheer chance two different jewelry retailers brought us results
where they had tested three segments; high-dollar coupons, low-dollar coupons,
and a control group. Both had the same strange results.
Upon reviewing the results, we found the high dollar offer
worked best with high average order customers, followed by the control group
(that got nothing), and then the low dollar offer. For high average order customers, the low
dollar offer actually suppressed response below the un-mailed control group.
The opposite – or maybe we should call it the same –
happened on the low-dollar end. The low
dollar offer won, followed by the un-mailed control group, followed by the
high-dollar offer that actually suppressed response.
For those in fundraising, this is hardly a shock. The best ask is in the range, maybe a little
higher, than the typical or most recent gift.
But for direct marketers targeting segments with common offers, does it
really matter?
To try to answer the question, we went used a similar 20,000
name list like what we used for checking the 40-40-20 rule that’s described in another
blog. This time we used only the
key-coded segment as the groups to create the analysis, and looked at both
likelihood of response and dollars-per-name.
Dollars-per-name is defined as (response rate * average order) and it is a good indicator of the profitability of a segment.
RFM versus Response Rate gave us an R-Squared of 0.73, so it
explained 73% of the difference in response among segments.
RFA versus Response Rate gave us an R-Squared of 0.80, so it
explained 80% of the difference in response among segments. A clear winner!
If we look at the individual impact of Monetary versus the
individual impact of Average Order inside a 95% statistical confidence window,
we see Average Order is always positive.
That means the higher the average order, the higher the expected
response rate.
Monetary, on the other hand, could have either a positive or
slight negative impact on response rate, if we look at it in the same 95%
confidence window. In other words, statistically speaking, Monetary did NOT
improve our response prediction. To
confirm this we left Monetary out of a regression equation and just used
Recency and Frequency. R-Squared went UP
to 0.76!
So far, that is just looking at response alone. If we look at Dollars-per-Name, we tip the
scales even more in favor of RFA. RFM
did well predicting Dollars per Name with an R-Squared of 0.79, but RFA did
extremely well with an R-Squared of 0.91.
91% of segment to segment variation explained – that is very, very
powerful.
Looking at slope in the regression, there was a wide range
within 95% confidence for Monetary, varying by a factor of seven from low to
high. But the slope for Average Order
varied only from 0.65 to 1.00, so it fell in a fairly narrow range. That is due
largely to the high correlation in expected order amount with previous average order.
While the advantage of tailoring offers based on average
order is obvious, even with the same offer to all segments Average Order statistically
has a substantial advantage over Monetary in predicting both response rate and
dollars per name.
Which one do you use?
