Use Recency Metrics to Make More Money on Your Promotions
First published: crm-forum.com "Speed
Up CRM ROI using Behavioral Metrics" 5/22/01
Jim's Intro: This article outlines a very simple method for setting
up an effective customer retention program in an environment where
product discounting is used to drive sales. The program is quite
suitable for "lights out" automation and has two primary
Systematically maximizes response while minimizing discount costs,
increasing profits in the short term
Targets the right offer to the right customer at the right time,
reducing "e-mail overload" and increasing customer value in
the long term
Recency and Response
Recency has a long history in database marketing, and has proven to
be predictive repeatedly across many types of customer behavior.
The activity you track for Recency could be purchases, visits,
downloads, log-ins - almost anything that requires an
"action" of the part of a customer. Customers engaging
in multiple actions could be assigned a Recency metric for each
action. For example, the
customer could be very Recent on page views but not very Recent on
purchases. This would
imply the customer is likely to visit again but is becoming less
likely to purchase – just the kind of customer you should make an
offer to before they stop coming back to your site.
You will generally see response rates to a promotion asking for a
specific action (purchase, visit, click a link) fall as a function of
Recency - the number of weeks or months since the customer last
engaged in the activity you are trying to encourage. This
relationship is a smooth curve and quite predictable once you
establish the "slope" of it for your business.
Response rate by Recency might look like this:
Customer inactive for 1 month,
Response rate = 20%
Customer inactive for 2 months,
Response rate = 10%
Customer inactive for 3 months,
Response rate = 4%
Customer inactive for 4 months,
Response rate = 1%
The absolute response rates will be different depending on
the business, media used, and offer, but the relative response
rates will follow a decelerating curve as shown above, that is, the
less Recent the customer, the more dramatic a drop in response rate
you will get to your request for an action from your customers.
In terms of using this information for promotions, you will find
some point along the curve where you will get "breakeven",
meaning the cost of the campaign will equal the profits or benefit
generated. For example, let's say you offer a discount, gift, or
other incentive in your retention / lapsed customer campaign and need
a response rate of at least 4% to pay back the campaign cost.
This is your breakeven point.
The implication for this 4% breakeven campaign contained in the
Recency information above is this: don't bother to promote to any
customer who hasn't engaged in the activity you are trying to
encourage for over 3 months, because you're wasting your money.
Response will be too low to pay back the cost of the campaign
with any customer who has been inactive for over 3 months with you.
This Recency effect is
very stable over time, allowing you to predict in advance what
response to a campaign will be, once you do this
"establishing" campaign to see what your response rate is
for any particular offer. Recency will predict average response
rate for any specific combination of offer and media used.
You can save a tremendous amount of money by
forecasting your response by using Recency, and not promoting to
customers unlikely to be profitable.
Set up and execute a Recency test.
Classify customers in 30-day Recency segments by the last date of
the activity you want to profile for Recency. If you want to profile purchases, customers could be
segmented by date of last purchase:
In the past 30 days
31 – 60 days ago
61 – 90 days ago
91 – 120 days ago
121 – 150 days ago
151 – 180 days ago
181+ days ago
Take a 10% random sample of customers from each segment (every 10th
person in the segment), and send all of them a promotion with the
same offer, say 20% off any purchase in the next 30 days.
Look at the response rate by these 30-day segments.
You will find response falls off significantly as you look at
Recency segments further back in time.
If you repeat the test using the same offer to a different
sample of each 30-day segment, the response rate by segment will be
very close to the response rate by segment in the first test.
This kind of stability allows accurate predictions of marketing
ROI before promotions are even sent out to customers.
Recency and Offers
The response rate in any
one of the 30-day segments above will be influenced by the value of
your offer, and both response rate and the cost of the offer have
significant impact on the profitability of your campaign to any one
As offer value increases,
so does response rate, and so do costs. Ideally, you want to find the ideal mix of response rate and
offer value creating the highest profitability for each segment you
You can use Recency to
"ladder" the promotional discount, gift, or incentive value
offered in a promotion, boosting overall response while cutting
expenses by minimizing discount or other incentive costs.
purchases as an example, and say you usually e-mail all your
customers a 10% discount when you do a promotion. If you were
using a Recency ladder approach for this purchase incentive, you might
apply your discount strategy this way:
Customer inactive for 1 month,
Response rate = 20%, discount = 5%
Customer inactive for 2 months,
Response rate = 10%, discount = 10%
Customer inactive for 3 months,
Response rate = 4%, discount = 15%
Customer inactive for = 4 months,
Response rate = 1%, discount = 20%
Using this approach, you are allocating the most "bang for the
buck" discount-wise where you need it most - the least
Recent, lowest response customers, and pulling back on some
discounting where you don't need it as much - the most Recent, highest
Since your most Recent customers are most likely to respond, you
can back off on their discount and you reduce the cost of giving
discounts to customers who “may have bought anyway without a
discount”. You then
reallocate this discount money to where it is needed most – boosting
the response rates of those much less likely to respond - the less
Recent customers in the database.
Your response rates will vary depending on the offer, media used,
and your business. You
have to test these ladders with different combinations of offer and
media to find the optimum profitability for each Recency segment. The interesting and quite useful benefit of this approach is
the "automatic" overall customer retention effect discount
Using a ladder of this type means your promotional discount budget
is automatically working harder and harder to keep a customer
active with you as they drift further and further away from you.
The less Recent a customer is, the less likely they are to buy or
visit again, and by using a discount ladder you are counteracting the
customer LifeCycle (the tendency of customers to leave you over time)
with stronger discounts as the defecting customer behavior plays out.
If a most Recent customer does not respond to the 5% offer, as they
get less Recent, they automatically get offers rising in value, and at
some point, many will take advantage of an offer. The customers
who run through this system without taking any offers were likely lost
to you as a customer already, and not worth the extra expense to try
and keep promoting to them.
Set up and execute a discount ladder test.
Pick any one of the segments from your Recency test above and now
test discount level for the segment.
Let’s say you used a 20% discount in the first test.
Pick a segment (say 91 – 120 days), and create a 20% random
sample of the segment (every 5th customer) divided into 4
equal test groups. Send
each test group a different discount - say 5%, 10%, 15% and 20%.
Look at your response rates and calculate the profitability for
the 91 – 120 day segment at each discount level.
You will find your result looks similar to the following table:
Discount Test :
91 – 120 Day Recency Segment
As you can see, the most profitable offer to the 91 – 120 day
Recency segment is 15% off. If
you offer 20%, you get a higher response rate but lower profits; any
offer under 15% significantly diminishes response rate.
Repeat this test for each Recency segment, and you will
find the most profitable discount rising as the customer becomes less
Recent, creating your discount “ladder”.
Benefits of this
When you implement
your promotions based on a Recency / Discount ladder, as customers become less Recent
and therefore less likely to respond to a promotion, they will be
automatically offered a higher discount – one that maximizes profit
for each Recency segment the customer passes through. Ladders
are in effect a "lights-out" customer retention program
suitable for automation.
There is a subtle but
important side benefit to using a Recency / Discount ladder approach
to manage e-mail efforts. Instead of blasting out indiscriminate
offers to the whole customer base, taking a ladder approach more
closely matches the offer value to the "attitude" or point
in the LifeCycle a customer has
reached. Following the mantra of Permission
Marketing, this is called being "relevant", and will
tend to increase open rate and response as customers begin to put a
higher value on your e-mail relative to to other offers they may get.
In addition, as e-mail
clutter and execution expense increase, response will fall and profits
will decrease as customers get tired of receiving multiple
promotions. Over time, you will find it is simply more
profitable to e-mail customers less often, because you know for a fact
the most profitable offer to make and when to make it based on the
Recency / Discount Ladder. Using this approach will generally
help you rise above the clutter by sending fewer, higher impact
The data points you gather
from determining how Recency affects response and offer are extremely
valuable to the data mining effort because they are based on actual
customer behavior rather than “softer” attributes like
demographics. The basic
parameters of Recency, response, and offer represent actual
bottom-line financial impact to the enterprise.
When you include variables with a known financial impact in
your data mining process, the predictions and correlations output by
the miners can actually be used to increase the profitability
of the business.
This approach to creating
a customer retention program is clean, simple, and easy to implement.
And if you don't have any formal customer retention program in place,
much better than what you're using now!
Down book teaches you step-by-step many more of these simple, tried and true methods for making
more money marketing to your customers based on their behavior.
What would you like to do now?
the book with Free customer scoring software at:
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Customer Marketing Models and Metrics (site article