How To Measure Future Customer Value and
Manage it with E-mail
First published: Business Intelligence Toolbox, "How
To Measure Future Customer Value and Manage it with High ROI E-mail"
Folks, here's an honest to
goodness, classic customer retention marketing program, suitable for
automation, complete with metrics and testing methodology. This is
the first program I developed for interactive customer retention at Home
Shopping Network, averaging a 135% net ROI at 60 days, month after month.
This is the exactly the kind of work you can do "pre-CRM" to
determine whether CRM techniques will increase the value of your customers
and by how much. Going through the following process will also
identify any special needs you may have to consider when choosing a CRM
example of the kind of information found in the
book, similar in format, using examples and a step by step
approach. This version is not as detailed as typical explanations in
the book (attention spans are short on the Web). If you don't have a
relationship marketing or customer retention techniques,
you might take this site's concept tour and then
return to read this article.
Customer Retention and Valuation Concepts
Have you ever heard somebody refer to his or her customer list
as a "file"? If you have, you were probably listening to someone who
has been around the catalog block a few times. Before computers
(huh?), catalog companies used to keep all their customer information on 3 x 5
Theyíd rifle through this deck of cards to select customers
for each mailing, and when a customer placed an order, they would write it on
the customerís card. These file cards as a group became known as
"the customer file", and even after everything became computerized,
the name stuck.
Who cares? It happens that while going through these cards by
hand, and writing down orders, the catalog folks began to see patterns
emerge. There was an exchange taking place, and the data was
speaking. What the data said to them, and what they heard, were 3 things:
1. Customers who purchased recently were more
likely to buy again versus customers who had not purchased in a while
2. Customers who purchased frequently were
more likely to buy again versus customers who had made just one or two
3. Customers who had spent the most money in
total were more likely to buy again. The most valuable customers tended
to continue to become even more valuable.
So the catalog folks tested this concept, the idea past
purchase behavior could predict future results. First, they ranked all
their customers on these 3 attributes, sorting their customer records so that
customers who had bought most Recently, most Frequently, and had spent the most
Money were at the top. These customers were labeled
"best". Customers who had not purchased for a while, had
made few purchases, and had spent little money were at the bottom of the list,
Then they mailed their catalogs to all the customers, just
like they usually do, and tracked how the group of people who ranked highest in
the 3 categories above (best) responded to their mailings, and compared this
response to the group of people who ranked lowest (worst). They found a
huge difference in response and sales between best and worst customers.
Repeating this test over and over, they found it worked every time!
The group who ranked "best" in the 3 categories above always had
higher response rates than the group who ranked "worst". It
worked so well they cut back on mailing to people who ranked worst, and spent
the money saved on mailing more often to the group who ranked best.
And their sales exploded, while their costs remained the same or went
down. They were increasing their marketing efficiency and effectiveness by
targeting to the most responsive, highest value customers.
The Recency, Frequency, Monetary value (RFM) model works
everywhere, in virtually every high activity business. And it works for
just about any kind of "action-oriented" behavior you are trying to
get a customer to repeat, whether itís purchases, visits, sign-ups, surveys,
games or anything else. Iím going to use purchases and visits as
A customer who has visited your site Recently (R) and
Frequently (F) and created a lot of Monetary Value (M) through purchases is much
more likely to visit and buy again. And, a high Recency / Frequency /
Monetary Value (RFM) customer who stops visiting is a customer who is
finding alternatives to your site. It makes sense, doesnít it?
Customers who have not visited or purchased in a while are
less interested in you than customers who have done one of these things
recently. Put Recency, Frequency, and Monetary Value together and you have
a pretty good indicator of interest in your site at the customer level.
This is valuable information to have.
Assuming the behavior being ranked (purchase, visit) using RFM
has economic value, the higher the RFM score, the more profitable the customer
is to the business now and in the future. High RFM customers are most
likely to continue to purchase and visit, AND they are most likely to respond to
marketing promotions. The opposite is true for low RFM customers; they are
the least likely to purchase or visit again AND the least likely to respond to
Measurement: How do I know when I have a
customer retention problem?
For these reasons, RFM is closely related to another customer direct marketing
concept: LifeTime Value (LTV). LTV is the expected net
profit a customer will contribute to your business as long as the customer
remains a customer. Because of the linkage to LTV, RFM techniques can be
used as a proxy for the future profitability of a business.
High RFM customers represent future business potential, because the customers
are willing and interested in doing business with you, and
have high LTV. Low RFM customers represent dwindling business opportunity,
low LTV, and are a flag something needs to be done with those customers to
increase their value.
RFM scoring of individual customers is a catalog and TV shopping technique used
to select which customers can most profitably be promoted to. There is a more simplistic application of RFM
web sites can use to easily track the quality of overall customer retention, without going
through the effort of RFM scoring individual customers. This tracking can
be used to measure customer retention and trigger profitable customer
retention promotions. The basic technique creates a platform for learning
the key customer behavior metrics needed to manage customer retention, and
provides a foundation for building a more comprehensive effort.
Here's a real life story I
have seen repeated over and over. Many companies judge their best
customers by looking at Frequency of activity, either purchases or page
views. They set a threshold, like 25 purchases or 100 page views,
and then count the number of customers who have achieved this goal.
As long as this number of customers keeps growing, they think the business
is on track and doing fine.
Then someone with experience
in database marketing does an analysis, and the company finds out that 60% of
these customers haven't purchased or visited in over 12 months! So they
desperately try to
e-mail these people offers and get them to come back, but get truly lousy
response rates. The customer relationship is already over, and the company
has lost a ton of their best customers because they have no formalized,
proactive customer retention program. These defected best customers
are a very troubling sign for the future value of the business.
Customer tracking by Frequency
is a rear-view mirror, because it doesn't take into account the future
potential of a customer to contribute to revenues. You have to track
customers by Recency to predict future value, because Recency is the
strongest indicator of future customer activity.
If you have been tracking
the loyalty of your customers as a group using the Drilling
Down visual method, the equivalent of the above scenario would be
seeing the Frequency Hurdle Rates
rising while the Recency Hurdle Rates are falling, a classic sign of
failing customer retention.
You can act to slow or prevent
some of this customer attrition by implementing a basic customer retention
measurement and management program using e-mail. The following program uses
customer Recency to categorize customers and create a framework for
profitability measurement and automation of the retention program.
1. Choose the activity
you wish to measure and manage the future value of: purchases, page views, downloads, click-throughs,
whatever "action metric" is important to you and the business
2. Choose a time metric
to define customer Recency in this activity. Blocks of 30 days are pretty standard and also
tie in with other monthly reporting and operational cycles when
databases might be updated. Over-achievers with significant database
horsepower might use weekly data, especially if you are looking at visits
and you are a time-driven site, for example, a site focused on news.
You want the freshest data you can have
to use these techniques; fresher data = better results.
3. Identify customers who have engaged in the activity you are
measuring and determine the date these customers most Recently
engaged in the activity. If you
are using 30 day blocks of time, you would identify customers last engaging in the activity in the past
30 days, in the past 31 - 60 days, in the past 61 - 90 days, and so
forth. Go out at least 6 months in 30 day blocks. After 6
months, you can have a count for
"everybody else" who has not engaged in the activity for over 6
4. Print your
report. This is a report of the number of customers who last (most
in the specific activity a certain number of days ago. A customer is
represented only once in any of the 30 day blocks below; remember, we are looking
only at the most Recent date the customer engaged in the
activity. An example using purchase Recency could look like this:
Table 1 - Customer Recency
= 30 days
- 60 days
- 90 days
- 180 days
Read: "5,786 customers
last purchased within the past 30 days, 4,356 customers last purchased 31
- 60 days ago, 3,872 customers purchased 61 - 90 days ago..."
or it might look like this:
Table 2 - Customer Recency
= 30 days
- 60 days
- 90 days
- 180 days
Read: "1,198 customers
last purchased within the past 30 days, 2,577 customers last purchased 31
- 60 days ago, 3,872 customers purchased 61 - 90 days ago..."
Which of the
two tables above represents the business with the most future
potential? Which table represents the business where the most customers are likely to
continue engaging in the activity being profiled?
If you guessed Table 1, you're
right. Both these tables represent businesses with a total of 24,141
customers, but there are many more Recent customers in the
Table 1 business then there are in the Table 2 business. Since the
more Recent a customer is, the more likely they are to repeat an activity,
the business in Table 1 can expect more business out of their current
customers in the future than the business in Table 2. The business
is Table 1 has much better customer retention, and the customers on
average have higher future value. Real world visual examples of
visitor Recency comparable to the ones above can be found here.
OK, now what? Well, if
you do this exercise every month, you can compare trends in the 30 day
customer Recency blocks and watch the customer Lifecycle
play out before your eyes. In a healthy business, the number of
customers in the most Recent block should grow faster than the numbers in
the other blocks. If the number of customers in the most Recent block is
shrinking while the numbers in the other blocks are rising, you've got a
customer retention (future value) problem, and need to take action. Note: You don't
want to see growth in the 180+ block at all, but it's inevitable, and the
longer you are in business, the larger this number will grow. You
should be most concerned with managing (reducing) growth in the blocks from 60 days to 180
days, where you can still take effective, profitable action.
Management: How do I
about customer retention problems?
Customer Retention management
involves trying to drive as many customers as you can into the most recent
customer block as profitably as possible.
Think of it this way.
You want customers to remain active and Recent with you so they are
generating revenues. Some customers will do this without any special
marketing attention from you. Others will need an incentive.
High ROI customer retention programs focus on only the customers who need
an incentive. By approaching retention this way, you avoid spending
precious marketing dollars where they are not needed and can increase them
where they are needed. In other words, if you allocate the budget
away from customers with a low likelihood to defect (the most recent
customers), you can put more money per customer to work against customers
who are more likely to defect (more distant customers).
Generally, the sweet spot for
customer retention activity, the point at which spending money retaining a
customer generates the highest ROI, is somewhere in the middle of our chart
above. Spending money on very Recent customers or very distant
customers is not usually profitable. How do you find the sweet spot
for your business?
Check out Table 3 below:
Table 3 - Response Rate by
= 30 days
- 60 days
- 90 days
- 180 days
If you e-mail the exact same
offer (say, $3 off anything on the site, or a free download, or promotion
of new content) to all the customers in this
table, you will get a response rate grid similar to the one
above. What's important to understand here is not the actual numbers, because they will vary
depending on the offer and media used.
What is important to
understand is the relative differences in the response rates.
You can expect the most Recent customers to have a very high response
rate, and the response rate to drop sharply as customers get less
Recent. The most Recent customers will generally be 8 to 40 times
more responsive than the least Recent customers.
So how do you turn all this
into something you can use? You create a customer retention test,
measure the results, and turn the test into a monthly customer retention
e-mail promotion. It's a bit complex to set up the first time test,
but once you complete the test promotion, you will either have your
systems set up to measure the results every time, or you can just run a
test periodically to make sure your results are still on track. This
process can be totally automated.
Here's what you do:
1. Select an equal
percentage of customers from each of the 30 day blocks on our Recency grid
above. A good number for a test like this is a 10% random sample
of the customers in
each block. It's very important the sample is truly random.
This sample will receive your promotional e-mail; they are called the test
2. Make sure you can identify
every customer (not just the ones selected for the promotion) by
their Recency block before you do the test. Either tag their
record somehow or make sure you can determine when they last engaged in
the activity being promoted before the date the test e-mail is sent.
Customers not receiving the promotion (90% if you used 10% for the test
group) are called the control group.
3. E-mail the exact same
offer to each 10% of the block group as a single promotion, making sure all other potential
variables are equal. For example, don't send the e-mail to different
Recency blocks on different days of the week. Tabulate response by Recency
block, including total sales and cost of goods sold. If you can't
get the actual cost of goods sold, use the average for your business.
Pure content businesses would
look to potential ad sales generated for the "sales" metric in
the following formulas. If you perceive you have "no costs" to doing a
promotion, there is no need to do the following ROI analysis. May I
humbly submit you might
consider offering something of value (cost to you) if you're serious about
getting defected customers to return to your site, for example, great new
content you have to pay someone to write. The beauty of this method
is, after the test, you are only making an offer to those customers
you are likely to lose and most likely to get back, so you can
afford to spend more per customer on the promotion.
4. Use the following formula to look
for your sweet spot. You want to do this calculation for each 30 day
Recency block in the promotion:
Start with Sales Generated by Test Group
minus Cost of Goods Sold to Test Group
minus Cost of E-mail Promotion to Test Group
minus Cost of Discount (or other incentive, or content)
to Test Group
Equals Promotion Profit by 30 day customer Recency Block
Divide by The number of customers in the Test Group
Equals Promotion Profit per Customer by Recency Block
5. Now, calculate the
profit per customer in each Recency block during the time period of the
promotion who did not receive the promotion. Note: this
could be done using a 10% random sample of these people, if you
wish. Given a choice, I'd use the whole group.
Start with Sales Generated by Customers NOT in Promotion
minus Cost of Product
Equals Profit by 30 day customer Recency Block
Divide by The number of customers in Control Group by
Equals Profit per Customer by Recency Block
6. Compare the profit
per customer by Recency block of customers in the Test Group versus
customers in the Control Group. Subtract
the profit per customer in the Control Group from the profit per customer
in the Test Group. This
difference represents the profit due to your promotional efforts, the
profit existing because you spent money on one group of customers versus
the other group of customers where you spent no money and these customers
did not receive any promotion.
It should look something like this:
Table 4 - Profit per
Customer by Recency Block
< = 30 days
- 60 days
- 90 days
The most profitable 30 day
block (61 - 90 days for this example) in the promotion is your sweet spot. This is where you should
focus this customer retention promotion. Each month, select the
customers who have "rolled over" into this block and e-mail your promotion
For example, if the 61 - 90
day Recency block is the most profitable for you, each month, select all
customers who have not engaged in the activity you profiled (purchases,
page views, downloads, click-throughs, whatever "action metric"
you are tracking) for
61 - 90 days, and send them your promotion.
People who respond and engage
in the activity become "30 day customers" again, and are now
much more likely to continue in the behavior after the promotion.
Using this promotion over time, you will begin to shift your whole
customer base to a more recent status. Or said another way, your
business will start to look more like the one in Table 1
rather than the business in Table 2. You will be
increasing the future value of your customers and making money at the same
Once you do the work of the
initial test, this simple retention promotion becomes a very easy to
execute, regular program that can serve as the base for a building a
customer retention effort. Further testing of offers, discount
levels, and so on can be used to optimize the profitability of the promotion.
From there, automation of this promotion through the CRM engine or other
internal processes creates a highly efficient and effective "lights
out" promotion which automatically minds some of your customer retention
problems for you.
Note how linear the profits
are by Recency in the Control Group. This is why
Recency in the customer base is so important; the most Recent customers
are generally the most profitable customers. If you are wondering why the
most Recent customers were the least profitable when promoted to, you need to understand
the concept of subsidy cost.
Subsidy cost is the primary reason why the net profit difference between
the test and control groups (Test - Control in the table above) is similar to a Bell Curve
concept, rising then rolling over and
falling. Subsidy cost can be measured and best (usually most Recent)
customer programs designed to avoid subsidy costs. There is an
entire chapter devoted to this subject, with mathematical models
describing it, in the Drilling Down book.
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