Retention Metrics: Retail and Wireless
# 47: 7/2004
Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention, Loyalty, Defection
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In This Issue:
# Topics Overview
# Best Customer Retention Articles
# Question - Retention Metrics: Retail?
# Question - Retention Metrics: Wireless?
Hi again folks, Jim Novo here.
This month we're focusing on how to create retention metrics and
use them to prove out the ROI of your programs. We have one
example from Retail and one from Wireless.
I've tossed in a couple of great article links, one on what goes
wrong with loyalty programs and one on how to calculate LifeTime
Value. With that, we're deep into the topic of customer
retention for this issue.
Let's do some Drillin'!
Best Customer Retention Articles
Getting loyalty programs right
July 22, 2004 SearchCRM.com
Hmmm - lots of info on what can go wrong with a loyalty program but not much
help on what to do about it. Check out the article below for some
suggestions, and call Kobie Marketing if you
need some help either making your existing loyalty program profitable or
creating a new one. It is possible to build financial models that will
drive program profitability if you know what you
How to Compute Customer LTV
July 28, 2004 DM News
Arthur Middleton Hughes tees up again with a simple explanation of how to
calculate LifeTime Value. I agree with him, it's
pretty darn simple, and I always wonder why people have so much difficulty with
it. Besides, no matter how you calculate it, what matters in retention
work is the LTV of a segment or customer relative to another; the
absolute number is only useful to determine the allowable cost to acquire
segments or customers.
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Questions from Fellow Drillers
Retention Metrics in Retail
Q: May I ask you one more question?
Q: In your book you don't go into too much detail on
A: Yikes! And I thought the **entire book** was
about measuring retention!
Q: ...but in your mind, for an online retailer, what is the
best way to gauge retention in its most basic, and simplest
form? % of orders that are from repeat buyers? % of orders
in month 2 who are repeaters that first bought in month 1?
A: I would take direction on this from the actual results of
campaigns. Basically, at the point a customer no longer
responds, they have defected. Perhaps this averages 3 months or
6 months after 1st purchase, and there will be category or price
segments within these "time" segments. Retention is
really measured by the defection.
Now, that's not to say that % orders from repeats or the other one
you mentioned are not valid, but I suggest you think about the
specific question you want answered by the metric you choose.
% orders from repeats, for example, is a common metric in mail order
but is often biased by campaigns, e.g. if you ratchet up customer
acquisition during a single month you poison your own metrics.
You can "reverse engineer" into it by looking for people
that have already defected and then find their start date, then take
the average length of time between start and finish. Let's say
you agree that a person who has not bought in over 12 months is
probably no longer a customer. Find all those people, find their
first and last purchase dates (exclude 1x buyers), calculate average
months between first and last purchase.
If this number is 8 months, then your average LifeCycle is 8 months
long, and your "active" customer base is therefore everyone
with a purchase in the last 8 months. Divide this number by
total # of customers, and you have your retention rate.
Oh yea, I forgot, you said "simple". OK, for
simple, the "Wall Street" standard used throughout the
direct retailing industry is "12 month active". What percent of
customers have made at least 1 purchase in the past 12 months?
That is retention rate.
The problem with this approach is it begs for a "last
ditch" retention marketing effort at 12 months since last
purchase. But if the LifeCycle is really 8 months long, you will
be late and the program will lose money.
That's why I always
prefer to match the metrics to the actions. It simply doesn't
make sense to me to know customers have an 8 month LifeCycle and then
use a retention metric called "12 month active".
should at least use "8 month active", if you have the
resources to figure out it is in fact 8 instead of 12. The
"12 month active" is used as a default because it lines up
with the annual reporting of financial statements, but is often not a
true measure of customer behavior - it's simply
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Retention Metrics in Wireless
Q: I got the job of servicing the top users on our network. I must confess
I dazzled the interviewers with statistics from your Recency,
and Monetary model !!!
A: Congratulations! Well done.
Q: I know I should be paying you for this but I would like to
with you once in a while to tell you what's going on..? If you are not happy with this I really understand.
A: I'm fine with it, as long as it doesn't become a full time job!
Q: Here's my plan:
1. I would like to start by segmenting high users by usage,
2. Next I would like to profile demographics for each usage band
3. Next I will keep a Monthly tab on their recharges i.e. Frequency of
recharge in a month and Monetary value of recharges
4. A special helpdesk will be set up specifically for them (top users)
complaints hence we will have a log of their complaints. All their
complaints are to be resolved in four hours (if it is not network
5. We will communicate with them by calling twice a year (they don't like
being called too often) also we are thinking of sending birthday text
messages (it is a form of communication)
That is all I can think of for now. If you have any tips I would
really appreciate it. Thank You for helping me get this job.
A: You're welcome, glad it worked out!
I'm not sure how helpful the demographics will be, at least in
beginning, that would typically be a topic for "Phase 2".
Ultimately, you will be judged on the increased retention and related
profitability of these top users; you are going to have to prove what
you are doing is working. So it is very important to
establish exactly where you are with retention.
The usage bands idea is a good one, but that only gives you the
you need to then determine defection rates for the segments, since a
in this defection by usage band metric is probably the best proof you will have that your
is working. And once you have the defection rate and the value of a
customer in the band, you can get to program profits.
For example, let's say you find out there are 1,000 customers in
the "best" usage band, they are worth $100 in profits
annually, and their 1 year defection rate is 30% - a year after these
1,000 customers start, there will only be 700 of them left. You
implement your programs, and the annual defection rate drops to 20%,
meaning there are 800 customers left at the end of the
This means your programs are responsible for retaining 100
customers with an annual value of $100 - you generated an additional
100 x $100 = $10,000 to the gross profit line before the cost of your
programs. If your programs for these 1,000 customers cost
$2,000, you increased cash flow by ($10,000 - $2000 = $8,000 or $8 per
customer ($8,000 / the original 1,000). This is the kind of
"proof" you are looking to have - the kind of proof that
will make management really sit up and notice!
So, go back 2 years or as far back as you can, and look
the customers who joined, and segment by usage bands / recharges.
this question: what percent of the customers in each band are still
customers after 1 year, and what percent are still customers today?
are your baseline 1 year and 2 year (if you can go back that far) retention rates for each band. Your mission is
to improve these rates, and in so doing, you will increase cash flow /
Then perhaps you can look at demographics to help design your
I would do something else first, if possible. Interview defectors, and
find out why they are leaving. You can interview Recent
each band, you don't need to talk to the ones from 2 years ago. What they
tell you will be more important to designing your program than any
demographics (save geography, which will highlight potential service problems).
should be really most interested in what people do and why, rather
they are, because behavior predicts behavior, demographics do not.
That's it for this month's edition of the Drilling Down Newsletter. If you like the newsletter, please forward it to a friend - why don't you do this now while you are thinking of it? Subscription instructions are at the top and bottom of the newsletter for their convenience when subscribing.
Any comments on the newsletter (it's too long, too short, topic suggestions, etc.) please send them
right along to me, along with any other questions on customer Valuation,
Retention, Loyalty, and Defection right here.
'Til next time, keep Drilling Down!
- Jim Novo
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