LTV, RFM, LifeCycles - the Framework
Drilling Down Newsletter #110: 5/2010
Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention, Loyalty, Defection
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Hi again folks, Jim Novo here.
LifeTime Value. The RFM model. Customer
LifeCycles. Control Groups. Program Lift. Sometimes it gets a bit overwhelming when you're trying to crack
this customer marketing optimization thing for the first time.
And so it is with our fellow Driller this month, who is pursuing a
noble cause - the universal measure of success for customer marketing
and experience programs across the company. And he's on the
right track by thinking changes in customer value are key.
But somewhere along the way, he's become entangled in all the
acronyms and can't get to the light. So we're going to toss him
a rope and see if he (and perhaps you?) might begin to make sense of
these powerful customer value measurement and management tools.
Up for some Drillin'?
Questions from Fellow Drillers
LTV, RFM, LifeCycles - the Framework
Q: I visited your website because I am trying to understand how to develop a
customer LifeTime Value model for the company that I work at.
The reason is we are looking at LTV as a way to standardize the ROI
measurement of different customer programs.
Not all of these
programs are Marketing, some are Service, and some could be considered
"Operations". But they all touch the customer, so we
were thinking changes in customer value might be a common way to
measure and compare the success of these programs.
A: Absolutely! I just answered a question very much like this the other day, it's great
that people are becoming interested in customer value as the cross-enterprise common denominator for understanding success in any
If I am the CEO, I control
dollars I can invest. How do I decide where budget is best
invested if every silo uses different metrics to prove success?
And even worse, different metrics for success within the same silo?
By establishing changes in customer value as the platform for all
customer-related programs to be measured against, everyone is on an
equal footing and can "fight" fairly for their share of the
budget (or testing?) pie. By using controlled testing, customers
can be exposed to different treatments and lift in value can be
compared on an apples to apples basis - even if you are comparing the
effect of a Marketing Campaign to changes in the Service Center.
But are you sure you want to
use LifeTime Value for this application?
Q: From what you stated on your website, I will not
be able to develop a LifeTime Value model unless I understand the customer
Lifecycle. The customer lifecycle is something that I could get
a good understanding from using doing a RFM
My question is, once I complete the RFM analysis, what would be my next steps in developing a
customer LifeTime Value model? At this point in time, the hardest thing
that I am trying to wrap my head around are the variables to include in
the model. I visited Arthur Middleton Hughes' website:
and he suggests the following variables (download spreadsheet, if
Jim, could I simply use those variables going forward to calculate the
LifeTime Value of a customer at my company? I would appreciate any
assistance you may be able to provide to me on this matter. Thanks.
A: Well, that's a big tangle of related
issues! Let's unpack first, then answer the
question. First, the relationships between these ideas:
Lifetime Value versus Lifecycle - LTV is a number, LifeCycle is a
trend over time that contains trigger events. You don't need the
LifeCycle to develop (calculate) LTV, you need the LifeCycle to
most efficiently and profitably act on and manage LTV issues.
RFM versus Lifecycle - RFM is a tactical model that is a
"snapshot" of customer state at a point in time, the
customer's likelihood to respond. Frequently used names for
these customer states include active, lapsing, lapsed,
defected. Lifecycle is the "movie" one might put
together from these snapshots of RFM states; the migration from one
customer state to the next are the Lifecycle trigger points.
Now, let's make sure we understand each one of the ideas:
Strictly speaking, LTV is not a very flexible concept and is best used
for determining how much you can spend to acquire a customer and still make a profit.
This is the equation that Mr. Hughes has provided, a man by the way that I have a lot of respect for. His model is
quite detailed and useful for the purpose of finding break-even cost to
acquire a customer.
To use Arthur's LTV model, you have to find historical
values and plug them in. You could assume nothing will change
and the LTV of certain segments of past customers will be the same;
this is great for "benchmarking", for example.
However, this approach is not measuring LTV, it's predicting
LTV based on historical data. This is fine, and a valid method for
certain types of analysis.
But, the premise of your question is you will be testing, and
testing implies something new will occur. So while you could use
LTV to estimate results, you'd have to wait quite a while to prove the
results one way or another. LTV is really "forensic"
in this way - you won't know the final answer until the customers
You could certainly go back 2 - 5 years after the tests, and prove
one group had higher LTV than another, but that's not typically a very
useful approach when doing testing.
RFM (Recency, Frequency, Monetary)
RFM is a predictive model that takes a "snapshot" of the
customer base and gives you a score for each customer, a prediction of
likelihood to respond relative to all customers.
By itself, RFM doesn't tell you if you are making money or not. It is used to classify the
"state" of customers at a point in time, usually for
targeting purposes - are they active, lapsing, lapsed, defected?
In other words, it's a customer segmentation tool.
For example, RFM could be used to choose your test and control
groups for a campaign using Lift measurement - you would want test and
control to have the same range and balance of scores. In fact,
one of the tragic campaign measurement mistakes people often make is
not taking into account the likelihood to respond when selecting test
and control groups, resulting in biased test results.
One of the great features of RFM is the idea of "ranking"
customers relative to each other; this gives allocation of budget and
success measurement a standard to follow. A
single customer can have many different scores over the course
of their LifeTime, with the likelihood to respond the score at a
specific time. In fact, if you looked at RFM scores over time for a single
customer, you would have a clear understanding of the LifeCycle of a
customer - the most powerful segmentation available in terms of
message and offer targeting.
The problem with looking at RFM scores over time is complexity; the
beauty of individual customer scores at a single point in time becomes
unbearable when you are talking 125 different scores on 50,000
customers over 6
months. That's the internal or analytical problem. Externally, this kind
of information is extremely gnarly to present and explain to senior
managers, it's presentation hell.
The way I solve this problem is with a tool I call LifeCycle Grids.
The Grids takes the same
fundamental drivers used in the RFM model and instead of ranking, uses
thresholds or "hurdles" to classify customer states.
This creates a standardized customer LifeCycle "dashboard"
so comparisons of customer value between different segments can be
made more easily. It works for both short and long term
observations and is easy to represent either numerically or
graphically. And because it uses finite thresholds for activity
rather than ranking, the same calculations that create the dashboard
can be used to actually drive or trigger actions.
So the dashboard is actually the controller as well. This is
extremely beneficial in terms of linking presentations, plans, and
results. People can literally point to a segment on the LifeCycle
framework and say, "Let's deliver message X to each person from segment Y who enters this cell" and see the results right where they pointed
when the dashboard is updated.
Once you test some ideas and find out which approach generates
incremental profits for a cell in the Grid, you can automate delivery
of the program as customers enter that cell of the Grid. This is
the classic "sense & respond" approach to marketing
communication - right message, right person, right time.
The LifeCycle Grids are demonstrated in a lot of detail for different applications in
the series here,
but probably of most interest to you as it relates to customer
analysis, see here.
And now, to answer your question:
Which approach above, if any of these, would be best for
standardizing measurement of ROI in widely diverse customer programs?
LTV would be appropriate if what you want to know is breakeven cost
to acquire. Since we are talking about customer programs, I
doubt that's what you want to use. Plus, if you want a hard
number rather than a prediction, you could be waiting a long time for
RFM is a "snapshot" model and so not really suited to
long-term studies of customer value.
Customer Lifecycle models are more likely to be involved in the
execution of a program, not the success measurement. LifeCycle
tracking could be (and often is) used to predict the financial
success of campaigns before they have run their course, but you're
only predicting success, not delivering numbers into an ROI model the
CFO would accept as "fact".
Answer: None of the above.
What you need is an approach designed for the task, which in this
Lift Measurement or Near-Term Value
Lift is a measure of the performance of a
test group of customers compared with a control group of similar customers who are not exposed
to the test. You can read more about control
groups here. In the analysis of value contributed by each
group, many of the same values from Arthur's LTV model are used - product
margin, costs of program, fulfillment costs, payment parameters, etc.
However, if you are talking about a program to existing customers,
cost to acquire is probably not relevant, though you might use source
(campaign) to segment your test approach.
Lift is typically measured at intervals, say every 30 or 60 days,
to see how test versus control populations are tracking, and can
continue after the test is over to pick up residual value
created in the customer. However, this is not a Lifetime Value measurement,
Lift models measure incremental contribution to LTV created by
the Marketing, Service, or Operations program execution.
This means if you get lift from program test versus control, when
you go back 2 - 5 years later and measure true rather than predicted
LTV - after the customer has defected - you should in fact see the LTV
in the test group higher than in the control group, barring any
radical downstream difference in customer experience between test and
control. In this way, Lift models are actually predictive of
changes in LTV. That's why the output of Lift models is
sometimes referred to as the measurement of "Near-Term
Value" and used much more often than the forensic approach of
waiting for customers to defect.
All the above are core concepts in customer value measurement and
LTV is a measurement of net financial value contributed by a
customer, and Lift measures are like a "time slice" of
the overall LTV curve.
LifeCycles are a management framework for programs designed to
affect LTV, and models using Recency, Frequency, and Monetary are used
to look at a "time slice" of the LifeCycle.
LTV can generally be increased in two ways: by creating more value
during the existing LifeCycle, or by extending the LifeCycle.
Marketing (including Product) is typically used when doing the first, Service
and Operations - customer experience and satisfaction - are largely what
So it is completely appropriate to establish a unified approach to the
measurement of customer programs intended to increase the value of a
customer across all these disciplines, in
order to ensure the allocation of scarce resources to highest
and best use.
A great question, and for a great cause!
Have a question on Customer Valuation, Retention, Loyalty, or Defection?
Go ahead and send it to me here.
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'Til next time, keep Drilling Down!
- Jim Novo
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