LifeTime Value: Strategic or Tactical?
Drilling Down Newsletter #86 1/2008
Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention, Loyalty, Defection
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Hi Folks, Jim Novo here.
Many folks think the topic of Lifetime Value
is complex and not worth understanding. I disagree, of course,
but a recent series of interactions got me thinking about how some of
this attitude towards LTV might be a result of confusing Strategy and
Tactics. We will speak with a fellow Driller about this same
idea, and go through how the two fit together with respect to the
LifeTime Value concept.
We also have some material on the role Marketing can play in
Operations to drive higher Marketing / Company ROI; an article on VRU
optimization and a set by step plan for Marketers who want to raise
their Strategic profile by playing in this sandbox.
Best Marketing Productivity Articles
Listen Up! (VRU Optimization)
Speaking of the role Marketing should play in Operations, here’s the first article I have ever seen in a Marketing context about optimizing a VRU / IVR. This challenge is really very similar to optimizing a web site.
You have path, and traffic down branches of path. You have bounce rates and exits. You have the same “choices with correct context” issue that is the heart of designing good navigation and inline link text.
You have usability.
To access these and other reviews of recent articles on
Marketing Productivity with links to the original articles click
Sample Marketing Productivity Blog Post
Marketing through Operations
January 9, 2008
Customer-centricity is something companies want to embrace more than ever.
Companies can do this through a Chief Customer Officer, but why isn’t a Marketing exec taking the reins on this issue?
In direct marketing companies - where customer-centricity is not just a fad, but has a decades-long history - the Marketing folks know that Operations typically contains a goldmine of customer-centric Marketing opportunities.
Many of these opportunities come from problems with empathy and context - or for the more technical folks out there, “Usability”....Read
Questions from Fellow Drillers
LifeTime Value: Strategic or Tactical?
Q: I'm working with IT on building a new database and at the same time hope
to build in new query capabilities to allow me to understand our customer
lifetime value and build it accordingly. I've been tasked with putting
some math examples together for IT so they can understand what I'd be looking at and how CLV would be calculated.
A: I'm sort of lost on this because it sounds to me like you are looking to
forecast LTV; reality is that LTV is really only known after the customer
has defected. So, you can go back in time and say "the LTV of customers
acquired through this campaign is X" a year or two after the fact and use
that info going forward in the business.
Q: In addition to your Drilling Down
book, I also have read Sunil Gupta's "Managing Customer as
Investments". One thing that stands out for me is that Gupta stressed the importance of allowing for a discount rate and I
don't see any mention of discount rate in your examples.
A: Yes, a case in point for my comments above. The reason I don't mention NPV is I favor
a relative LTV approach; I just think it's more practical, and in my experience management is not really interested in the NPV approach when
discussing Marketing, they want to know about "cash I make now", not cash
you are forecasting in the future.
Said another way, to use NPV you have to have something to discount to the
present, meaning you are making a forecast of absolute LTV, and this forecast will probably be wrong because there are too many unknowns that
will occur, at least in B2C.
Q: This is the request from my IT rep....which leads me to believe that she
may have missed that Absolute vs. relative LTV conversation which we
had - or maybe I'm missing something. Once I have these examples we will be ready to begin our modeling.
Here is the formula I was going to provide IT for calculating LTV:
<bunch of equations and data>
A: Again, far from me to play IT expert here, but what I would concentrate on
when creating a new database is making sure you have all the data you need, making the acquisition of
that data as clean as possible, and creating a database that is as flexible
as possible. I don't really understand why you need to provide any
"LTV formula", because if the data and structure are right, you can run any
formula / query any time you want. You can both go back in time and forward
in time, and use different equations for each, the big difference being back
in time is "actual" and forecast is "predicted" and adjusted with NPV.
What I am thinking here is there may be some confusion
on both sides about the difference between using customer data /
valuation for Strategic versus Tactical purposes, so perhaps some more
info on that will help you and IT communicate on this project more
A Strategic use of customer valuation would probably
be fairly stable and something that could have static formulas created
for reporting purposes at the CEO level. You would probably use
the NPV approach and forecast absolute (a number) LTV.
For Tactical use of customer valuation in campaign management and driving Marketing ROI, you are better off not
messing around with NPV and looking at LTV from a relative (is it
getting better or worse) perspective.
Let's see if I can give you some practical examples of
what I mean.
The Gupta formula:
CLV = m(r/1+i-r)
m=customer margin or profit per period (year)
r= retention rate
is a marvelous macro equation to describe LTV when you are trying to
make strategic customer investment decisions.
He also provides modifications to this model for
different scenarios where retention rate or profit are variable, with
a final result that on average, the LTV of a customer is between 1 and
4.5 times the current period profit.
This is probably shockingly low to some people, but
you have to realize that at a 60% defection rate only 2.8% of
customers are left after 7 years; put another way, with a 12% discount
rate and 90% retention, the multiple on current period profit is just
4 for LTV.
This is a perfectly competent analysis and especially
powerful in it's simplicity; I use this kind of thing when talking to
CFO's, CEO's and other financially-oriented, strategically-focused
individuals. The plain fact of the matter is because of the
discount rate, any arguments about "how long a customer is a
customer" become moot, because the incremental value of the out
years is very small. This realization generally kicks C-Level
people in the gut and gets them thinking about "doing
Strategic customer value, in case it's unclear, would
be used for making Marketing decisions like these:
* What lines or services should we add?
* What marketing mix is optimal?
* Should we implement a loyalty program?
Formulas like Gupta's are also often used to value
companies for sale or estimating the value of a potential acquisition.
However, on a practical, tactical level, we know the
dangers of making database marketing decisions using the "average
customer", knowing there is a lot of behavioral skew, and that
there is a huge profit difference between best and not so good
So, when you're developing targeting ideas and just trying to see
where you make the most money so you can optimize customer campaigns, I think
his model is really overkill.
When I look at campaigns, I basically look at "breakeven" - will the net margin after all costs pay for the customer
acquisition or the incremental sales generated from the customer as a
result of the campaign? Once the customer segment hits breakeven, then it's all
upside profit from there, right? As long as you don't do something to destroy value (which
is certainly possible), LTV should remain the same or increase and since you are past breakeven,
financial liability is zero.
And that's when you start looking at the relative value - one segment beats breakeven by
$2 per customer, another beats by $20 per customer, then all else equal, I invest more in the campaign that beats by $20 and let it go -
it really doesn't matter what the end or "terminal" LTV of the customer is.
I made the right decision by allocating towards higher ROI so I don't need
to know absolute LTV, do I?
Put another way, I'm optimizing a system, trying to
make it better and better at generating profits. The end,
absolute numeric value number is really not relevant to me. If what I'm doing is always
adding incremental value, absolute LTV will take care of itself and can be measured as a separate exercise - if you have resources.
Next, we know these relative valuations can change over time.
To monitor further, you use a simple "sales to date" analysis to see if LTV is still
growing and a "% Recent" index to predict if sales will continue to grow.
Based on this longer-term idea, you make further adjustments to your
campaign allocation to optimize the system.
So for example, let's say the net $2 customers above are late bloomers and the
net $20 customers above are fast starters; you do this sales to date /
analysis at 3 months or 6 months and find out the $2 folks are now $30 with
great Recency and the $20 folks are still $20 with terrible Recency.
So you adjust your campaign allocations to reflect this change based on the real data, not
a forecast that has been discounted with NPV. I mean, you have the actual
data, why mess with a forecast?
The above is an example of why I'm not clear on why a "formula" needs to be
solidified or why you would want to strap yourself into that kind of inflexibility - unless, of course, there is
something going on system-wise that I am (obviously) not competent to comment on. None of these queries
are complex formulas, but you do need all the source data to keep track of
the campaign populations and go back and query them. That's why I stressed
the data and flexibility above.
Now, if we're talking about a resource issue, as in you live in a is a "set
it and forget it" type of environment where the ability to run custom (though very simple) queries on the
data is lacking, and you need a "formula" approach, I would use the LifeCycle Grids from
my book to replicate the idea above. If you don't recall this idea,
the Grids are similar to the pix in this
Once the data collection and output for one of these Grids is programmed,
all you need is the ability to run a Grid for each campaign - and that
should be super-easy to set up, even as a self-service thing. For example,
you go into a menu system and select:
Run LifeCycle Grid --> Choose Campaign Code --> Choose Date Range
and run your own reports. They could be set up to run overnight or on
weekends if system resources are an issue.
Then, you could just visually compare the Grids or have them output to
spreadsheets and perform calculations on them. It should be very obvious
which campaigns generate higher relative LTV using this method - over both the
short term and the long term. If dollars are more important than number of transactions, use dollars instead of Frequency.
If you really want to get to a "net net net" LTV number (and you should!), have
that number carried in the database cumulatively as opposed to created in the query
each time and use that number instead of Frequency in the Grids.
This further reduces the need for custom work. In other words, if the system knows margin and cost of campaign, have the
net net net calculated during updates and stored in the customer
Basically, all you need to know is the margin on the product and the cost of
all campaigns, which would come from a promotional history table - cost of
all promotions (and any discounts) the customer received. This
net LTV number could be updated on a weekly or monthly basis. If you want extra
credit, also net out the cost of customer service calls using the customer
service history or use an average per customer during updates. Then when you run the
Grids, you will be working with net cash, which is a good thing.
Look, I think the Gupta book is fabulous and have reviewed it
blog. But you always have to ask yourself, why am I doing this analysis, and what
kind of action will be taken? His book is largely about strategic
customer analysis (the idea of a "portfolio" of customers), and I'm guessing you are
looking for something more tactical because of your focus on campaign
His approach is long-term and planning oriented; mine is short-term and
results oriented. Neither conflicts with the other, they operate on
different time horizons for different purposes, and ultimately the short-term tactical results for relative LTV should roll up to the long-term
strategic forecasts of absolute LTV. That is, if the forecasts are accurate!
Does the above make sense to you? Hope it helped to form the approach to the IT side of this better in your
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That's it for this month's edition of the Drilling Down newsletter.
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'Til next time, keep Drilling Down!
- Jim Novo
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