Calculating LifeTime Value (LTV) or
Lifetime Customer Value (LCV)
The most difficult part of calculating LTV
is deciding what a “lifetime” is.
If you have studied customer LifeCycles,
you know what it is. The
lifetime is the amount of time a customer will stick around before
defecting and leaving your business. We will get into some details
on calculating lifetime value in a moment, but first, a clarification.
The lifetime value concept has been
horribly abused and misunderstood from the beginning in dotcom land.
It is not necessary to figure out an absolute lifetime value for a
customer or wait "a lifetime" to find out the value.
If you are new to this lifetime value stuff and have not tracked the
appropriate parameters, or your company is new and lacks meaningful
operating history, you can look for "relative lifetime value",
link it to customer behavior, and still get tremendous leverage from using
LTV / LCV in your business model.
Here's a very simple example. Say I run the same ad in two different
newsletters and get response from both. When I look at these
responders, maybe a week later for a content visit or 30 days later for a
purchase, I find a high percentage of repeat visitors or buyers from one
newsletter, and a low percentage from the other.
Repeat behavior indicates higher lifetime value, and predicts future
repeat behavior, regardless of what the actual monetary lifetime value is.
I can switch money out of the low repeat newsletter into the high repeat
newsletter and get higher ROI without having to measure
anything but repeat behavior.
By the way, using customer behavior to predict the future value and
loyalty of customers is a 40 year old technique still used by mail order
and TV shopping today. Large sites with CRM
analytics are using this technique, known as RFM,
to predict customer value and response to promotions. If you'd like
to see more details on using relative LifeTime Value to make ad or
product decisions, see the tutorial: Comparing
the Future Value of Customer Groups.
If you want to see a practical high-ROI
implementation of LTV / LCV proxy, read How
to Measure Future Customer Value and Manage it with E-mail.
Let's say you're not satisfied with
using customer behavior as a proxy for lifetime value. You're a
glutton for punishment, or your boss wants a hard number. No
are a few issues we need to put on the table when discussing the
calculation of LTV:
1. If you haven't been in business long enough
to know the Lifetime of a customer, just put a stake in the ground by
looking for defected best customers. Look at customers who have
spent or visited the most with you and then of these, look at the ones who
haven't made a purchase or visit in some time (6 - 9 months, for example). In all likelihood,
the last purchase or visit was the end of the Lifetime when considering
best customers who have stopped buying or visiting. When best
customers stop, they're usually all done. Then look
at first purchase or visit date for these customers, calculate your
Lifetime, and use this length of time as the "standard" customer
LifeTime, realizing the average lifetime is probably much shorter.
2. Frequently, a customer will
defect for a few years and then come back.
This is cool, and normal. Their
life changed somehow and they left, and now they need you again.
Most offline marketers would call a customer who has had zero activity for
over 2 years a defected customer.
Online, it's more like 6 months for the average customer, unless you are
in a classic seasonal business. If
the customer starts up again, they would be a “new customer”, for
marketing and modeling purposes. They
will more likely behave like a new customer than a current customer. The
behavior will ramp and fall off all over again, just like it did in their
previous LifeCycle with your business.
That doesn’t mean you can’t use the
same customer number, or combine the old behavior record with the new
behavior record in the customer service shop.
In fact, knowing how long on average a customer defects before they come back
can be a useful promotional tool.
But there has been a significant break in
behavior, and this customer is more likely to behave as a new customer
than a customer who has been with you the whole time.
That’s just the way it works.
They’re likely to be interested in different products, for
You decide if it’s a new lifetime or not
based on your business. In
most cases, from a marketing perspective, and for the purposes of LifeTime
Value, they should be treated as a new customer.
Otherwise, all your customers will have “infinite” lifetimes,
and you lose the relevance of the metric.
3. Another challenge to calculating
LifeTime Value: usually much of the data you need to complete the simple
calculation are not available, or can't be agreed upon by all the players,
especially if you are in a big company.
If you don't know what the average unit returned costs you in terms
of overhead, you can't do the calculation. If you don't know what the
average number of customer service calls per unit shipped is and what the
calls cost, you can't do the calculation. This is a particularly difficult
problem for offline retailers, who don't have a database that captures
nearly enough relevant data.
Here's one way approach it if the operational data you need is unclear.
to focus on the average unit sold, and break up all the revenue and cost
components that comprise the unit. Once you get to a profit / unit, just
multiply by units sold to a customer over the "lifetime", minus
overhead and promotional costs, and you get LTV.
Average price, cost of goods sold, gross margin...should be easy to find.
To get customer service costs, look at how many units you move annually,
and divide by annual customer service cost.
Do the same thing for returns, and so on, until you know the costs
/ unit sold of all the elements going into a sale. Don't forget credit
processing, after sale support, etc. For example:
Net Profit per Unit Analysis:
Average Sale Price
Cost of Goods Sold
Revenue Ship & Handle
Cost of Ship & Handle
Call Center (1 call every 5 sales)
Returns and Processing (5% of Sales)
Fraud / Merchandise Loss (1% of Sales)
Promotional Costs / Discounts / Ads
Net Profit per Unit
LTV Calculation and Customer Acquisition Cost Calculations:
Say the average customer buys for 2 years, then stops for at least 1 year.
Therefore, we define the LifeTime of a customer as 2 years.
Over 2 years, the average customer makes 16 purchases.
16 x $1.20 Profit per Unit = $19.20 LTV of the average customer
The average customer recruits 3 other customers. The maximum
acquisition cost of a new customer should be 4 x $19.20 = $76.80 to
By the way, I'm not a fan of including
pass-a-long or referral customer value in an individual customer lifetime
value. If you do, what are the pass-a-long customers worth?
It's double counting. Use it to look at acquisition costs as in the
example above, but don't include it in LTV calculations. The sum of
all your customer lifetime values should equal your future profits; if you
include the value of pass-a-long customers in lifetime value, you will
over estimate profits.
be surprised if you find some customer groups have negative LTV's –
it’s very common. This is the part of LTV analysis usually forgotten,
because it literally means you would be more profitable if you had fewer
And explaining that to your boss (if you have one) is often a
challenge, even on a positive day.
After measuring customer value, the next step
is to manage customer value - to make money by creating very
high ROI customer marketing campaigns and site designs. The Drilling
describes how to easily create future value and likelihood to
respond scores for each customer, and provides detailed instructions on
how to use these scores to continuously improve the profitability of your
What would you like to do
the book with Free customer scoring software at:
Out Specifically What is in the Book
Marketing Models and Metrics (site article