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  Behavior Visualization
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Calculating LifeTime Value (LTV) or 
Lifetime Customer Value (LCV)

Jim's Intro:  There are two kinds of LifeTime Value measurement - absolute and relative.  The first is very difficult to calculate; the second, very easy to calculate and in many ways more powerful than the first.

The most difficult part of calculating LTV is deciding what a “lifetime” is.  LifeTime Value is the value of the customer over the LifeCycle (if you don't know what a LifeCycle is, you really should read the article on LifeCycles before reading this one).  Lifetime Value doesn't exist without a LifeCycle.  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 over the last several years.  It is not necessary to figure out an absolute LifeTime Value for a customer or wait "a lifetime" to find out the value to use the concept in managing customer 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 leverage from using LTV / LCV in your business model to manage customer value.

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 relative LifeTime Value and loyalty of customers is a 40 year old technique still used by mail order and TV shopping companies 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 Potential Value of Customer Groups

Let's say you're not satisfied with using relative LifeTime value as a proxy for absolute LifeTime Value.  You're a glutton for punishment, or your boss wants a hard number.  No problem.  Here 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 LifeCycle 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 example.

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.  Try 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



Gross Margin



Credit Clearing



Revenue Ship & Handle



Cost of Ship & Handle



Call Center 
(1 call every 5 sales)

  ( .80)


Returns and Processing 
(5% of Sales)



Fraud / Merchandise Loss (1% of Sales)

  ( .40)


Promotional Costs / 
Discounts / Ads

  ( .80)





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 breakeven.  

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.

Don't 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 customers.  And explaining that to your boss (if you have one) is often a challenge, even on a positive day.  Good luck!

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 Down book 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 customers.


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