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In marketing, customer lifetime value (CLV), lifetime customer value (LCV), or user lifetime value (LTV) is a prediction of the net profit attributed to the entire future relationship with a customer. The prediction model can have varying levels of sophistication and accuracy, ranging from a crude heuristic to the use of complex predictive analytics techniques.

Origins

One of the first accounts of it is in the 1988 book Database Marketing, and includes detailed worked examples.

Uses and Advantages

Customer lifetime value has intuitive appeal as a marketing concept, because in theory it represents exactly how much each customer is worth in monetary terms, and therefore exactly how much a marketing department should be willing to spend to acquire each customer, especially in direct response marketing.

Lifetime value is typically used to judge the appropriateness of the costs of acquisition of a customer. For example, if a new customer costs $50 to acquire (COCA, or cost of customer acquisition), and their lifetime value is $60, then the customer is judged to be profitable, and acquisition of additional similar customers is acceptable.

Additionally, CLV is used to calculate customer equity.

Advantages of CLV:

• management of customer relationship as an asset
• monitoring the impact of management strategies and marketing investments on the value of customer assets
• determination of the optimal level of investments in marketing and sales activities
• implementation of sensitivity analysis in order to determinate getting impact by spending extra money on each customer
• optimal allocation of limited resources for ongoing marketing activities in order to achieve a maximum return
• a good basis for selecting customers and for decision making regarding customer specific communication strategies
• measurement of customer loyalty (proportion of purchase, probability of purchase and repurchase, purchase frequency and sequence etc.)

Misuses and Downsides

NPV vs Nominal Prediction

The most accurate CLV predictions are made using the net present value (NPV) of each future net profit source, so that the revenue to be received from the customer in the future is recognized at the future value of money. However, NPV calculations require additional sophistication including maintenance of a discount rate, which leads most organizations to instead calculate CLV using the nominal (non-discounted) figured. Nominal CLV predictions are biased slightly high, scaling higher the farther into the future the revenues are expected from customers

Net Profit vs Revenue

A common mistake is for a CLV prediction to calculate the total revenue or even gross margin associated with a customer. However, this can cause CLV to be multiples of their actual value, and instead need to be calculated as the full net profit expected from the customer.

Segment Inaccuracy

Opponents often site the inaccuracy of a CLV prediction to argue they should not be used to drive significant business decisions. For example, major drivers to the value of a customer such as the nature of the relationship are often not available as appropriately structured data and thus not included in the formula.

Comparison with Intuition

More, predictors such as specific demographics of a customer group may have an effect that is intuitively obvious to an experienced marketer, but are often omitted from CLV predictions and thus cause inaccuracies in certain customer segments.

Effects on Business Practices

Its use as a marketing metric tends to place greater emphasis on customer service and long-term customer satisfaction, rather than on maximizing short-term sales.

Predictive Models

Simple Ecommerce Example

(Avg Monthly Revenue per Customer * Gross Margin per Customer) / Monthly Churn Rate

You should have something that looks like:

$100 avg monthly spend * 25% margin / 5% monthly churn = $500 LTV 

A Retention Example

4 Steps

1. forecasting of remaining customer lifetime in years
2. forecasting of future revenues year-by-year, based on estimation about future products purchased and price paid
3. estimation of costs for delivering those products
4. calculation of the net present value of these future amounts

Forecasting accuracy and difficulty in tracking customers over time may affect CLV calculation process.

Inputs

• Churn rate, the percentage of customers who end their relationship with a company in a given period. One minus the churn rate is the retention rate. Most models can be written using either churn rate or retention rate. If the model uses only one churn rate, the assumption is that the churn rate is constant across the life of the customer relationship.
• Discount rate, the cost of capital used to discount future revenue from a customer. Discounting is an advanced topic that is frequently ignored in customer lifetime value calculations. The current interest rate is sometimes used as a simple (but incorrect) proxy for discount rate.
• Contribution margin.
• Retention cost, the amount of money a company has to spend in a given period to retain an existing customer. Retention costs include customer support, billing, promotional incentives, etc.
• Period, the unit of time into which a customer relationship is divided for analysis. A year is the most commonly used period. Customer lifetime value is a multi-period calculation, usually stretching 3–7 years into the future. In practice, analysis beyond this point is viewed as too speculative to be reliable. The number of periods used in the calculation is sometimes referred to as the model horizon.

Model

:

${\text{CLV}}={\text{GC}}\cdot \sum _{i=0}^{n}{\frac {r^{i}}{(1+d)^{i}}}-{\text{M}}\cdot \sum _{i=1}^{n}{\frac {r^{i-1}}{(1+d)^{i-0.5}}}$,

where ${\text{GC}}$ is yearly gross contribution per customer, ${\text{M}}$ is the (relevant) retention costs per customer per year (this formula assumes the retention activities are paid for each mid year and they only affect those who were retained in the previous year), $n$ is the horizon (in years), $r$ is the yearly retention rate, $d$ is the yearly discount rate.

Simplified Models

It is often helpful to estimate customer lifetime value with a simple model to make initial assessments of customer segments and targeting. Possibly the simplest way to estimate CLV is to assume constant and long-lasting values for contribution margin, retention rate, and discount rates, as follows :

${\text{CLV}}={\text{GC}}\cdot ({\frac {1+d}{1+d-r}})$

See also

Gompertz distribution

External links

1. custora.com/home/customer_lifetime_value
2. Free customer lifetime value calculator
3. kaushik.net/avinash/analytics-tip-calculate-ltv-customer-lifetime-value/
4. ariegoldshlager.posterous.com/recommend-reading-customer-lifetime-value-pot
5. quora: How-do-you-calculate-Customer-Lifetime-Value