customer lifetime value formula

We have written about the importance of calculating customer lifetime value (CLV) as a means to quantify the benefit from carefully segmenting and marketing to individual consumers. A general customer lifetime value formula will include the following components:

1. A net value that arises from selling to each customer which is in essence equal to revenue minus costs (R-C),

2. The probability that a consumer is going to return for new business. This is frequently expressed as a retention rate (r). At an individual level, this factor can be usually estimated with the help of any of the predictive analytics techniques,

3. A discount factor (f) to account for the present value of future cash flow streams,

4. and finally, a duration (d) or the “life” of consumer with respect to the business. 

This last component can be industry specific ranging from a few years to a human life span. If you consider a product which is marketing exclusively to teenagers for example, the duration can be no more than 10 years whereas if you consider a product such as an automobile, this duration can literally span a person’s lifetime.

So how does computing the customer lifetime value become industry specific? Particularly how can we adjust the general formula for the needs of the automotive industry? Let us take a look at this component by component for the auto industry.

1. Net Value (R-C): Some people argue that since costs of advertising and promotion are spread over the entire customer base, the cost (C) for an individual consumer is fixed and therefore we can completely ignore this term. This argument is meaningful if you are only focusing on new customer acquisitions where there is no database of records for individual costs (such as discounts offered to a particular customer). The approach used in an automotive application would be to simply ignore C and focus only on R, if the intent is to only quantify CLV for prospective customers.

2. Retention rate (r): For many applications, retention rate is assumed to be a constant. We described in an earlier article how to compute the retention rate if we know the repurchase frequency under this assumption. But what if the repurchase frequency is not a constant but depends upon a host of factors? Specifically, for the automotive case, it is well known that repurchase is influenced by age of the customer (older people tend to keep cars for longer), usage (more miles driven leads to faster repurchase), income (higher income leads to earlier repurchase or re-lease), and finally brand perception (luxury brand owners tend to buy more frequently). One study reported that BMW 7 owners to be 33% more valuable than BMW 5 owners and 62% more than BMW 3 owners, over their respective lifetimes. 

To adjust the retention rate for an automotive application we will first need to understand and model precisely how these various factors would influence the repurchase rate and the include this into our CLV formula.

3. Discount factor (f): Similar to the retention rate, the discount factor is also assumed to be a constant for many applications. However when it comes to automotive customers, because the duration extends over a human lifespan, this assumption may be open to question. Similar to retention rate analysis, we could model the discount factor as a function of other external variables which could influence it. However, a simpler model would use time series forecasting method such as exponential smoothing to predict the discount factor over the expected lifespan of the customer (and hence the duration).

Clearly there are several considerations to make when applying the customer lifetime value formula for the auto industry. The major contributor to these adjustments stem from the fact that car companies would like to “own” their customers for their entire lifespan and not just the duration of time till they defect. One could make a similar argument for the telecom industry – after all people do need mobile phones as long as they live. However the relationship of a consumer to their cell phone is not as emotional as it is to their car (but that is another debate). Furthermore it is far more expensive to attract new customers than to retain existing ones in the car industry and thus the stakes are significantly higher for manufacturers. 

Originally posted on Thu, Mar 07, 2013 @ 08:49 AM

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