How to Calculate Customer Lifetime Value (CLV) in Ecommerce

Posted by Edward Gotham 15 May 14

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What is CLV in ecommerce?

CLV stands for customer lifetime value and, put simply, it’s the value a customer contributes to your business over the entire lifetime at your company. The main methods of calculating CLV are split between historic and predictive CLV.

  • Historic CLV (Good indication of CLV)  

    Simply the sum of the gross profit from all historic purchases for an individual customer.

  • Predictive CLV (Great indication of CLV) 

    A predictive analysis of previous transaction history and various behavioural indicators which forecasts the lifetime value of an individual. As long as the equation is accurate, this value will become more accurate with every purchase and interaction.

Why is customer lifetime value important?

Customer lifetime value is one of the most important metrics in your arsenal as an ecommerce retailer. It helps you:

  1. Generate real ROI on customer acquisition

    CLV helps you focus on the channels that give you the best customers. You should be optimising your marketing channels in terms of the lifetime value a customer contributes to your brand rather than the gross profit on the initial purchase. You are therefore trying to maximise your customer lifetime value in relation to you cost of customer acquisition (LTV:CAC).

    In doing this you will completely changes the economics of your customer acquisition strategy. Suddenly you can pay a lot more to acquire a customer. You are not constrained by the profit generated from a single purchase but from the purchases made over a lifetime with your brand. Information about your customers with the highest CLV (Hero customers) will also give you insight into exactly who you should be targeting in terms of demographic. This combined knowledge will allow you to beat your less data-driven competitors.

  1. Enhance your retention marketing strategy 

    The value of a marketing campaign (for example, one aimed at turning your one-time purchasers into repeat customers) should not just be valued on the instant revenue they drive. It should be valued in terms of what impact it had on the Avg CLV of the segment of customers you are targeting.

    How did it alter the trajectory of CLV gained over time for an average customer? In order to accurately calculate this you'll need accurate predictive analytics so that you can see how predicted CLV is influenced by different marketing actions.

  1. Create more effective messaging, targeting & nurturing 

    Segment your customer base out by CLV so that you can improve the relevance of your marketing with more personalised messaging. An important variable to use here is the types of products you market to your customers from different segments.  

  2. Improve your behavioural triggers 

    Using clustering techniques you can discover the behavioural triggers that incentivised your best customers to make their first purchase. You should be trying to replicate this behaviour with your prospective customers in order to turn them into first time purchasers.

  3. Improve output from customer support 
          Focus your time on giving special attention to your most valuable customers.

How do I calculate CLV?

Historic CLV

This is simply the sum of the gross profit from all historic purchases for an individual customer. Sum all gross profit values up to transaction N where transaction N is the last transaction a customer made with your store. If you have access to all your customer transactional data you can calculate this in Excel or, if you want to save time and have this calculated automatically through software, you should try a tool such as Ometria. 

CLV (Historic) = (Transaction1+Transaction2+Transaction3...+TransactionN) X AGM

AGM = Average Gross Margin

Calculating CLV based on net profit ultimately gives you the actual profit a customer is contributing to your store. This takes into account customer service costs, cost of returns, acquisition costs, cost of markeitng tools etc. The issue with this is that it can be highly complex to calculate this on an individual basis, especially if you want the figures to constantly be up to date. Gross margin CLV will still give you great insight into the true profitability of your customers to date. 

Predictive CLV

Predictive CLV algorithms try to obtain a more accurate value of CLV through predicting the total value a customer will eventually give to your store over their entire lifetime. This is summed up so nicely by Vladimir Dimitroff that I will quote his defintion here:  

"CLV is always the NPV (net present value) of the sum of all future revenues from a customer, minus all costs associated with that customer."

In practice this can be hard to achieve due to the requirement for up to date discount rates. There are numerous ways to calculate a predictive CLV that vary wildly in complexity and accuracy however we will focus on a couple of examples here. Simple and Detailed. 

Simple

Screen_Shot_2014-05-15_at_09.31.27

Where:

T = Average monthly transactions

AOV = Average order value

ALTAverage Customer Lifespan (in months)

AGM =Gross margin

Let's call the above equation gross margin contribution per customer lifespan (GML).

 

Complex

Screen_Shot_2014-05-14_at_18.36.56

Where:

R = monthly retention rate

D = monthly discount rate

 

Be aware that these models will never be exactly right, they are just forecasts. To quote Vladimir again:

"predictive techniques...are always limited to the horizon of our models' predictive accuracy and confidence."

Having said this the more tailored you CLV equation is to your specific industry the more accurate it will likely be. The best models are highly accurate. It is essential to do these calculations to get a real understanding of how valuable your customers actually are, the more accurate you become the more powerful your marketing can be.

One important factor the above equation misses out on is the ongoing costs to your business for retaining a particular customer. You would need to calculate this in order to get a net value for CLV. In addition the most complex predictive models make increasingly more accurate CLV figures based on how a specific individual continues to interact with your store. Taking into acount both interaction and transactional information. As we know not every individual is the same, some are a lot more valuable than others and as you gather more data about an individual you can determine, with increasing accuracy, what sort of individual they are likely to be - High, Medium, Low value etc. 

Good luck calculating more accurate values for your customers! If you're interested in increasing the CLV of your customers. Check out our Customer lifecycle marketing ebook. 

 

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