Table of Contents
The latest chapter represented our beginning on this Customer Value Optimization journey.
After understanding what customer value is, it’s time to move on to the next important step.
Customer Lifetime Value is a method to predict how much profit your customer database can produce over the course of your relationship with them.
However, this simple formula does not always apply as most businesses are more complicated than that. So two other methods have been proposed: historical and predictive CLV.
Historical Lifetime Value
Reiterating the formula, Customer Lifetime Value is calculated as:
CLV = Lifetime Value x Profit Margin
- Lifetime value = Average Order Value of your Customers in a given period of time x Average Number of Transactions per year/week (purchase cycle) x Average number of years/months/weeks
- Profit Margin = (Total Revenue on a given period of time – (Cost of goods sold (COGS) + overhead + marketing + all other administrative expenses/Total revenue) x 100
Customer Lifetime Value example
Let’s create a hypothetical store where the average order value is $50 and the customers purchase on average 3 times a year for a 1-year span. The lifetime value would be:
Lifetime Value = $50 x 3 x 1 = $150
The store figured out that the total Revenue brought by the store since the beginning is $1,000, 000, but the expenses weighted for $980,000, then the Profit Margin is:
(Net) Profit Margin = ($1,000, 000 – $980,000)/ $1,000, 000) x 100 = 2%
Customer lifetime value = $150 x 2% = $3
A more efficient way to determine customer lifetime value is through predictive CLV.
The predictive CLV is built based on predictive analysis and takes into account previous transactions plus various behavioral indicators that forecast the lifetime value of an individual.
This value becomes more accurate with every purchase and interaction, so this is a better method to calculate customer lifetime value.
To calculate the predictive CLV you need to:
- Identify the touchpoints where your customer creates value;
- Find out what determines that value and if it differs from customer to segment;
- Identify why a customer has moved from one moment to the next.
Then, you can determine the predictive CLV in two ways.
1. Simple predictive CLV
CLV = ((Average monthly transactions * Average order value) * Average gross margin) * Average customer lifespan
*where the average customer lifespan is calculated in months. This formula is also used to determine the detailed predictive CLV, so let’s call it CLVs.
2. Detailed predictive CLV
CLV = CLVs * Monthly retention rate1 + Monthly discount rate – Monthly retention rate
One thing to keep in mind when calculating the predictive CLV is that it will never be 100% accurate as this is just a forecast.
However, if you personalize the formula for your business, you can determine a highly-accurate customer lifetime value.
Also, note that the equations above don’t take into account the cost associated with retaining a customer. To get a net value for your CLV, you will also need to calculate this. And if you really want to be accurate, you may also want to consider interaction and transactional information for each customer, as every individual is unique.
Lifetime value to Customer Acquisition Cost Ratio
The ratio between CLV and CoCA (customer acquisition cost) is one of the most important aspects that a VC will look at before investing in your company.
You simply can’t acquire customers forever. So finding the right customer acquisition and retention mix is the key to a sustainable eCommerce growth.
A good ratio would be 2, a bad one would be below 2 and the best ratio is 3.
If it is below 2, that means you are either doing this consciously in order to gain market share, either your business is bleeding money.
If it’s above 3, it usually means you’re either harvesting cash because your business can’t grow anymore as you are the market leader and don’t want to diversify.
Another possible explanation for this value could be the fact you’re too prudent and don’t want to grow faster, or you’re not aware of this and you don’t want to go faster.
The basic rule in poker is to look at your own cards. If you’re in the eCommerce game and you don’t know this ratio, it means you’re breaking this rule.
Check out our next article where we tackle an inspection of the Customer Value Optimization methodology!
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