How to interpret the Lifetime value report

This is a guide on how to analyze your customer data using the lifetime value report.

The basic concept of cohort analysis

In  cohort analysis, we're following how a group of customers who bought the first time at a certain month or year (=a cohort) buy in consecutive months and years. Instead of looking at how a single customer has behaved, it breaks them into groups based on customers' first purchase date for analysis.

Cohort analysis is a method that only uses historical data - it doesn't do any predictions. That's why it's a more reliable method also for smaller stores that don't have enough order for statistically significant predictions. Read more about predictive lifetime value on our blog post.

Some questions you can answer with cohort analysis:

  • How much new customers from 2018 have purchased in 2019?
  • How many sales margin a new customer generates in the first year?
  • How much I can spend on new customer acquisition if I want to turn a profit on a customer after 6 months? 
  • Is there a difference in LTV between different products/categories?

But first, let's learn to read the report!

How to read the LTV report

Here's an example from one of our test stores. Customers are by default grouped to groups by the month they made their first order.

Every row represents a fixed group of customers and every column is a month after their first purchase. In the picture below, there are 4398 new customers who made their first purchase in January 2018. In the first actual cell, 61€ means that these customers purchased on average total of 61€ per customer on their first month as customers. 

The value is on average per customer, calculated by dividing the total sum of the first orders and then dividing it by the number of customers in the group.

Then on the next cell (Month 1), we can see a value of 65€. 

This means, that on average a customer of those original 4398 customers has made an additional order the next month resulting 4€ increase in total customer sales per user. It doesn't mean that the next month's order value was 4€, but that net new sales from that customer group on that month resulted in 4€ increase per customer when divided by the original number (4398) of customers in that cohort.

In our example with the 6-month timeline, we can see the avg. sales for that customer grow up to 85€ from the original 61€.

Choosing the right metric for your analysis

When doing your analysis, you can choose from three different metrics in the menu: 

  • Sales per customer 
  • Sales margin per customer
  • Customers

Sales per customer

Sales per customer use the actual total sales including taxes as the basis for the analysis. 

By using this metric, you can understand the total average sales by a customer over time. But keep in mind: it doesn't account for any of the product or shipping costs and so doesn't fully reflect the profitability of a customer. 

Sales margin per customer

Sales margin per customer deducts all the product and shipping related costs from the order data used and so reflects the profitability of a new customer much better. This is our recommended metric if you've got accurate product cost data.

It doesn't account for marketing costs as Lifetimely is not currently doing marketing cost attribution (coming in future).


The third option is to use the number of customers as the metric. Instead of showing monetary values, it shows what number of customers in the cohort purchased that month. So for example in the screenshot below, 15 customers from November 2018 cohort made another purchase in January 2019 (Month 2-column).

Actual value vs. the percentage 

There's this nifty little toggle for changing values from absolute values to percentages. It gives you the option to find answers to questions like "What percentage of our customers return to purchase each month?".

Filtering the report

As usual, aggregated data are not often enough and you might want to try to drill down to understand the differences between customers and products. In this case with our report, you can use filters that enable us to segment the analysis using customer tags, products purchased, countries, and many more variables. 

Currently, the filters search for customers all orders and not just the first order. So if a customer has purchased a product at any point in time and you're using that product as a filter, that customer will be included in the analysis.

R-% values

The R-% value for each cohort is the percentage of customers from the cohort who have made a second purchase. The R%-value gives you a way to track your repeat purchase rate over time and answer a question like "How many of my Black Friday customers from the past year have repurchased?".

LTV average values

On the bottom of the report is the LTV average section with a breakdown of average customer value by time after the first purchase.

The average LTV values are calculated based on the selected calendar time frame. For example, 3-month LTV equals the average total sales during the first three months after the customer's first purchase, including the original first-order value. The calculation is based on customers who have more than 3-months since their first order - the same logic applies also to 6-, 12- and 24-month values. So if you're seeing N/A values, there are no customers with enough time from their purchase in the selected timeframe.

How to act based on the LTV report

We've written a thorough guide that will help you apply the analysis for your business problems: Check it here.

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