The most successful ecommerce companies in the world truly understand their customer lifetime value. That knowledge allows them to be more thoughtful about acquiring new customers and improving repeat purchase rate, which enables them to grow much more quickly.
In the first post of this series, we focused on why customer lifetime value is an overall important business metric. (Read here if you missed it!)
Now that you understand its significance, next we’re going to explore concrete ways to measure it.
BUT BEWARE: Customer lifetime value can be a complex and confusing metric. For that reason, we’re going to offer a few different approaches to measuring lifetime value, ordered from simplest to most complex. Note that none of these approaches are perfect, as the perfect model would predict a customer’s purchase behavior with 100% accuracy. Still, each provides a useful heuristic for using historical data to estimate customer lifetime value.
In the simple method, we’ll use a few easily available business metrics to get a snapshot of LTV for a given time period. Most ecommerce platforms will provide what you need in the reports or analytics sections; this article walks through the calculations for Shopify users.
At a high level, this method calculates LTV using Average Order Value (AOV), Purchase Frequency (PF), and average customer lifetime (ACL).
LTV = AOV*PF*CL
To begin, pick a timeframe. We would recommend choosing either the last year or the lifetime of your business. Calculate AOV by dividing revenue over that time period by the number of orders in the same period.
Next, calculate PF by dividing orders by the number of customers who purchased in the period.
Finally, estimate ACL by analyzing the length of the average customer’s relationship with your brand. If you don’t know your ACL or haven’t been in business long, Avinash Kaushik recommends using 3 years as a rough estimate. With all of these numbers, you’ll have what you need for your first LTV calculation.
However, there are drawbacks to this simple calculation. It’s purely revenue focused and does not yet account for gross margin or other costs. Additionally, it gives you a snapshot of overall metrics, but does not break customers into groups based on when they become customers, i.e., high AOV vs. low AOV. If the majority of customers are new customers, it might artificially suggest low LTV, when in reality most customers haven’t had an opportunity to repurchase.
A more complex method of LTV analysis that solves some of The Simple Method’s issues involves analyzing order level data and applying segmentation.
The first step of this method is to download and export all orders with customer identifiers (e.g. name or email), order identifiers, date of order, revenue, COGs, and any other factors you might want to analyze (e.g. marketing source) for each order.
With this data in excel, you can use a pivot table to determine sum of lifetime revenue, sum of lifetime orders, date of first order, date of last order, COGs, and net margin (Revenue minus COGS) for each customer. This data alone will be fascinating. You’ll see some big spenders you may recognize by name.
This data also sets you up well for further analysis using the RFM method. RFM stands for Recency (how recently did the customer purchase), Frequency (how often does the customer purchase), and Monetary Value (how much does the customer spend, aka AOV).
Using the RFM model, assign tiers to each customer for each attribute of R, F, and M. Choose either 3 or 4 tiers for R, F, M and categorize each customer into a tier based on where they fall vs. others. In other words, if number of orders is in the top third among all customers, that customer gets a 3 out 3 for Frequency.
With the new RFM rankings, you can analyze LTV of customers in each tier. For example, sum average lifetime net margin for each RFM tier, and then divide by the number of customers in that tier to determine average customer lifetime value.
Which RFM factor tends to have the greatest impact? You can also begin to analyze LTV by when the customer purchased, marketing source, and other fields you brought into your data set.
There’s a whole world of additional approaches to more sophisticated LTV modeling. This report is a great start for merging academic methods with practical applications. These additional models take into account elements that the first two methods miss, like time value of money, incremental marginal costs of each sale, probability of future purpose, deeper cohort analysis, and more.
Plus, let’s not ignore the software that can help you calculate these metrics. A few notable business intelligence and analytics platforms to consider are:
Can your business afford to ignore customer lifetime value any longer? Absolutely not. Failing to do so means you’re not truly analyzing the economics of your business.
Now that you’ve calculated customer lifetime value, get ready for Part III, where we talk about how to use these metrics to effect change in your business.