Lifetime Value (and Customer Lifetime Value) are important metrics for Shopify stores when it comes to customer segmenting.
Its limitations are that it only looks at a value at a point in time. A LTV of $1,000 might be really great. Really low. Or even could be a customer who last ordered five years ago and is now gone.
That's why I like how the RFM analysis makes use of LTV as the Monetary component.
First off, each customer's LTV is ranked against the rest of your customers. So that customer with a LTV of $1,000 would be compared to everyone else being scored by RFM. If they actually have a higher LTV, RFM will score them well.
Secondly, the other components of RFM (Recency, Frequency) help score the customer based on their last order and how many orders they have. That bypasses the problem of using LTV on customers who stopped buying a long time ago. They might have a good monetary score from their LTV, but their Recency and Frequency scores would be so low you'd recognize they aren't that great of a customer anymore.
Combining RFM and your Average LTV, you can ignore individual customer LTVs for most segmenting. Use the Average LTV and the RFM components for your segmenting, then the algorithm can automatically adjust customers based on their behavior.
Repeat Customer Insights comes with a full RFM analysis for Shopify stores and even includes two LTV/Monetary-based visual Customer Grids to see how your customers are categorized.
Eric Davis
Learn which products lead to the customers who spend the most
You can use the First Product Analysis in Repeat Customer Insights to see which products lead to the customers who spend the most. Going beyond best sellers, it looks at the long-term purchasing behavior of your customers.
Topics: Rfm Average ltv Customer lifetime value
