Ordering frequency is another predictor of customer loyalty.
RFM measures this with the middle component, Frequency. That means customers with a score of 4 or 5 there are more likely to be loyal but there’s another method I also include in Repeat Customer Insights.
First, find out your Average Orders per Customer. This will show you how many times an average customer is ordering before they stop.
Next, decide on a multiplier for loyal customers. I use 2x, meaning if a customer orders double the average then they are loyal.
Finally, run through your Shopify customers and find everyone who has placed at least that many orders.
These customers were potentially loyal, that doesn’t mean they are loyal.
You’ll want to do a bit more analysis to make sure they are still active and still buying. That’s where the RFM method of loyalty has a leg-up on this. The other two components of RFM handle other aspects of the customer to make sure they are active and placing good-sized orders.
Comparing how often a customer orders against the average is a simple way to get an idea of who might be loyal. It’s also great for building a loyalty program around, especially order-frequency based ones.
This analysis is done for you in the new Who Are Loyal focus page in Repeat Customer Insights.
Use cohorts to find out who the best customers are in your Shopify store
Repeat Customer Insights will automatically group your customers into cohorts based on when they first purchased. This will let you see how the date customers bought would impact their behavior.