A lot of power comes from combining things. Peanut butter and jelly. Hot peppers and vinegar. Bart and Homer. That combining is what make RFM a powerful customer analysis model. By combining three different measurements it can cover a lot of different behavior while still being simple to understand. (Unlike most AI or “fake-AI” systems …
Tag: rfm
Before starting any loyalty program, identify who your best customers are
There are lots of tactics for building customer loyalty. The first step missed by stores is identifying who your best customers. That’s required so you can tailor your plans to getting more of those types of customers. RFM and models based on RFM like Repeat Customer Insights’ customer grades are simple to understand while having …
Digging into customer details to find your best customers
It’s been a busy month for Repeat Customer Insights development with a bunch of new features and analyses getting released. Eagle-eyed customers will notice the New Feature banners but I want to share them in detail. First up is the new customer details page. Every customer in your store will have one of these. You’ll …
Why RFM has no set points that determine a customer’s ranking
Recently a Repeat Customer Insights was asking about what triggers the RFM scores to change, like from Recency 5 to 4. There are no set points or triggers that move customers from one RFM score to another. Rather, RFM ranks each customer against your customer base as a whole and based on where the individual …
Measuring the results from marketing tactics from month-to-month with RFM
A Repeat Customer Insights customer emailed asking how to measure improvements in their customer segments each month: We are putting together a strategy of measuring our tactics in these numbers. A few items we are hoping to look at is what % of our customers are at risk, what percentage have defected and what percentage …
Segmenting by comparing to your average customer
A simple way to evaluate customers is to compare them to your hypothetical average customer. Has this customer ordered more or less often than the average customer? Does this customer spend more or less in each order than the average customer? Does this customer order sooner or later than the average customer? These sorts of …
Picking one and only one metric for customer analysis
I was thinking, if I had to pick one and only one metric to look at for customer analysis, I think I’d pick Recency. That’s the first part of the RFM model and while I’d rather use the whole model, the Recency would be a good single metric. Recency tracks how recent a customer has …
Find the loyal customers who spent the most in your Shopify store
With the release of the RFM score filters in Repeat Customer Insights, there’s more ways you can segment your Shopify customers. One important segment are the loyal customers who have spent the most. Loyal is defined in the app as having recently purchased and have made many purchases. Nothing about their purchase sizes though, which …
New customer filters for RFM scores released
Repeat Customer Insights now has the ability to filter customers by their RFM scores. Previously the app would use the RFM scores behind-the-scenes but they weren’t shown except in the data export. Great for spreadsheet users, but not great for stores who wanted to browse and explore their customers. I’ve added the option to filter …
Using Recency and Frequency to measure customer loyalty
In Repeat Customer Insights there are a few insights you can pickup by comparing the Recency-Frequency (RF) Grids. The segments to the top right are the best for repeat customers, the ones to the bottom left are the worst. If segments to the right are increasing (higher Frequency), that’s a sign that new customers are …