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 …
Tag: kb repeat customer insights segmenting
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 …
Most valuable 80/20 customer segments for your Shopify store
As a follow-up to my article about which segments to focus on, you can take a more precise approach. Each cell in Repeat Customer Insights’ Customer Grids is about 4% of your customer base. (The grid isn’t strictly 4% though due to how the algorithm works but it’s good for a rule of thumb). That …
See how your customer segments have changed with the Customer Grid History
There’s been a suite of analyses I’ve wanted to add to Repeat Customer Insights since first reading them. I quietly launched the first back at the beginning of September but I’ve been watching its performance before officially announcing it. Eagle-eyed customers might have spotted it already. I’m calling it the Customer Grid History and it’s …
Using alternative customer segmenting models when your Shopify store is still growing
The RFM model works really well for large stores. With 125 unique segments, there’s a lot of fine-tuning potential. For smaller stores (and stores that don’t want to fiddle), it might be too much as it is. Say you get 500 orders per month (6,000 per year). That means you’ll have about 4 customers added …
More powerful customer grading algorithm to better understand your customer behavior
After months of planning and development, I’m happy to announce that Repeat Customer Insights had a major update to its customer grading. The new version of the customer grading still uses the simple letter grades (A, B, C, D, F) but now the grading algorithm takes into account more factors. It’s based on RFM which …
How your customers automatically decay with RFM
RFM scores are built from three components: Recency, Frequency, and Monetary. Though they are scored the same, they act differently. Recency is an odd one and can look the most chaotic for some. That’s because Recency will start to decay from the moment a purchase is made and get lower and lower until the customer …
An ACME catalog of customer segmenting options
There are an infinite number of ways to segment customers. Repeat Customer Insights includes a few of the more effective ones. Which methods you use will depend on your goals and to a lesser extent, your brand. Here’s a few off the top of my head that cover: how recently they’ve ordered how many orders …
Finding which customers are active and spending the most with your Shopify store
Last month a Repeat Customer Insights customer was asking about who their top 40 active customers were based on total spend. Both of those segments can be found in the RFM analysis. Active would use the Recency portion and spending would use the Monetary. These customers would be highlighted by a high Recency and high …
Finding your consistent big spenders in Shopify
In Repeat Customer Insights I was asked: What is the best report to determine most consistent highest spenders? You’ll want to use the Customer Segmenting reports for that. You can also use the Customer Grid as it’s a visual view of the same data and will let you drill down into the details for each …