The Customer details page in Repeat Customer Insights shows what data the app has on a specific customer and how the analyses come out. Customer data First there is the customer data from Shopify including the customer’s name, email, what marketing consent they’ve given, and the ID from Shopify for the customer. The customer’s acquisition …
Tag: kb repeat customer insights segmenting
Customer Segmenting to know which customers are the best customers
The Customer Segmenting in Repeat Customer Insights ties together various segmenting models into one place: RFM: Recency Frequency Monetary Customer Grading: A-F letter grades Automatic Segments Customer analysis Each customer is listed with their various analyses: Customer Grade When they last ordered How many orders they have (excluding cancelled orders) How much they’ve spent so …
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 …
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 …