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 …
Tag: kb repeat customer insights grid
Giving discounts to your best repeat customers
A Repeat Customer Insights customer was asking about using loyalty discounts and how that could impact customer behavior. Loyalty is mainly measured by the Frequency score in RFM with the Recency showing a bit of influence too. It makes sense, the more times a customer orders then more likely they are loyal. And the more …
How is a Shopify customer assigned automatic segments?
Recently a subscriber was asking how the Customer Grid in Repeat Customer Insights knows which segments to give to each customer. There are three Customer Grids in the app: Recency-Frequency (RF) Grid Frequency-Monetary (FM) Grid Recency-Monetary (RM) Grid Each grid combines two parts of the RFM scores. One score is for the vertical columns, one …
How RFM is used by the Customer Grid to segment customers into behavior groups
A Repeat Customer Insights customer was asking how the app creates the 2-D customer grid and the customer segments. The app uses RFM to score each customer from 1 to 5 across three different criteria: Recency, Frequency, and Monetary. The Customer Grid takes two of each of those criteria and lays them out in a …
How to identify, track, and retain repeat customers in Shopify
A Repeat Customer Insights customer was asking me last week how to identify, track, and keep their high-repeat buyers. My answer is specific to the app but you can still follow it if you have a way of measuring customer purchases and grading them by how recently and frequently they order. The app uses the …
Better customer segmenting by looking at more than one analysis
When you’re marketing to your best customers, how do you decide who is the best? Is it based on how much they spend? Or how many times they’ve ordered? Or how long ago they last ordered? Each of those is a component of the RFM algorithm but it doesn’t give you a straight answer. That’s …
The segment types Repeat Customer Insights creates automatically for your Shopify store
A Repeat Customer Insights customer was asking about the different segment types and sizes. Since each store has a couple hundred segments created automatically, it can be difficult to know how they work. One set of segments comes from the RFM algorithm. Each Recency (R), Frequency (F), and Monetary (M) metric is scored from 1-5. …
Give each customer segment a purpose
One way to approach customer segments is to define your segments and then give each one a purpose. First create a segment of customers based on some common behavior behavior. Then reflect and think about their behavior by putting yourself in their place. What do they want? What do you want from them? How can …
How to find the top 1% of your customers using RFM
Recently Richard asked how to find their best 1% customers in Repeat Customer Insights. In the majority of the sources I’ve researched, Recency is the most valuable measurement followed by Recency/Frequency. Some ignore the Monetary one completely, but it has many uses. The big question to start with is, what’s "best"? Spending is useful but …
Uncovering your potential best customers
Finding which sets of customers to target with your marketing messages is important. A customer of Repeat Customer Insights was asking about how to find their potential best customers and if possible, ones who’ve ordered during a specific time. Today, we are trying to identify now who are our potential future best customers based on …