One thing the RFM analysis will teach you is that the biggest spenders aren’t always the best customers. How recently and frequently a customer buys are more important. Big spenders are nice when they buy but if they are only one-time customers you won’t get much long-term benefit from them. They’ll help you out this …
Tag: rfm
Adapt your marketing to avoid your worst customers
At some point it’s not worth it to market to some customers for reorders. They might be unresponsive. They might be chronic returners. Or they might just cost too much to reach so even when they do order, you’re not turning a profit. Your limit will depend on how much it costs to reach them. …
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 …
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 …
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 …
How many customers do you need to start segmenting your marketing
Segmenting your customer base to improve your marketing is powerful but you can do it too early. Splitting a small group of people into even smaller groups can mess with the statistics too much and get you odd results. A good rule of thumb I use is to make sure each group includes least 100 …
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 …
Surviving the relatives
When analyzing your customers, it’s important to compare how they perform relative to the other customers. If you have a customer who has spent $1,000 in their lifetime, would you consider them a good customer? It’s hard to answer that. $1,000 sounds like a lot. But if your average order is $800, that’s not even …
Where the Automatic Segments get their customer thresholds in Repeat Customer Insights
A Repeat Customer Insights customer was asking about how the Automatic Segments are created: I am wondering where the app gets the “whale” threshold from, and other thresholds? All of the Automatic Segments come from the RFM values. With RFM each customer gets 3 scores from 5 (best) to 1 (worst), where each score is …
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 …