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. Each score would be 20% of your entire customer base.
So if you had 100,000 customers, your Recency 5 group would be 20k customers, Recency 4 another 20k, etc.
Same with Monetary, where each score is about 20k customers.
Frequency is similar but due to my modified version, Frequency 1 will be large (your one-time customers) and the remaining values would be proportioned into quartiles (25%). I’m going to assume Frequency is the straight 20% quintiles from here on just to make the examples easier to understand.
Each R, F, and M can be used as-is but they become even more powerful when you group them into pairs. I’ll call this a 2-D segment.
When you’re looking at a 2-D segment, they are 4% of your customers (20% of 20%) or about 4,000 in each. That’s because instead of getting split into only 5 groups, there’s 25 groups. e.g. Recency 5/Frequency 5, Recency 5/Frequency 4, etc
(There’s also the 3-D group but that’s only in the raw export. Each of those combinations are 0.8% of your customer base but there are 125 of those segments so you can get really fine-grain with your segmenting).
The Customer Grid with the automatic named segments are combining the 2-D groups into easier to understand groups. Each "cell" in the grid is a 2-D group and 4% of your customer base.
That’s why the "New" (RF) segment is small, it’s just one cell (4%) and the "Neutral (RF)" segment is larger, it’s 4 cells (16%, 16,000 customers).
I have all of these different options to cater to different store sizes and campaign options.
Some stores or campaigns only want to target a small number of customers (e.g. new store or an established store wanting to call customers 1-by-1) so they’d use the 3-D, 2-D, or maybe a small named segment.
Other times a larger segment is wanted, say for a no-cost email campaign, so they’d use just the straight score for one of the RFM values like Monetary: 5 (big spenders) or Frequency: 1 (first-time customers).
That’s why I put a lot of data into the Customer Export, so if the given segments aren’t perfect for a store they can sort and filter the data to create their own.
Repeat Customer Insights does all this segmenting for you. It helps to understand it but you don’t need to do all the math to build the segments, you just use the one with the behavior and size you need for a campaign.
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