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 if someone placed one huge order years ago and never came back, are they "better" than a customer to places a medium-sized order every week?
I think looking deeper into the RFM scores would be worthwhile.
There's two ways I'd approach this in the app:
- There's also a semi-hidden segment called VIP. Those aren't in the Customer Grid but are customers who have 5's in all three scores.
Click any segment in the Customer Grid and then look for the dark green VIP label, not the VIP (RF) one.
Those are the top 0.8% of your customers.
- Alternatively you could ignore the Customer Grid segments and focus on the raw RFM scores that you can get from the Customer Export.
A customer with a score of 5 for Recency is in your top 20% for Recency. Similarly, a customer with a 5 in Frequency or Monetary is in the top 20% of each of those.
So instead of trying to define one segment that's the best, start by taking 1/3rd from of each group. Use the of the top 20% of each segment (the 5 score) and then pare them them down by 1/20th to get the top 1% (e.g. keep a customer with the scores of 5 4 4 over one with 5 3 3).
The first option is faster but not as precise. The second takes a bit longer but you have flexibility in case you need exactly 1% or some other percentage.
If you'd like to have segments like these created for your Shopify store, Repeat Customer Insights will do that automatically for you.
Segment your customers to find the diamonds in the rough
Not all customers are equal but it is difficult to dig through all of your data to find the best customers.
Repeat Customer Insights will automatically analyze your Shopify customers to find the best ones. With over 150 segments applied automatically, it gives your store the analytics power of the big stores but without requiring a data scientist on staff.