One of the final features added to Repeat Customer Insights before the holidays is the automatic comparison of your repeat customer's behaviors versus your store average.
Many of the other insights compare how your Shopify store performs against industry averages and published statistics.
That's great for benchmarking but oftentimes your store is too unique to really compare.
That's why these two new comparisons can be powerful.
They compare how your repeat customers behave versus your store's averages.
The first launch of these includes comparisons for the Average Order Value and Repeat Purchase Rate, so let's use one of those as an example.
Say your store has an Average Order Value of $100.
On the 3rd order Repeat Customer Insights detects that your customers are only spending $85 on average though.
That'll be flagged for you to take a look at.
Then after you've worked on things and increased that, the app will turn off that insight automatically once it's above the storewide Average Order Value.
Best of all, it'll self-adjust for you. So if you run a promotion that drives up your AOV to $110, the app will find the new weak areas and recommend those to improve.
That'll make it easier for you to spot the weak areas in customer behavior and decide where to focus your resources and attention on.
Which is a major reason why the app exists, to have it help you make better decisions.
These comparison recommendations can show up on the Customer Purchase Latency report for each of the order rows.
If you haven't installed Repeat Customer Insights yet, it's an easy way to get this advice and analysis on your customer behavior.
Eric Davis
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.