Recently a Repeat Customer Insights customer was asking about how order refunds work in the app because Shopify's behavior is odd.
Shopify subtracts refunds for repeat customers that come in during that month from the repeat customer revenue in that month.
We had to issue a bunch of refunds in Jan and Feb so it is skewing our Jan and Feb numbers even though the orders were placed in Nov and Dec.
The source of Shopify's skew is that they are logging refunds against the period when the refund happened (like accountant would) and not like a behavior entry (logged against the period when the order behavior happened).
That will cause high-refund months to appear to under-perform, even if new orders were perfectly fine. If you know about that, you can remove the refund amounts from the gross sales in a month but not everyone knows to do that.
In contrast, Repeat Customer Insights uses the gross revenue of the order minus any discounts or refunds. That's all logged at the time the order was placed, even if the refund happened months later.
That way the refund reduces the original month, which should the long-term customer behavior.
One interesting way to think about this is to go to the extreme. What if you had to refund every order from 6 months ago because someone sent you a lot of poor-quality customers who never should have bought?
In Shopify's reports, this month would look bad and that prior month would still look great. Even though the customers were crap and you're eating a ton of costs.
In a true behavioral system, this month would look normal and that prior month would look bad. That'll lead you to check where all the bad orders came from and find out that someone sent a ton of bad traffic.
Both ways are fine but for different reasons:
- Shopify's is fine for bookkeeping.
- Repeat Customer Insights is great for understanding what your customers are doing.
P.S. I also caution stores using with Shopify's "new" or "repeat" customer fields as they really skew historic data. See point #1 in this article.
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