Now and then I'll get a Repeat Customer Insights customer trying to reconcile their Shopify data to the app.
Or a Shopify store trying to compare Google Analytics traffic to Shopify's Acquisition reports.
It never goes well for the person comparing.
Every analytics systems has its own way of collecting and measuring the metrics. Any differences will show up as different numbers.
What if your Google Analytics has a different cookie lifespan than Shopify's cookies?
You're going to get different bounce rates, time-on-site, landing page attribution, etc. One difference can make everything slightly off and make you feel like there's a problem.
Even in Repeat Customer Insights where the source data is the same (Shopify's data), did you know that Shopify doesn't track the historic state for repeat customers? A customer is defined as a repeat customer if they have more than one order, even if you're running a historic report at the time of their first order. That alone wrecks any sort of report that looks at new-vs-returning customers.
Then there's timezones. Oh timezones...
Shopify uses your store timezone for date boundaries so if your timezone changes then that order you got 2 minutes past midnight, might shift into a completely different day (or month or even year).
(For fun, run a sales report for a couple of days grouped by hour in one tab. Change your store timezone to be 12 hours different in another tab. Run the sales report again in a third tab.)
This doesn't even include any data transformations that analytics systems apply on top of their data which means reconciliation gets even harder. (e.g. Google Analytics' data sampling...)
One could go mad trying to resolve of these and get two analytics systems to match.
But there's an easy solution.
Ignore the discrepancies between systems and look at how the data looks inside each system.
Sure check that most of the data is loaded. If Google Analytics is showing 10x the traffic Shopify is showing, check for a problem. Maybe Google's tracker is on extra domains or duplicated. Or maybe Shopify's tracker just got on some ad-block lists.
But if it's off by 5%, ignore the differences and use the system.
The point of data and data analysis is to support decision-making. If the data in a system can help you make a decision, follow it. Even if the data is slightly off from another system.
That's why a lot of the recommendations Repeat Customer Insights are comparing your data against itself. If a metric is performing better or worse, that's a time when you need to look and make decision about what to do.
Retain the best customers and leave the worst for your competitors to steal
If you're having problems with customers not coming back or defecting to competitors, Repeat Customer Insights might help uncover why that's happening.
Using its analyses you can figure out how to better target the good customers and let the bad ones go elsewhere.