Whether you’re an ecommerce veteran, or newer to the field, you’ve likely encountered the frustration of revenue discrepancies between Google Analytics and your ecommerce platform. At first, it might seem like a minor issue—less than 2% of the total. But in some cases, the gap can be as much as 25% or greater, and when the C-Suite wants answers, “We don’t know why” isn’t going to cut it.
These discrepancies often stem from a collection of smaller issues that, when added up, create big data problems. And unreliable data can lead to poor decision-making, and no one wants to be responsible for a hiccup that costs the company.
At Tadpull, we’ve traveled this road many times with clients in all industries. Below, we’ve outlined some of the usual suspects contributing to revenue differences and what you can do about them.
Taxes & Shipping
A key reason for revenue discrepancies lies in how taxes and shipping are reported. Google Analytics (GA4) includes both taxes and shipping in its revenue totals (for purchase revenue). However, some implementations exclude them by default, or report them separately. Each merchant reports on these figures differently, so if you’re seeing a significant gap, check whether your preferred reporting tool is reporting taxes and shipping the same way as GA4.
Returns
Returns data can cause significant differences in revenue totals, especially if it’s not uploaded back into Google Analytics. Many merchants don’t take the time to sync return information, which means GA4 is still showing pre-return figures. Whether you’re using your ecommerce platform or another reporting system, verify if returns are being included or excluded. Returns systems are often tricky to manage, and this oversight can create confusion when reconciling data.
Order Edits
Customers change their minds—often! They’ll call customer service to add or remove items from an order or apply a discount code that didn’t work at checkout. While it seems harmless, these post-checkout edits contribute to discrepancies in revenue data. The more these edits occur, the harder it is to keep revenue figures perfectly aligned between systems. We’ve also seen return and exchange platforms edit orders differently, which can cause discrepancies in reporting if not noted and accounted for in other systems.
Time Horizons
Shorter time periods, such as a week or a month, often show tolerable discrepancies. But when you expand your view to a quarter or longer, the difference can become harder to stomach. One key issue here is that Google Analytics doesn’t allow historical data updates. For instance, imagine a customer accidentally orders $15,000 worth of products and cancels the order a few months later. While it’s easy to identify this large discrepancy, other issues, like time zone differences or daylight savings settings, can be sneakier.
For example, timezones play a crucial role in aligning reporting between platforms. If the timezone is different between your reporting systems, even by just an hour, the one-hour shift each day can cause discrepancies when orders are classified just before or after midnight. Even if you don’t have many late-night shoppers, this can add up over time.
Browsers & Tracking Blockers
If a customer checks out while using an ad blocker in the browser, Google Analytics might not record the transaction at all. If they are using a browser like Safari or Brave that by default blocks tracking or disables cookies, Google Analytics might not capture the touchpoints through that customer’s journey to more accurately classify their transaction within a marketing channel. This is another common cause of discrepancies that can be hard to detect without digging into browser and device reports.
So, What Can You Do?
At Tadpull, our recommended approach begins with a data export from your ecommerce system for a specific period—usually one month. Here’s a step-by-step outline to get started:
- Export order data from your ecommerce platform (Hint: be sure to include taxes and shipping costs because those are included in Google Analytics orders).
- Match each order with Google Analytics, timestamp by timestamp.
- Compare totals to identify discrepancies.
- Use tools (like Python) to help streamline this process and flag inconsistencies.
This detailed, order-by-order comparison can uncover where the discrepancies are happening. We use specialized tools to make the process easier and faster, but this manual approach forms the backbone of any reconciliation effort.
Data validation is something we take seriously and spend a lot of time on when onboarding new clients onto our software platform. And we revisit the topic on a regular cadence because you never want to discover an issue months later. The thing that we find helps most is the subject matter expertise in ecommerce and digital marketing to start the forensic analysis with some good hypotheses.
If you’re struggling with revenue differences and want to get to the bottom of it, we should talk! Don’t waste time with unreliable data—reach out to our team at Pond for a consultation today. We can help you pinpoint the problems and make smarter, data-driven decisions for your ecommerce business.