Skip to content
  • There are no suggestions because the search field is empty.

A Guide to Solving Data Discrepancies Between Convert.com and Other Platforms

Solve reporting gaps between Convert.com and GA4/Shopify with universal segmentation, aligned metrics & time zones, latency checks, and clear attribution rules.

IN THIS ARTICLE YOU WILL:

It's common to see differences in data between your A/B testing tool (Convert.com) and other platforms like Google Analytics, Shopify, or ad platforms. These discrepancies can be confusing, but they are almost always explainable. This article will guide you through a systematic process to find and fix the root cause of these issues.

The Core Principle: Universal Segmentation

The most important step in troubleshooting is to ensure you are comparing the same user population across all platforms. Without this, your data comparison is flawed from the start. You must segment your data to only include visitors who were part of your A/B test.

How to Segment Your Data

  • In Google Analytics (GA4): Use the custom audiences automatically created by the Convert GA4 integration. This ensures you are only looking at users who were exposed to the experiment.
  • In Shopify: Utilize the custom order attributes that are appended to each order by the Convert Shopify App. Filter your sales data using the experiment id and variation id to see the exact results for your test.

In Other Platforms: Look for experiment IDs, variation IDs, URL parameters, or or other unique identifiers to filter your data.

The Troubleshooting Process: A Step-by-Step Guide

After segmenting your data, follow this checklist to identify the source of the discrepancy.

data_analysis_flowchart(1)

Step 1: Metric Alignment 

A common mistake is comparing different metrics.

  • Problem: Comparing Convert.com's Visitors (a unique user count) to Google Analytics' Sessions (which counts every visit, including from the same user).
  • Solution: Always compare Convert.com's Visitors metric to Google Analytics' Users metric.

Step 2: Dates and Time Zone Alignment 

Even a small time zone difference can cause data to appear inconsistent.

  • Problem: Mismatched date ranges or different time zones (e.g., Convert.com's timezone vs. your local time).
  • Solution: Convert.com's report timezone is adjustable. Manually align the time zone setting in your Convert.com reports to match your other platforms. Ensure you are comparing the exact same date range, which can help smooth out daily differences.

Step 3: Data Latency 

Not all platforms update in real-time.

  • Problem: Comparing Convert.com's real-time data with platforms that have a reporting delay. GA4 can take up to 72 hours for data to fully populate.
  • Solution: Wait for at least 72 hours for all your data to fully process before making a comparison.

Step 4: Technical Implementation

A single technical issue can invalidate your entire test.

  • Problem: The tracking code is not firing on all test pages, or there are script conflicts. Race conditions between the Convert snippet and other tags, like GA4, can cause visitor counts to be lower if the GA4 tag doesn't fire in time.
  • Solution: Use a debugger tool (like the Convert Chrome Debugger or Google Tag Assistant) to confirm that your tracking code is correctly placed and functioning on every page of your test.

Step 5: Platform-Specific Rules

Different platforms have unique rules for data collection and attribution.

  • Google Analytics: The way you've set up goals or events may differ from Convert's default tracking.
  • Shopify: How sales with discounts or refunds are handled can vary between the two platforms, causing discrepancies in revenue numbers.
  • Other Platforms: The biggest source of discrepancies is a different attribution model. An ad platform might credit a conversion to a "last-click" or "view-through" interaction, while Convert credits the conversion to the experiment that the user was part of.

Final Check: Outside Factors

If you've followed all the steps and a small discrepancy still exists, it's likely due to outside factors. These are normal and expected.

  • Look for: Ad blockers, browser privacy settings, or IP address filters that may be blocking data collection on one platform but not the other.

A small discrepancy of 5-15% is considered normal and acceptable once all major issues have been addressed.