Get more accurate and consistent testing data across analytics, A/B testing, personalization & analytics tools.
Replicate rulesets from other tools in Google Analytics whenever possible.
Make sure you know how each tool works and the definitions for their terminology.
Replicate the user-level setups used in most testing tools in Google Analytics whenever possible.
Use custom reports and custom segments to find out whether you get matching, or at least closer numbers.
If a tool provides hit-, conversion-, or transaction-level data, compare the unique IDs to detect any missing data.
Build custom segments for each variant to fix discrepancies between Google Analytics and A/B testing tools.
Most testing tools provide guidelines for building Google Analytics segments in their documentation. Decide whether to build session or user-based segments; most testing tools default to user-based segments, while Google Analytics defaults to session-based ones.
Use unique names to keep track of custom dimensions used in testing tools, to avoid custom dimension values being overwritten by other experiments.
For example, name your experiments something like “Category Page – Filter Area Focus – Desktop – GA12” in your testing tool, where GA12 refers to the custom dimension 12 in Google Analytics, and the relevant setting in the testing tool for that experiment.
Alternatively, you can use custom Events instead of dimensions, but that usually requires custom development work.
Consider delays in data syncing between tools and platforms.
For example, if there’s a difference in numbers between Google Analytics and Google Optimize, it could be because all metrics in Google Optimize are first processed by Analytics and pushed to Optimize, which can take up to 12 hours.