Data Discrepancies

Common causes for Discrepancies between Ruler and Internal Products

Data Discrepancy & Match Rate

There may be instances where discrepancies occur between your internal data, third-party tools, and Ruler. These differences are expected due to variations in reporting methods and data quality and do not impact your overall reporting or results.

Potential Sources of Discrepancies:

Time Zone Differences: Ruler reports in UTC, so slight day-to-day differences in reports are expected.

Exchange Rates: Ruler converts your currency to your desired reporting currency, which may cause differences based on current exchange rates. Ensure that any sales or revenue data sent to Ruler is reported in the correct currency to avoid larger discrepancies.

Tagging Gaps: Differences may arise if the Ruler tag is missing from any pages or not set up to track certain conversions, affecting internal lead tracking versus Ruler’s data.

Cookie and JavaScript Blockers: As Ruler uses a first-party tracking tag, some website activity may not be tracked if a user has cookies blocked, preventing the tag from firing.

Non-Automatic Data Update: Once data is tracked and uploaded into Ruler Analytics, it will not update further if off-site changes occur. For example, sales that are cancelled, returned, or refunded will still be tracked and counted in Ruler Analytics unless manually deleted from your reports.

Factors Impacting Offline Data Match Rates:

Lead to Sale Time: Ruler will only match to leads captured while your Ruler tracking has been active and tracking all conversion points.

Non-Digital Lead Sources: Ruler matches only to leads captured by your setup. Any leads captured via offline sources that Ruler is not tracking will default to unmatched.

Review Areas for Reducing Discrepancies:

• Ensure all sales data is sent with the correct currency for the sale amount.
• Verify that the Ruler script is implemented on all pages and that all your conversion points are tagged correctly.

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Slight discrepancies between internal data sources and attribution platforms are common and should not be viewed as major concerns. These variances are typically within expected ranges and do not significantly impact the overall accuracy or actionable insights of the attribution model. You can still identify trends and optimise based on consistent patterns, rather than focusing on exact alignment across all data sources.

If you are concerned about the discrepancies you are seeing in your data, reviewing these areas can help identify and resolve any underlying issues.

If there are larger data discrepancies, please reach out to a member of the team who can assist you further.