Marketing teams rarely struggle because one dashboard is obviously wrong. The harder problem is that every platform can be internally consistent while still disagreeing with the numbers another team is using for reporting.

The Scale of the Problem

Consider a team running campaigns across multiple ad and analytics platforms. Each source has its own attribution model, timezone handling, refresh timing, and filtering rules. When reporting time arrives, the team is often comparing numbers that were never designed to match perfectly without context.

Why Discrepancies Happen

1. Attribution Window Differences

Google Ads uses a 30-day click window. Meta uses 7-day click + 1-day view. LinkedIn uses 30-day click + 1-day view. When a conversion happens, each platform claims it differently — and they can’t all be right.

2. Timezone Handling

Google Ads reports in your account timezone. Meta reports in UTC. If your team spans multiple timezones, end-of-day numbers will never match up.

3. Currency Conversion Timing

Platforms convert currencies at different times. Google Ads might use the exchange rate at the time of the click, while Meta uses the rate at the time of conversion. For volatile currencies, this can create significant variance.

4. Bot and Fraud Filtering

Each platform has its own bot detection system. A click that Google Ads filters out as fraudulent might still count on Meta — or vice versa. There’s no standard definition of a “valid” click.

The Operational Cost

Beyond the direct financial impact, data discrepancies create:

• Decision drag: When numbers don’t match, teams slow down before they can agree on what changed
• Wasted analyst time: Hours spent reconciling instead of analyzing
• Missed optimization opportunities: If you can’t trust your data, you can’t optimize effectively
• Reporting delays: Monthly reconciliation pushes insights weeks into the future

The Solution: Automated Reconciliation

Modern reconciliation platforms like Skewix solve this by:

1. Connecting all platforms in a unified data model
2. Detecting discrepancies automatically with configurable thresholds
3. Explaining root causes with AI-powered analysis
4. Delivering actionable insights instead of raw variance reports

The result is a cleaner review loop: teams can identify where numbers diverge, decide whether the variance matters, and document the context before sharing a report.

Getting Started

The first step is understanding your current state. Run a discrepancy analysis across the sources you already use, then review which differences are expected and which need investigation.

Ready to evaluate a structured reconciliation workflow? Open Skewix.