Every year, marketing teams waste billions of dollars on unaccounted ad spend discrepancies. These aren’t just rounding errors — they’re systematic differences between what platforms report and what actually happened.

The Scale of the Problem

Consider this: a typical enterprise runs campaigns across Google Ads, Meta, LinkedIn, TikTok, and Microsoft Ads. Each platform uses different attribution models, timezone handling, and bot filtering. When you try to reconcile these numbers, you’re comparing apples to oranges to bananas.

A recent study found that the average enterprise has a 12.4% discrepancy between platform-reported spend and actual billing. For a company spending $1M/month on ads, that’s $124,000 in unexplained variance — every single month.

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 Hidden Costs

Beyond the direct financial impact, data discrepancies create:

Decision paralysis: When numbers don’t match, teams can’t agree on what’s working
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:

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

The result? Teams that used to spend 20+ hours/month on manual reconciliation now spend zero. And they catch discrepancies in hours instead of weeks.

Getting Started

The first step is understanding your current state. Run a discrepancy analysis across all your platforms for the last 30 days. You’ll likely be surprised by what you find.

Ready to eliminate manual reconciliation? Start your free trial today.