When reconciling game performance data, follow these key steps: Verify data sources: Ensure both parties pull data from the same source, whether from an MMP partner, BI tool, or via API. Align time zones: Make sure both sides use the same time zone, especially when working with partners in different regions like China. Ensure target consistency: Confirm that both you and your partner are measuring the same metrics (e.g., Gross ROAS vs. Net ROAS). Account for attribution windows: Ensure alignment on the attribution window used by both platforms to avoid discrepancies. Check tracking parameters: Make sure campaign and ad group IDs are correctly tracked across both platforms.

Technically speaking, it usually stems from an incorrectly configured backend or an incomplete logic that’s often a result of miscommunication between development team and project managers. In an ideal world, this is bound to happen and is completely acceptable unless you’re making the same mistake again and again. Here’s what you can do: 1- Data cross-referencing at respective data points before aggregation 2- Consistent and clear communication between the teams 3- Up-to-date systems to keep up with growing technical demands 4- Enough time for execution to prevent any missed data points 5- Coordination and uniformity in performing and executing assigned tasks

Discrepancies in game performance data? Don’t let them derail your insights! Here’s how to stay on track: 📊 Identify source reliability: Determine which data streams are most consistent and trustworthy. 🧩 Align metrics: Ensure all sources measure the same variables in comparable ways. 🔍 Dive into anomalies: Investigate outliers to understand the story behind the numbers. 📚 Document processes: Maintain a clear record of data sources and reconciliation steps for transparency. Data integrity drives decisions. How do you handle conflicting metrics? Let’s share best practices! ���📈 #GameAnalytics #DataDrivenGaming

I’d first verify the sources to ensure they’re pulling from the same timeframes and metrics. Differences often stem from mismatched definitions - like “active users” meaning slightly different things across platforms - so standardizing terms is key. Next, I’d cross-check data manually for obvious errors or anomalies and prioritize the source with the most reliable tracking setup. If the issue persists, I’d dig into the underlying systems, consulting with data teams if needed. Clear documentation of findings ensures consistent interpretation moving forward.

Validate and align data across product analytics (e.g. Firebase, GameAnalytics) and business analytics, including ad networks (e.g. AppLovin, Google Ads), MMPs (e.g. AppsFlyer) and BI tools (e.g. Tableau). Clearly establish the 'source of truth' for each KPI to eliminate confusion. Address measurement differences such as timing gaps, calculation methods, and reporting delays to foster clarity and a shared understanding across teams.