10 Things to Check Before You Trust Your User Behavior Data

You have the dashboard. You have the numbers. But are you sure you're looking at clean, accurate, and useful data?

In user behavior analysis, bad assumptions often sneak in through dirty data, skewed interpretations, or incomplete tracking. Before you use behavior metrics to guide growth decisions, here’s a checklist of 10 critical questions to verify the integrity and clarity of your analytics setup.

Let’s make sure your data isn’t lying to you.

1. Have You Segmented Your Metrics by Source?

Averages mean nothing if they blend paid, organic, and social traffic. Always split key behavior indicators (retention, session length, revenue) by source, campaign, and channel to uncover hidden patterns or poor-quality traffic.

👉 If you’re using SolarEngine, use multi-dimensional filters to analyze behavior by acquisition source and deeplink path.

2. Are You Looking at Medians or Just Means?

A single power user can spike your average. Use medians and percentiles (P50, P90, P10) to understand how the “real majority” of your users behave, not just the top performers.

3. Do You Have Real-Time Data Visibility?

If you're making campaign decisions based on yesterday’s numbers, you're already behind. Make sure your analytics stack supports real-time or near real-time tracking—especially during high-traffic periods like sales events.

4. Are Outliers and Abnormal Users Being Filtered?

Users with 10-second lifespans or 100 ad clicks in 2 minutes can distort everything from LTV to retention. Flag suspicious sessions and exclude them from core KPIs.

5. Have You Mapped Real User Paths, Not Just Funnels?

Funnels are helpful—but users rarely behave linearly. Use path analysis to see where they branch off, loop back, or drop off unexpectedly.

6. Are All Key Events Properly Tracked and Named?

Make sure all critical events (e.g., registration, checkout, ad view, upgrade) are correctly tracked, named consistently, and implemented across platforms. A missing or misfired event can throw off entire reports.

7. Is Your Attribution Data Synced with Behavior Logs?

You need to know not just what users do, but where they came from. Make sure user-level attribution (channel, campaign, creative) is tied to their behavior timeline.

👉 SolarEngine’s integrated attribution lets you trace every user action back to its source for clearer ROI mapping.

8. Are Your Cohorts Logical and Up-to-Date?

Cohorts should reflect real user stages: first-time users, high-LTV groups, churn risks. Don’t lump everyone into generic buckets. Update cohort definitions as user behavior evolves.

9. Are You Comparing Against Benchmarks or Just Guessing?

How do you know if a 25% D7 retention is good? Use industry benchmarks or internal historical data to contextualize your metrics—and avoid over- or underreacting to outliers.

10. Do You Regularly Review Key Metrics for Unusual Spikes or Drops?

User behavior changes quickly—especially during campaigns or version updates. Set a routine to check daily or hourly trends for core KPIs like sign-up rate, ad engagement, or session depth. Manual reviews may not be as fast as real-time alerts, but they're essential if your tool doesn’t support automated notifications.