Search...

Frequency Matters: Using Distribution Analysis to Identify Your "Power Users"

Sign up for FREE - SolarEngine

In a healthy mobile app, not all users are created equal. Often, a tiny fraction of your audience—your "Power Users"—contributes the majority of your revenue or engagement. Identifying this group is impossible through simple averages. To find them, you need distribution analysis. This guide explains how to use SolarEngine to segment your users by behavior frequency and property value, turning raw logs into a high-precision targeting strategy.
 
 

I. Why Averages Hide Your Best Users

Relying on "Average Sessions per User" or "Average Revenue per User (ARPU)" is a common growth pitfall. If you have 1,000 users who spend nothing and 1 user who spends $1,000, your average is $1. But treating everyone as a "$1 user" will alienate your Whale and waste your ad budget on the 1,000 non-spenders.
Distribution analysis solves this by grouping users based on the frequency of an event or the specific value of an event property. It answers critical business questions like:
  • "How many users made 0 purchases versus 5+ purchases in the last month?"
  • "What is the distribution of users across different payment price points?"

 

II. The SolarEngine Method: Customizing Your Intervals

SolarEngine’s Distribution Analysis model provides the flexibility needed to match your specific business model.

  1. Flexible Grouping (Default, Discrete, or Custom)

You can choose how to bucket your data depending on what you are measuring:
  • Default Intervals: Let the system automatically generate logical groups.
  • Discrete Grouping: Analyze each unique value separately—perfect for tracking "Level Reached" in a game.
  • Custom Intervals: Define your own buckets (e.g., $1-$10, $11-$50, $51+) to align with your store's pricing tiers.
 

  1. Multi-Dimensional Insights

You aren't limited to just one view. SolarEngine allows you to refine these distributions through:
  • Conditional Filtering: Only see the distribution of users from a specific country or acquisition source().
  • Analysis Entity Switching: Switch from "User ID" to "Account ID" to see how product usage is distributed across different companies in a B2B scenario().
 

III. Strategic Application: From Analysis to Tags

The true power of distribution analysis is realized when you turn these insights into action.

Step 1: Identify the "Power User" Threshold

Run a distribution report on your most valuable event (e.g., "Purchase"). You might find that users who purchase more than 3 times have a significantly higher retention rate.

Step 2: Create a Result Tag

Once you've identified the "3+ purchases" cohort, SolarEngine allows you to save these specific users as User Segments (Tags). Specifically, you can use Result Tags saved directly from your analysis findings.

Step 3: Compare and Optimize

Take that "Power User" tag and apply it to a Users Analysis report to perform Crowd Comparison.
  • Compare the attributes of your Power Users against your Churned Users.
  • Discover if your Power Users are primarily using a specific device brand or came from a particular marketing campaign.

 

IV. Validating the Edge Cases: User Look-Up

When your distribution analysis reveals a group of extreme outliers—users with 100+ sessions a day or $1,000+ in daily spend—you need to verify if they are truly "Power Users" or potential fraud.
Use SolarEngine’s User Look-Up feature to search for their Account ID or Device ID. By reviewing their detailed behavior logs, you can confirm if their activity is genuine, allowing you to refine your high-value segments with total confidence.
 

Conclusion

Understanding user behavior patterns is about finding the signal in the noise. By using SolarEngine’s Distribution Analysis to break away from averages, you can pinpoint the exact behaviors that define your most valuable users. When you know who your Power Users are, you can stop guessing and start growing.
 
Sign up for FREE - SolarEngine
Previous
Beyond the Percentage: How to Measure the LTV of Your Retained Users
Next
Why Your Marketing Funnel is Lying to You: The Case for Non-Linear Analysis
Last modified: 2026-02-25Powered by