Application usage intelligence
Understand how your software is actually used in the wild with aggregated, anonymized usage data from real users.
Real usage data, not self-reported metrics
Move beyond surveys and analytics that only capture engaged users. Access actual application usage time, session patterns, platform distribution, and market share data from users who may never interact with your in-app analytics. All data is fully anonymized and aggregated across thousands of users.
The blind spots in traditional analytics
Software vendors often struggle to get accurate usage data because traditional analytics have critical gaps:
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Ad blockers and privacy tools
Many users block in-app analytics, creating a biased sample that over-represents less privacy-conscious users
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Desktop app visibility gaps
Desktop applications often lack comprehensive usage tracking, especially for offline or enterprise deployments
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No competitor intelligence
Your analytics only show your app - you can't see how you stack up against competitors or understand market share
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Self-selection bias
Surveys and opt-in feedback only capture users who care enough to respond - missing the silent majority
How one software vendor used WhatPulse data
The situation
A productivity software vendor wanted to understand actual usage patterns for their application. Their internal analytics showed promising engagement, but they suspected they were missing a large segment of users. They also wanted to understand when users were most active, which platforms drove the most usage, and how their app compared to competitor adoption rates.
The data WhatPulse provided
We delivered anonymized, aggregated usage statistics for their application including:
Usage metrics:
- Average session duration across user base
- Time-of-day usage patterns (hourly breakdowns)
- Week vs. weekend usage differences
- Session frequency (daily, weekly, monthly users)
Market intelligence:
- Geographic distribution of active users
- Platform breakdown (Windows, macOS, Linux)
- Market share in productivity tools category
- Comparative usage vs. similar applications
Surprising discoveries
Peak usage shifted afternoon
Contrary to their "morning productivity" marketing message, peak usage occurred 2-4 PM, not 8-10 AM as assumed. Users were using the app for afternoon focus sessions, not morning planning.
Sessions shorter than expected
Average session time was 47 minutes, not the 90+ minutes their internal analytics suggested. This revealed that power users (who opted into analytics) weren't representative of typical usage.
Windows dominance underestimated
68% of usage came from Windows vs. 32% macOS. Their team had focused heavily on Mac features because Mac users were more vocal in feedback - but Windows users represented the silent majority.
Solid market position validated
With 12% market share in the productivity tools category, they were ranked #4 - better than they estimated. This data helped them secure additional funding.
Strategic decisions informed by data
The vendor used these insights to make several strategic pivots:
- Shifted messaging from "morning productivity" to "afternoon deep work sessions" in marketing materials
- Optimized onboarding for shorter sessions instead of assuming users would spend 90+ minutes
- Prioritized Windows development and allocated 70% of engineering resources to Windows features
- Scheduled maintenance windows during 4-6 AM when usage was lowest (not during assumed "lunch hours")
- Benchmarked performance against the top 3 competitors and identified feature gaps
- Used market share data in investor presentations, securing Series A funding
What you'll receive
Time-series usage data
Hourly, daily, and weekly usage patterns showing when your application is most active. Understand peak hours, seasonal trends, and usage cadence across different user segments.
Platform breakdowns
Detailed distribution across Windows, macOS, and Linux. Understand which platforms drive the most usage and where to focus development efforts.
Market share analytics
Your application's market share within its category compared to competitors. Track trends over time and understand your competitive position.
Geographic distribution
Where your users are located globally. Identify strong markets and untapped geographic opportunities for expansion or localization.
Sample data format
Example application usage report:
| Application | Avg Session (min) | Daily Active | Platform Split |
|---|---|---|---|
| Your App | 47 | 38,492 | Win: 68%, Mac: 32% |
| Competitor A | 62 | 89,234 | Win: 55%, Mac: 40%, Linux: 5% |
| Competitor B | 38 | 124,392 | Win: 72%, Mac: 28% |
Common use cases for application intelligence
Product development prioritization
Allocate engineering resources based on which platforms drive the most usage. If 70% of your users are on Windows, stop prioritizing Mac-only features.
Infrastructure planning
Schedule maintenance during actual low-usage hours, not when you assume users are offline. Right-size server capacity based on real peak usage patterns.
Marketing message optimization
Align your messaging with how users actually use your product. Don't market "morning productivity" if users primarily use your app in the afternoon.
Competitive benchmarking
Understand your market position and identify opportunities to capture market share from competitors. Track trends to see if you're gaining or losing ground.
Investor reporting
Provide third-party validated market share and usage data to investors. More credible than self-reported metrics and harder for investors to discount.
Localization decisions
Identify which countries have high adoption to prioritize localization efforts. Understand untapped markets where you have low but growing usage.
Privacy guarantee
Aggregated metrics only
All usage data is aggregated across thousands of users. No individual user sessions or behaviors are ever shared.
Application name and usage time only
We track which application is active and for how long - nothing about what users do inside the application.
Minimum sample sizes
We never provide data from fewer than 1,000 users to ensure complete anonymity and statistical significance.
GDPR compliant
Aggregated, anonymized data is not considered personal data under GDPR. Full compliance with all data protection regulations.
Understand how your app is really used
Get the usage intelligence you're missing from traditional analytics. Contact our data partnerships team to discuss your application and get a custom quote.