FAQ for ASO Specialists

Does it track keyword sentiment for optimization purposes?

While Parse My App doesn't have a dedicated keyword sentiment tracking dashboard yet, our AI analysis extracts keywords and phrases directly from user reviews as part of the pain points, praises, and feature requests.

These keywords reflect the actual language users employ when describing the app, making them incredibly valuable for ASO. For instance, if users frequently mention "offline mode," "sync issues," or "battery drain," those are natural keywords you should consider incorporating into your app description and metadata.

Unlike traditional keyword tools that show search volume, Parse My App shows you what users actually care about based on review sentiment. This helps you prioritize keywords that resonate emotionally and address real pain points.

Future updates will include more structured keyword extraction and sentiment scoring specifically for ASO workflows.

Can I filter reviews by app version to catch post-update issues?

Currently, version filtering is not available as a built-in feature. However, we fetch the latest reviews in real-time, which typically include recent app versions.

If you're tracking post-update sentiment, you can run an analysis immediately after a new version release and compare it to a previous analysis saved in your dashboard. This gives you a before/after snapshot of user sentiment.

Version-based filtering is a frequently requested feature and is on our roadmap for future releases.

How do you handle fake reviews, bot detection, and spam?

We fetch reviews directly from the App Store and Google Play, which have their own spam and fake review detection systems. We do not add an additional layer of spam filtering on top of the store data.

However, our AI models are designed to recognize patterns. If there's a surge of near-identical reviews (a common sign of fake review farms), the AI will surface similar pain points or praises, and you'll notice the repetition in the quotes.

Additionally, our stratified sampling methodology helps reduce the impact of spam. By proportionally sampling across all star ratings, a flood of fake 5-star reviews won't dominate the analysis. You'll still see insights from 1-star and 3-star reviews.

If you suspect fake reviews in a specific app, you can cross-reference the insights with the full review list (displayed in the analysis dashboard) to manually verify authenticity.

Is there historical data beyond the 12-week trend window?

Currently, the weekly trends feature visualizes the past 12 weeks of rating and volume data. This is sufficient for tracking recent sentiment shifts, post-update reactions, and seasonal trends.

Historical data beyond 12 weeks is not available in the current version, but we're working on extending the trend window to 6+ months. This will allow ASO specialists to track long-term rating trajectories and identify multi-month patterns.

If you need historical insights now, you can save analyses to your dashboard and compare them over time manually. Each saved analysis captures a snapshot of the app's review state at that moment.

Can I benchmark my app against category averages and top competitors?

Yes, indirectly. While we don't have automated category benchmarking yet, you can manually analyze the top competitors in your category and compare their pain points, praises, and star ratings to your own app.

For example, if you're optimizing a fitness app, analyze the top 5 apps in the Health & Fitness category. Save each analysis to your dashboard. Then, compare what users love about each app and where they all fall short.

This gives you competitive intelligence and helps you position your app's strengths against category norms.

Automated category benchmarking (e.g., "Your app's rating is 0.3 stars above category average") is planned for future releases.