CSV Export
Export filtered review data for deeper analysis, reporting, and integration with external tools
Take your review data beyond AppReply with powerful CSV export capabilities. Whether you need to create executive reports, perform statistical analysis, or integrate with business intelligence tools, CSV export gives you complete access to your filtered review dataset.
Paid feature: CSV export is available on paid plans only. The export includes all reviews currently visible based on your applied filters.
Understanding what gets exported
Your CSV export contains comprehensive review data with all available information that AppReply has collected and processed.
The core review information includes unique identifiers for each review, complete user feedback content, star ratings from 1-5, reviewer display names when available, review submission dates, app versions users reviewed, platform identification (iOS or Google Play), and detected language of review content.
Device and location data provides additional context where available, including device model and OS version information, user country or region data, and precise language codes for localization insights.
Response information tracks your engagement efforts, showing response status (none, manual, or automated), complete developer response content if any exists, timestamps for when responses were sent, and response type classification showing how responses were generated.
AppReply-specific data includes additional intelligence the platform provides, such as English translations for foreign language reviews, applied category tags, internal processing status for each review, and integration status with app store connections.
Filter first, then export: Apply your desired filters before exporting to get exactly the data subset you need rather than processing the entire dataset.
The simple export process
Getting your data is straightforward and designed to handle datasets of various sizes efficiently.
Filter to target the specific data you need
You might focus on particular time periods like launch weeks or post-update periods, analyze specific satisfaction levels by filtering star ratings, segment by language for international market analysis, track engagement by filtering response status, or export reviews mentioning specific topics through keyword searches.
Click the "Export CSV" button
The system validates your plan to ensure CSV export access, processes your current filters to determine the exact dataset scope, generates the file with all matching reviews, and prepares a secure, temporary download link
Save the file
Now the data ready for immediate use in Excel, Google Sheets, business intelligence tools, statistical software like R or Python pandas, database systems for long-term storage, or reporting tools for stakeholder presentations
File size considerations: Large exports (1000+ reviews) may take a few moments to generate. Very large datasets might be split into multiple files for optimal performance.
Working effectively with exported data
Once you have your CSV file, several approaches can maximize the value of your review data.
For Excel and Google Sheets analysis, essential formulas help you quickly analyze patterns. Count 5-star reviews with =COUNTIFS(Rating,"=5"), calculate average ratings using =AVERAGE(Rating), count replied reviews with =COUNTIFS(Response_Status,"<>None"), and group by time periods using =TEXT(Review_Date,"YYYY-MM"). Pivot table suggestions include using review months or weeks as rows for timeline analysis, star ratings as columns for distribution views, count of reviews and average ratings as values, and platform, language, or response status as filters.
Regular exports: Consider setting up monthly exports to build a historical dataset for long-term trend analysis and year-over-year comparisons.
CSV export transforms AppReply from a review management tool into a comprehensive data source that can inform every aspect of your app development, marketing, and user experience strategy. When you combine regular exports with systematic analysis approaches, you create a powerful feedback loop that keeps your app aligned with user needs and market demands.