app-release-feedback-analysis

Compare app review themes around documented releases using Crawlora version notes and live/stored reviews. Use for a dated release-feedback brief with platform/app/version identity, review timestamps, sampling, missing release history, and causal limits preserved.

Access and scope

Set CRAWLORA_API_KEY to your Crawlora key. Prefer the listed Crawlora MCP tools when connected; otherwise use the bundled scripts/crawlora.sh, which sends x-api-key to https://api.crawlora.net/api/v1. Read reference/endpoints.md for exact methods and parameters. Check application code as well as HTTP status; payloads are inside data. Stop on authentication errors, back off on 429, and retry a transient upstream failure once. Bound calls/pages by the requested scope and credit budget. Retain source IDs, URLs, source dates, and observation/crawl times separately.

Describe observed feedback around a specified release or release set. Establish platform, exact app ID, storefront/language, release dates/versions, review window, and comparable sampling rules before collecting evidence.

Align releases and review observations

• Resolve iOS numeric track IDs and Android package IDs separately. Verify publisher/title/domain before cross-platform grouping. App Store version history supplies release notes/dates; Google Play detail supplies current app context, not a complete historical release timeline here. If a needed Android release date is not verified elsewhere or supplied by the user, leave the historical comparison unavailable rather than inventing a version-history route.
• Preserve release version, notes, date precision, platform/storefront and source. Dates can differ by market or rollout; a nominal release date does not prove every reviewer had the same version. Generic release notes are publisher claims, not evidence a reported issue was actually fixed.
• Collect matching live/stored review samples. Stored filters include store, exact app ID, country, minimum score and sort, but no date/version parameter; filter returned dates/versions locally and respect the result window. Helpful- ranked or score-filtered samples need their own disclosed selection bias.
• Deduplicate by source review ID and retain update/observation timestamps. A review's displayed version is stronger attribution than proximity to a release date; keep explicit-version, time-only, conflicting and unknown matches separate. Developer replies are not customer reviews or independent verification.

sh

scripts/crawlora.sh /appstore/search term="note taking" country=us
# Resolve an exact app ID before release notes and eligible review collection:
# scripts/crawlora.sh /datasets/apps-reviews/search store=ios app_id="$TRACK_ID" country=us sort=recent page=1 page_size=10

Report changes without claiming an experiment

Use the same theme rubric and source/window/sample budget before and after. Count unique eligible reviews and show theme n / review N with dates and version-attribution basis. Multi-theme counts can exceed N. Unknown/undated reviews are a separate bucket, not silently placed into a preferred interval. Cross-store star populations and filtered samples are not interchangeable.

Return a release and attribution ledger, comparable theme tables, short cited review excerpts, notes/reply observations, missing data and follow-up hypotheses. A change in sampled themes or average stars is not proof the release caused it: rollouts, selection, moderation, seasonality and changing users can differ. Do not infer crash rates, retention, install volume, or population improvement from reviews alone. Do not install apps, run private telemetry, contact reviewers, or schedule monitoring from this research request.