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How to avoid bad reviews of your mobile app

May 17, 2013 · Manish Lachwani

Want to avoid 1-star ratings? Focus on quality and performance

App downloads are exponential as a function of rank, and an increase in mobile app performance issues and response times is often closely correlated to a decrease in user conversions.  In the App Store rankings, the Top 50 is the inflection point for strong sales.  A 4.5-star app, for example, will be downloaded about 3.7x more than a 3.5-star app.  As Margo Visitacion of Forrester said, “Mobile apps live and die by their ratings in an App Store… When the rating suffers, customer adoption suffers.”

The link between app quality and ratings is clear

Our team at Appurify analyzed the top 100 free apps in the App Store as of May 2013 to investigate the impact of common performance issues (e.g., crashing, lagging, and slow load times) on app reviews.[1] We found that a clear majority of 1-star reviews featured reports of poor app performance.

Screen Shot 2013-05-16 at 11.56.42 AMLikewise, an analysis of 25 million individual app reviews showed that the most frequently-used words in 1-star reviews were mostly tied to poor performance (e.g., work, time, fix) and lacked the positive performance-related descriptors in 5-star reviews (e.g., easy, great, fun).

Avoid bad reviews with comprehensive, automated pre-launch testing

Pre-launch testing and debugging is the only insurance policy for app developers. Appurify finds and fixes bugs before mobile app users, speeds up development and testing, and saves money.  The platform provides a complete solution for automatically testing and debugging mobile apps.  We offer live access to real, fully-configurable iOS and Android devices in the cloud, accompanied by powerful first-of-their-kind testing and debugging tools.  The Appurify platform is fundamentally changing the way mobile app developers think about QA.

[1] Analysis components included: number of total reviews; number of “most critical” reviews; number of “most critical” reviews attributable to performance; keyword counts for frequently-occurring descriptors including “crash,” “fail,” “laggy,”  “battery,” and “slow”