Amazon calculates a product’s star ratings based on a machine learned model instead of a raw data average. The model takes into account factors including the age of a rating, whether the ratings are from verified purchasers, and factors that establish reviewer trustworthiness.
I would recommend this title to any developer working in the Android and IOS ecosystems.
It's full of time saving advice. For example, I had no idea a service like AppThwack (to easily check if your app crashes on more than 200 devices) even existed before reading. He has helpful tips for getting through the IOS review process, and how to manage updates without causing massive problems for your users. For the developer, there's plenty of information about how to architect your software in order to cause as few headaches as possible.
Jason also produces tons of insights. For example, I loved the data he's gathered on app reviews, and the analysis of why some apps get clusters of 1 star. It turns out that there are no clear metrics about who actually writes reviews, and it's not clear if they're a good sample of your user population.
Finally, I trust this book because the author has the credentials to back up his writing. He's worked in Test at Google and Microsoft, and he is now a director at Applause (formerly uTest), which is basically the industry leader in QA testing for apps. If anyone knows about app quality, he does.
Once again Jason hits it out of the park. I've always enjoyed his work and find his tips invaluable for use with my products and teams. We need to rethink our strategies for app quality assurance and software engineering. This book will make you question your assumptions and consider forming a new plan. You can deliver better software in less time if you are willing to do things differently. Listen to this guy.