The App Trap: Why Every Mobile App Needs Anomaly Detection

If you’re one of the many consumers using native apps 90% of the time you’re on your smartphone, you know first hand that mobile apps are big business. So big, in fact, that they are expected to generate 188.9 billion U.S. dollars in revenues via app stores and in-app advertising by 2020.

There’s an app for just about everything, from games to ebooks, to dating, cooking, shipping, sharing photos and more, businesses are developing more and more mobile apps to reach and engage their customers. But do these apps make money? In addition to charging for the app, many app developers monetize through advertising, in-app purchases, referrals and cross promotions.

Once using an app, businesses offer targeted advertising per user, options for premium content like access to extra levels or additional features, suggestions for additional apps by the same company or for related content that the company will receive revenue from for referring anyone who clicks through and converts.

With so many moving parts (e.g. frontend, backend, advertising platforms, partners), there are a multitude of opportunities for something to break, such as partner integration or data format changes, device changes like OS updates or new devices, external changes like media coverage or social media exposure, and company changes like deployments, new game releases, AB tests and more.

Just like the butterfly effect, where the flap of a butterfly’s wings can cause a string of events leading to a huge storm, if one element of an application is working less than optimally, it can cause major problems elsewhere, which translates into unhappy customers, uninstalls, revenue losses and drops in market share.

With traditional BI and monitoring tools like dashboards and alerts, you may only realize that something has broken down once your uninstall numbers begin to rise or you notice that users have stopped returning. Only a small percentage of very dedicated users will try a crashing app more than twice, so fixing the problem before you’ve lost users in droves is of key importance.

So, how can you mitigate problems on your business’s mobile app keeping users happy and engaged?

In a recent session at Strata Data San Jose, Ira Cohen, Anodot’s Chief Data Scientist and co-founder, presented “The App Trap: Why Every Mobile App Needs Anomaly Detection,” showing how to use automated anomaly detection to monitor all areas of your mobile app to fully optimize it.

Watch the full video below to learn more about the processes involved in automated anomaly detection — metric collection, normal behavior learning, abnormal behavior learning, behavior topology learning and feedback-based learning — and how, together, they can keep your app on track, making money, and keeping users happy.