The world of mobile service and apps has many moving parts. For mobile operators, there are issues of roaming and service quality. And app providers need to ensure their apps are working properly across every OS and geography, that they are profitable, users are engaged, and so on. With so many elements, there are many opportunities for something to break. If one element is working less than optimally, it can cause problems elsewhere, resulting in issues that annoy users and cause revenue leaks.
Many companies use dashboards and alerts to track what is happening on their systems and apps. Important KPIs include number of user installations, revenue, ad conversion, and cross-promotion success rate (for apps) and minutes of usage (MOU), call drop rate (CDR), average revenue per user (ARPU), and average revenue per minute (ARPM) (for service providers). Each of these KPIs has its own typical pattern, which may also have an associated trend. And each KPI can be broken down much further into more granular metrics—the more granularity the better, since it allows you to find the root cause more quickly if something goes wrong or to quickly leverage something that is working well. Add to that the fact that you probably want to measure these at a minimum every minute, and you will quickly find that you are overwhelmed with dashboards and alerts. This is where anomaly detection comes into play. Using machine learning, you can learn the normal behavior of each metric or even combinations of metrics (e.g., revenue per install) and identify in real time if a metric behaves abnormally.
Ira Cohen outlines ways to use anomaly detection to track everything mobile, from the service and roaming to specific apps, to fully optimize your mobile offerings, how to build an anomaly detection system, and what data to collect in order to keep your mobile service and apps and their revenue humming.
Ira Cohen is a cofounder and chief data scientist at Anodot, where he’s responsible for developing and inventing the company’s real-time multivariate anomaly detection algorithms that work with millions of time series signals. He holds a PhD in machine learning from the University of Illinois at Urbana-Champaign and has over 12 years of industry experience.
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