How Data Science Helps Prevent Churn at Avira, a 100-million User Company Calin-Andrei Burloiu Big Data Engineer Iulia Paov Machine Learning Engineer Strata + Hadoop World New York, 2015

About Avira Headquarters in Tettnang, Germany Security applications for –Windows –Mac OS –iOS –Android Awarded for malware detection

Big Data at Avira 430 million global installs 100 million users On-premise Hadoop cluster –7 worker nodes –30 TB logs and events –5 TB monthly new data

About User Churn Active Installs New Installs Uninstalls

Steps Diagnosis What can we measure? Which are the churn reasons? Understanding Why do users have issues? Who is likely to churn? Treatment & Prevention How can we react to prevent this?

Churn Diagnosis What can we measure? Which are the churn reasons?

What can we measure? Metrics –Churn rate –New Installs –Active users –Usage patterns

Computing Churn from Uninstall Events Uninstall events collected as application logs Pros: –An event is an uninstall for sure Some users reinstall Cons: –Some events are lost offline online

Computing Churn from User Inactivity Check user event logs Users are considered churned after some time of inactivity Pros: –More accurate Cons: –Requires waiting –Results come too late user inactive for 30 days user returns in the 31st day

User Inactivity Convergence

Estimating User Churn Predict monthly user churn rate –Predictor uninstall events –Outcome inactive users

Performing Survival Analysis

User Profile Consider –Devices –Behavior –Technical savviness –Business or consumer? –Errors Users User Profile

Churned Users Active Churned

Uninstall Surveys Ask users to complete a survey on uninstall Find churn reasons 1% users complete surveys Complaints from the past Uninstall Surveys

Lifecycle Surveys Complaints from the present Ask users to give feedback a few weeks after installation Questions based on insights from uninstall surveys Market research –Know your product’s market Lifecycle Surveys

Extracting Sentiments from Surveys Uninstall Surveys Lifecycle Surveys Sentiment Analysis  Sentiment analysis –Negative review Dissatisfaction –Positive review Arbitrary reasons (e.g. reinstall)

Extracting Churn Reasons from Surveys Topic detection –Churn reasons Insights might be misleading Uninstall Surveys Lifecycle Surveys Sentiment Analysis  Topic Detection Reasons

Churn Understanding Why do users have issues? Who is likely to churn?

Matching Profiles with Reasons Compare users –With churn reasons –Loyal Find patterns –Characteristics –Behavior –Context Uninstall Surveys Lifecycle Surveys Sentiment Analysis  Topic Detection Reasons User Profile Match

How Avira Identified Churnable Users Uninstalled surveys revealed an “update” issue as a churn reason –“The product could not update so I uninstalled.” User profile of users with the “update” problem –Context A particular version of the antivirus –Behavior Antivirus didn’t update for at least 2 weeks Users were active at least 4 times in 2 weeks

Churn Treatment & Prevention How can we react to prevent this?

How can we help? Find solutions for each churn reason Directly –Fix bugs –Fix UX –Add requested features –Offer the right price for extra features Indirectly –Head them to support team

To Summarize... Know your data Diagnose users who leave Find and understand reasons Treat every reason to prevent churn

Acknowledgements Many thanks to our colleagues who worked with us on this project or helped us with the presentation Rodica Coderie Data Scientist Viacheslav Rodionov Big Data Engineer Anna Tyrkich Designer