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Challenges of Predicting User Engagement


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These are presentation slides from Data Science Pop Up LA by Zahra Ferdowsi Data Scientist, Snapchat.

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Challenges of Predicting User Engagement

  1. 1. Challenges of Predicting User Engagement Zahra Ferdowsi Data Scientist @ Snapchat
  2. 2. User Engagement Who: New and/or existing users? What time period? When do you want to know? What engagement metric? How utilize the outputs? What: Churned and/or super engaged?
  3. 3. Who? • New users • Optimizing the registration and onboarding process • What activities in the first hours/days help users the most to retain/ cause churn? • How many friends? • Existing users • Change in behavior -> what direction they are going • What is the effect of certain experiences -> To optimize those processes • Both could be focused on churn and/or super engaged but usually they have different set of features and outputs
  4. 4. • You know a lot about the users • Desktop/mobile, time of the day, Android/iOS, PC/Mac, OS version, navigation/messaging speed, high/low penetration market, permissions • Need to know user segmentation/personas • High intent to buy/window shopper, Creators/consumers, adopting fast, level of engagement More on Who?
  5. 5. • There is always trade off between accuracy and knowing ASAP • Overcome the false positives by applying a solution that does not have a high negative effect on the false positives • Annoying engaged users with push notification? • Using different solutions depend on user persona/tenure/focus • Time is critical for churn users When?
  6. 6. • Short-term / long-term metrics • Long-term metrics are harder to predict • What if you have a sporadic purchase behavior? • Avg purchase once in a quarter: it would be harder to predict in the next week • Keep an eye on the seasonality trends • if a segment of users are coming only on the weekends, then better to look at the metric over a week What Time Frame?
  7. 7. • Which one is more important? • Reducing churn • Increasing engagement • It helps you to define the metrics • It does not necessarily mean different models What?
  8. 8. • What metric you are looking at? • Conversion, Time spend, create content, active number of days a week • Do we have to look at one metric for all the users? • Users with 10 friends vs. 1 friend in the first week? • Users in 3 zone: • Red zone: High probability to churn • Yellow zone: Low engagement • Green zone: High engagement Another What?
  9. 9. • Have an engagement score • Daily run to know the engagement score for each user • What is the segment that has the most change in last x days/months? • Use engagement to predict other metrics such as customer value • Optimizing the onboarding process: What activity to suggest users to do based on the stage they are in (Red / Yellow / Green Zone) • Optimizing push notification • A/B test or not A/B test: testing on personas How?
  10. 10. zahraferdowsi