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

1. Challenges of Predicting
User Engagement
Zahra Ferdowsi
Data Scientist @ Snapchat

3. 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?

4. 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

5. • 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?

6. • 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?

7. • 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?

8. • Which one is more important?
• Reducing churn
• Increasing engagement
• It helps you to define the metrics
• It does not necessarily mean different models
What?

9. • 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?

10. • 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?

11. zahraferdowsi
zahra.ferdowsi@snapchat.com