Growing a community is in many ways similar to growing an app. My talk will cover how we've approach Growth at Pinterest to grow to millions of users.

1. Data Driven Growth
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John Egan
Engineering Manager - Engagement Team @ Pinterest

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• Retail shopping rewards app
• Led Growth Engineering team
• Grew from 1MM to 8MM users in
3 years
• Acquired for $200MM
• Visual discovery & bookmarking
app
• Eng manager for Engagement
• Over 100 million MAUs
• Engagement experiments have
added millions of WAUs

3. Pre Product/Market Fit

4. Focus on
Product First
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5. Initial Traction

6. Do Things That
Dont Scale
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7. Growth Stage

8. Prioritize Based
On ROI
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New Users
MAUs
Dormant Users

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New Users
MAUs
Dormant Users

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New Users
MAUs
Dormant Users

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New Users
Dormant Users
Core
Casual
Marginal

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New Users
MAUs
Dormant Users

14. MAU Growth Accounting
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15. Acquisition
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16. Funnels
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2013

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2014

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2015

20. Activation
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21. 1d7s
• Percentage of new signups that use the app 1 or more times in the week
following signup
• Quicker to run experiments with
• At Pinterest this is highly correlated with user’s long-term retention
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24. Engagement
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25. One Key Engagement Metric
Medium: Total Time Spent Reading (TTR)
Twitter: MAUs with 7+ visits a month (7d28s)
Uber: Weekly Trips
Facebook: WAUs with 6+ visits a week (6L7s)
Pinterest: Weekly Active Repiners or Clickers (WARCs)
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26. Finding Your Key Engagement Metric
1) What are the actions a user has to take to get value from your product?
2) What is the frequency someone need to use your product
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27. User States
Core: Active multiple times a week
Casual: Active once or twice a week
Marginal: Active a couple times a month
New: Joined in the past 28 days
Dormant: Not active for 28 days
Resurrected: Was dormant, but became active
again in the past 28 days
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28. Badging
Holdout
Experiment
2:50 PM 100%
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29. Measuring Effect on Engagement
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30. Retention &
Resurrection
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31. Marketing
Pros:
• Coverage. Can reach the entire user base
Cons:
• Not personalized, lower open rates
• User’s have much lower tolerance
compared to emails
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32. Transactional
Pros:
• More personal than marketing
notifications
Cons:
• Users need activity to generate
notifications
• Need mechanisms to rate limit
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33. Recommendations
Pros:
• Coverage. Can reach most users
• Personalized to the user
Cons:
• Expensive and time consuming to build
out recommendation engine
• Quality may not be good if user has low
amount of activity
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2:50 PM 100%2:50 PM 100%

34. Measuring Engagement for Emails/Push
• Positive measures of quality
- Must lift key engagement metric
- Minimum required CTR rates
• Negative measures of quality
- App deletions
- Spam reports
- Unsubscribes
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35. Experiment Segmentation
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36. All Users
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37. Marginal Users (~1 month)
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38. Core Users (multiple times a week)
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39. Wrap Up
• Pre Product/Market Fit: Product comes first
• Initial Traction: Do things that don’t scale
• Growth Stage: Prioritize projects based on return on investment
• Use funnels for analyzing acquisition flows
• 1d7s to analyze on activation
• Use user states to understand how engaged users are
• Make sure emails/notifications for true engagement
• Use segmentation for experiments & metrics in general
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40. John Egan
Growth Blog: jwegan.com
Email: me@jwegan.com
Twitter: @jwegan_com
Pinterest: pinterest.com/jwegan
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