Advanced growth techniques from Pinterest’s growth expert - John Egan
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Data Driven Growth 1 John Egan Engineering Manager - Engagement Team @ Pinterest
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Transcript of Advanced growth techniques from Pinterest’s growth expert - John Egan
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
Pre Product/Market Fit
Focus on Product First
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Initial Traction
Do Things That Dont Scale
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Growth Stage
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
MAU Growth Accounting
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Acquisition
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Funnels
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2013
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2014
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2015
Activation
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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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Engagement
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One Key Engagement MetricMedium: 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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Finding Your Key Engagement Metric1) 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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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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Badging Holdout Experiment
2:50 PM 100%
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Measuring Effect on Engagement
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Retention & Resurrection
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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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Transactional
Pros: • More personal than marketing
notifications
Cons: • Users need activity to generate
notifications • Need mechanisms to rate limit
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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%
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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Experiment Segmentation
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All Users
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Marginal Users (~1 month)
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Core Users (multiple times a week)
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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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John EganGrowth Blog: jwegan.com
Email: [email protected]
Twitter: @jwegan_com
Pinterest: pinterest.com/jwegan
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