Using data to guide product development
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Transcript of Using data to guide product development
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Using data to guide product development
Mat Clayton Co-Founder
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What is Mixcloud?
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The Plan
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The Plan
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What does failure look like?
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turntable.fm - MAU
Text
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turntable.fm - DAU
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Competitor analysis
• Sitemaps are an amazing resource
• Google - “site:competitor.com“
• OpenSiteExplorer, can be used to find links and any growth tactics being used
• graph.facebook.com/<app_id> can give you monthly Facebook actives for FB connected apps
• API’s
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Key Performance Indicators (KPI’s)
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Pick a single KPI
• Easily understood
• Provides focus and align the team
• Represent the core of the product, and usually isn’t uniques
Mixcloud - Listener minutes / 28 daysAirBnb - Nights stayed / 28 daysEventbrite - Tickets Booked / 28 daysSendGrid - Mail Sent / 28 days
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Dashboards
• Distribute information amongst the team
• Make trends observable
• Give the team a solid understanding in top level metrics
• We’re big fans of graphite and grafana
• Alert on changes
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User Accounting
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User accountingSite A Site B
Active Users 1000 1000
Born +800 +100
Die -100 0
Sleep -300 -50
Awaken +100 +450
Next Period’s Active Users 1500 1500
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Retention Curves
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Identifying key product features for retention
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The Brainstorm
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What correlates with retention?
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First week Listens
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Key User Metrics - Twitter
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Twitter’s data
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Correlation does not imply Causation
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Cause or effect?
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How do you choose what to build?
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What facts do we know about our users?• What information did they give us? name, email, Facebook?
• Do they use mobile web/desktop/iOS/Android?
• Do they have email notifications enabled?
• Which core features have they used and to what degree?
• Which features did they miss?
• What was their last interaction with the service?
• Where did they come from? (Referrer)
• Do they use competitor products?
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Evaluating potential product features
• Do we have the resources to build it?
• How many users will the feature be relevant to?
• How frequently will they interact with it?
• What is the conversion rate against our primary goal?
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AB Testing (Always Be Testing)
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AB Testing Examples
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Big changes == Big results
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How big is big?
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AB testing• Continually run AATests
• Requirements
- Fast to implement, a single line of code
- Negligible cost to product performance, including SEO
- Results need to be available to the entire team
• Tools
- Django Experiments (we wrote it)
- A/Bingo (Ruby on Rails)
- Optimizely
- Google Website Optimizer
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Failure is acceptable
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“A good plan, violently executed now, is better than a perfect plan next week.” - George S. Patton