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A Perspective from the intersection Data Science, Mobility, and Mobile Devices
 

A Perspective from the intersection Data Science, Mobility, and Mobile Devices

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Invited talk at Stanford CSEE392I (Seminar on Trends in Computing and Communications) April 24, 2014. ...

Invited talk at Stanford CSEE392I (Seminar on Trends in Computing and Communications) April 24, 2014.

Covered three topics: (1) Data science at LinkedIn. (2) Mobile data science — how is it different, challenges and opportunities. Examples of how data science impacts business and product decisions. (3) Mobile today, and LinkedIn's mobile story.

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    A Perspective from the intersection Data Science, Mobility, and Mobile Devices A Perspective from the intersection Data Science, Mobility, and Mobile Devices Presentation Transcript

    • A Perspective from the Intersection of Data Science, Mobility, and Mobile Devices Stanford, EE392I April 24, 2014 Yael Garten Manager, Data Science for Mobile Products @ LinkedIn
    • Let’s talk today about: 2   1.  Data science at LinkedIn: driving product and business insights 2.  Mobile data science:What’s different, what’s important? 3.  Mobile today. LinkedIn’s mobile story.
    • Our mission: Connect the world’s professionals to make them more productive and successful 3   www.linkedin.com
    • Whatever you do, wherever you are 4   Connecting
    • LinkedIn at a Glance 5   •  Founded in 2003 •  300M+ members •  2 new signups per second •  Executives from all Fortune 500 companies •  >4B annual people searches •  Over 200 countries & territories •  >20 different languages
    • Your Professional Identity Amazing dataset that we can slice and dice. By seniority, by job function – we can ask many interesting questions.
    • So what data do we have? Content Social Graph Behavior, Engagement Interest and Intent: •  Queries •  PageViews (different types) •  Actions  (invites sent, articles clicked) Mechanism •  Device •  Channel
    • What does the Data Science team at LinkedIn do with the data? 8   •  Product and Business Insights Measure, understand and improve products Drive strategy •  Build Data Products •  Extract Insights we Share Externally
    • Knowledge: What’s going on?
    • If you can’t measure it, you can’t fix it. Measure everything. wisely.  10   Know thyself:What’s going on? In the form of reporting, knowing the numbers, understanding usage of products, data patterns, segments of users, tracking growth and health of the ecosystem. What  are  the  right  success  metrics  that  we  can  define?   What to measure?
    • Insights: what should we build or improve?
    • What to build? Rethinking our Mobile App 12   What do people on this page? Where does the opportunity lie? What drives them in? Where do they go next? Where can we reduce friction? How many drop off? What is the stickiest product? What works, what doesn’t?
    • Data to inform product design of the iPad app: iPad 12am 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 12pm 1pm 2pm 3pm 5pm 6pm 7pm 9pm 10pm 11pm Desktop At what times in the day are people using different devices?  
    • What to ship? Controlled online experiments (aka A/B testing) 14   1.  What are the right success metrics? 2.  How do we track (instrument) this? Data quality 3.  Experimental Design 4.  Analysis, Interpretation, Recommendation
    • Wisdom: What’s the next needle mover? dataà knowledge à insights à wisdom
    • Strategic Analyses:  Using data to drive the business via rigorous analytical framework •  What is the value of an action that a user takes on the site? •  What early behavior on the site is predictive of future engagement? •  How does mobile usage compare to and impact desktop site engagement? •  What is the value of a (mobile) user? •  How do people use LinkedIn organically? Drivers and flows
    • What does the Data Science team at LinkedIn do with the data? 17   •  Product and Business Insights •  Build Data Products •  Extract Insights we Share Externally
    • Data Products on your LinkedIn homepage 18  
    • Alumni Data Product 19  
    • The Skills Data Product 20  
    • How do we do it? 21   Extract
    • Skills:Assigning Skills to People 22  
    • What does the Data Science team at LinkedIn do with the data? 27   •  Product and Business Insights •  Build Data Products •  Extract Insights we Share Externally
    • Data Stories: Growing and Shrinking Industries
    • Where did all the people go from the collapsed financial institutions in 2008?
    • The 10 Most attractive start ups to Bay Area Engineers
    • Big Data that scales. What Technologies do we use? 31  
    • Let’s talk today about: 32   1.  Data science at LinkedIn: driving product and business insights 2.  Mobile data science: What’s different, what’s important? 3.  Mobile today. LinkedIn’s mobile story.
    • Connecting: Whatever you do, wherever you are
    • Ubiquitous Computing Constant access to information and products
    • Price Point Screen Size Connectivity App or Web Operating System Location App Version
    • 1.  Recognize the complexity 2.  Invest in tracking 3.  Leverage the data Control the chaos… Delight users
    • Price Point Screen Size Connectivity App or Web Operating System Location App Version Screen Size PhoneTablet Phablet
    • Price Point Screen Size Connectivity App or Web Operating System Location App Version App Version 1.0 2.0
    • App Version Price Point Screen Size Connectivity Operating System Location App or Web App or Web •  Can A/B test (+) •  Limited A/B capabilities (-) •  Transactional •  Organic, richer experience
    • App or Web App Version Price Point Screen Size Connectivity Location Operating System Operating System 3X more reach
    • Operating System App or Web App Version Price Point Screen Size Connectivity LocationLocation
    • 1.  Recognize the complexity 2.  Invest in tracking 3. Leverage the data Control the chaos… Delight users.
    • Data to inform product design of our iPad app: iPad 12am 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 12pm 1pm 2pm 3pm 5pm 6pm 7pm 9pm 10pm 11pm Desktop From coffee to couch and everything in between  
    • Follow the trends
    • Landscape or Portrait ? 45% 65% 55% 35%iPad iPad mini
    • 50% and growing. Optimize your email 2X higher CTR
    • The data says: Simplify 1.0 2.0 6%   8%   10%   12%   14%   16%   18%   20%   1/11   2/11   3/11   4/11   5/11   6/11   7/11   8/11   9/11   10/11   11/11   12/11   1/12   2/12   Mobile % of Unique Visiting Members 1.0 2.0
    • Leverage learnings across platforms
    • Let’s talk today about: 53   1.  Data science at LinkedIn: driving product and business insights 2.  Mobile data science:What’s different, what’s important? 3.  Mobile today. LinkedIn’s mobile story.
    • time % of traffic / revenue mobile moment
    • Q1 2011
    • Q3-2013
    • Q1-2014
    • This year
    • 25% 44% Singapore Netherlands Turkey>50% >50% >50% >45% >45% United Arab Emirates >45% Sweden Australia >45% Denmark 38% 32% UK US Brazil India End of 2013
    • Always on with mobile 3am 6am 9am 12pm 3pm 6pm 9pm
    • Sources: IDC March 2012, Gartner April 2013, 2012 2016 Smart phone PC Tablet Per day… = 100M Every day there are 4.5x new mobile devices sold than babies born! 378K 1.3M 371K Mobile is changing the world
    • Mobilize systems. Different architecture. New considerations. Feature parity? Same designs? Resourcing. Mobilize. Mobile First. Promotions" Tracking" Internationalization" Deep Linking" Analytics" Automation" Personalization" A/B" Experimentation" Page Load"
    • LinkedIn App So we have one great app. Segment use cases.
    • Contacts Recruiter Pulse LinkedIn App Multi-app. Cross promote. Slideshare
    • Mobile first. Platform first
    • Organic vs. Transactional
    • Opportunity. Essential. Transformational. Connecting.
    • Thank you. http://www.linkedin.com/in/yaelgarten @yaelgarten We’re hiring!