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Decision Making with Data by PayPal Sr Product Manager

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Decision Making with Data by PayPal Sr Product Manager

  1. 1. Decision Making with Data by PayPal Sr Product Manager
  2. 2. FREE INVITE Join 35,000+ Product Managers on
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  9. 9. COURSES Corporate Training Level up your team’s product management skills
  10. 10. Deb Dutta S P E A K E R
  11. 11. DEB DUTTA SENIOR PRODUCT MANAGER PAYPAL Decision Making with Data
  12. 12. 12 About me What to Product Managers do? Data in Product Management PM Cheat Sheet Agenda
  13. 13. 13 About me Sr Product Manager at PayPal ML Platform for Personalization, Marketing & Risk Masters, Computer Science from Carnegie Mellon University Fmr Group Product Manager for Global IoT Smart City solutions at Hitachi Data Systems Travel & Lifestyle Blogger & Influencer Instagram - @deesignplay (170K+) Live with 2 rescued pups + 1 rescued cat
  14. 14. 14 EMPOWER your team so you’re not the only Decision Maker
  15. 15. What do Product Managers do? Sense - Customer Discovery - Market Study Seize - Targeting Addressable Market - Pricing and Positioning Build - Build Minimum Viable Product - Measurable Goals & Milestones Measure - Validate with customer feedback - Growth and profit Learn - Incorporate and iterate - Grow to profitability & maturity
  16. 16. Innovation is a continuous learning process • Experiment ation and Learning • Customer Empathy • Idea Generation • Combining & Refining ideas • Insight Generation Sensing Seizing SeizingSensing Transform Abstract Conceptualization Synthesis (How?) Concrete Experience Analysis (Why?)
  17. 17. 18 Data in Product Management “ ”
  18. 18. 19 USER BEHAVIOR • What channels are driving best quality of users? • How are different segments behaving? RETENTION • Average lifecycle of a user • How many users churn out of the system? USER ENGAGEMENT • How many users return daily/weekly/monthly • How are users engaging with the product? CONVERSION • Where are the users getting stuck in the funnel? • What % of users are completing a task? FINANCIAL ROI • How successful was your marketing campaign? • How much $$$ did a feature drive? Metrics that Matter
  19. 19. Funnel Analysis GOAL: The funnel analysis essentially determines stages of churn Eg: Sign up, Email verification, Checkout success Simple steps to evaluate Signup Experience: • Clearly indicates the user drop off at each stage • Insights on which users are stuck at what stage • Effective way to compute completion Begin Signup Email Verification Add Bank or Card Activated Account
  20. 20. Funnel Analysis
  21. 21. Funnel Analysis
  22. 22. Funnel Analysis
  23. 23. Cohort Analysis GOAL: Analyze a subset of users with common characteristics over time. Instead of evaluating all the users at once, study behavioral analytics of smaller data sets and identify related segments for analysis Why study Cohorts? • Clearly detect patterns across the user lifecycle • Allow products and features to be designed with the target cohort or segment in mind
  24. 24. Cohort Analysis
  25. 25. Segmentation Analysis GOAL: Further slice a cohort based on common criterion such as age, demographics, device, OS etc Why study Segments? • Look for common characteristics such as shared needs, common actions, usage patterns by demographic • Clearly identify segments that need attention – high yield or segments that are not converting • Allow products design, strategy, branding to be designed with the target segment in mind
  26. 26. Segment Analysis
  27. 27. Avg Page Load Time Analysis
  28. 28. Segmentation Analysis
  29. 29. Segmentation Analysis
  30. 30. Event Based Tracking GOAL: Monitor each user interaction with screen content through instrumentation Why study Event Based Tracking? • Getting into the head of the customer • The more granular the instrumentation, the more fine grained your tracking will be to study user interaction from a web page or screen load • Surface UI and User experience concerns quickly • Tracking downloads, AJAX calls, image loads etc • Engineering team to incorporate from start
  31. 31. Click Through Analysis GOAL: Track user’s clicks, taps, links, button clicks for analysis Why study Click Through Analysis? • Typically used in Marketing campaigns and Search Engines for calculating Click Through Rate (CTR) and ROI per click • Can be used to draw deeper analytics to make Optimizations to product • Eg: Phone Confirmation experience needs user to fill Verification Code • Track % of uses click “Get Code”, “Not Now” • Track % of users that say “Resend code” • Tie it with Signup – to – Activation analysis
  32. 32. A/B Testing GOAL: Experiment before launch! Why A/B Testing? • Its how you can filter the great ideas from the not so great – let the users decide! • Let users determine what features go into the product • Save time on developing unwanted features • Never forget to launch anything without A/B testing for every single thing including backend products
  33. 33. 36 Who is my target Customer?1 What problem are you trying to solve?2 What data do I need to prove my hypothesis: Define your KPIs 3 How can you get started?
  34. 34. 37 PM Cheat Sheet
  35. 35. 38 Product Design Designing a Product: User: Who’s the primary customer/end user Problem: What is their biggest problem/goal Competition & Market Opportunity: What is the market offering today Gap: How can we make a difference KPIs: How can we measure our success
  36. 36. 39 Product Success How can you measure the success of your product? Goal: What is the goal of the product Depending on the goals, metrics vary in many ways. Some examples: • User Acquisition: current user base, growth, monthly active users, time spent on portal, analytics (demographics, geography, age etc), referring others • Conversion: Free to paid users, referring others • ROI: Customer acquisition cost, support costs, delivery costs, LTV of a customer, revenue growth rate
  37. 37. 40 Product Improvement How can you improve a product? Goal: What is the goal of the product Metrics: to measure success Problems: • Is the user base not growing? Can it be expanded by entering newer markets or verticals • Is revenue not growing? Can be done through increasing subscription costs, charge businesses more for targeted ads? • Do we need higher user engagement - monthly active users or Time spent • Increase clickthrough rate or Competition: What’s the competition doing better? Solution: can be crazy ideas, depends on risk level, budget, culture, strengths Roll out: How to implement it & validate solution
  38. 38. 41 Test Launch Success How can you test the success of your product launch? • Metrics - such as user growth, revenue growth, market growth etc • Usability & A/B testing - Roll the product and see how many people use it • Customer feedback - social networks, forums, press coverage etc • Support metrics - works seamlessly, not much support engagement • Google analytics - eg: tells you growth in traffic with a product roll out • Logging - check internal logs for instrumented counters to see how much a feature was used
  39. 39. Thank You End your presentation with a thank you slide. Leave it up while you answer questions. You want this slide to be plain so the attention is on you for question period and not the presentation. The frameworks in this template belong to their respective owners, none were created by this document owner. Frameworks are derived from various pieces of business literature.
  40. 40. Part-time Product Management, Coding, Data Analytics, Digital Marketing, UX Design and Product Leadership courses in San Francisco, Silicon Valley, New York, Santa Monica, Los Angeles, Austin, Boston, Boulder, Chicago, Denver, Orange County, Seattle, Bellevue, Washington DC, Toronto, London and Online