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How to Use Data for Product Decisions by YouTube Product Manager

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With millions of new data points every moment, how are Product Managers expected to make sense of it all? This talk outlined the steps required to distill and synthesize data to drive actionable product decisions. The most effective Product Managers are those who know their data: they can justify product priority and roadmap changes, calibrate resource asks and manage their own time more effectively. This lecture equipped the audience with the tools necessary to draw insight from unstructured data using Google’s cloud analytics suite.

How to Use Data for Product Decisions by YouTube Product Manager

  1. 1. How to Use Data for Product Decisions by YouTube Product Manager www.productschool.com
  2. 2. FREE INVITE Join 23,000+ Product Managers on
  3. 3. COURSES Product Management Learn the skills you need to land a product manager job
  4. 4. COURSES Coding for Managers Build a website and gain the technical knowledge to lead software engineers
  5. 5. COURSES Data Analytics for Managers Learn the skills to understand web analytics, SQL and machine learning concepts
  6. 6. COURSES Blockchain for Managers Learn how to trade cryptocurrencies and build products using the blockchain
  7. 7. Joao Fiadeiro TONIGHT’S SPEAKER
  8. 8. Data and Analytics for Product Managers Joao Fiadeiro, PM @ Google June 2018
  9. 9. A little about me... ● Product Manager at Google, where I work in the YouTube Music team ○ Involved in social/community efforts for artists and analytics ● I began my career as a data scientist 5 years ago ● Proud generalist!
  10. 10. Today’s content Part I: Analytics Concepts for PMs ● The key concepts within analytics Part II: Implementing Analytics ● From vision to metrics Part III: Experimentation ● Data-driven product roadmaps Part IV: Reporting ● Let your analysis be heard High-level overview of the role of analytics in a Product Manager’s day- to-day… ...Followed by a hands- on demo of some useful tools
  11. 11. Part I: Analytics Concepts for PMs
  12. 12. Deriving value from data: key concepts Data Points Segmentation Funnels Cohorts Analytics Concepts Implementing Analytics Experimentation Reporting
  13. 13. Data Points Data points are the individual points of collected data that are measurements of particular items within the platform ● Data usually exists somewhere; though it may be hard to find ○ Is the right data being collected? ○ Do you have access to it? ● As a PM, your job is to know what data is being collected and where it lives ● If you don’t have measurements, you don’t have anything… Get creative. Analytics Concepts Implementing Analytics Experimentation Reporting
  14. 14. Segmentation Segmentation is about grouping together people by a common characteristic and seeing what the usage patterns of the product are as a group ● Segments must be measurable; typical segments are behavioral, technical or demographic ● Segmentation slices the analytics, allowing underlying patterns in behaviour and usage to be observed, rather than be hidden by noise and averaging Analytics Concepts Implementing Analytics Experimentation Reporting
  15. 15. Gangnam Style: A study in Attention Span Analytics Concepts Implementing Analytics Experimentation Reporting Watch Time (%) by Gender and Age Avg. % Completion by Age
  16. 16. Funnels A funnel is made up of the measurement of the key event at each step of the flow or user journey ● Users don’t just do something in isolation. Instead, they perform a series of actions to accomplish a task or goal. These flows or user journeys can be measured using funnels ● Optimize for improving every step of the funnel. A solid conceptual funnel allows for rich metrics between every layer Analytics Concepts Implementing Analytics Experimentation Reporting
  17. 17. Funnels at YouTube Each layer expressed a ratio of the previous layer… is worth more than a thousand words. Find the golden path! Analytics Concepts Implementing Analytics Experimentation Reporting
  18. 18. Cohorts The primary purpose of cohort analysis is for comparative analysis to answer the question of how users’ behaviour changes over time ● Segment users into buckets and explore differences in behavior. ○ For example: how does the behaviour of users who registered a week ago differ from that of users who registered a month ago? ● Used to understand retention and churn Analytics Concepts Implementing Analytics Experimentation Reporting
  19. 19. Cohorts Analytics Concepts Implementing Analytics Experimentation Reporting
  20. 20. Part II: Implementing Analytics in the Real World
  21. 21. Implementing Analytics Product Managers must establish a clear product vision but should also be able to articulate key performance indicators The process of planning consists of these steps: 1. Define the product vision 2. Define the KPIs that meet the product vision 3. Define the metrics that allow you to hit your KPIs 4. Define the funnels (via user journeys) that affect your metrics Analytics Concepts Implementing Analytics Experimentation Reporting
  22. 22. The Vision ● By starting with the vision, you ensure that what you measure will help you achieve the product vision ● Avoid the trap of vanity metrics by tying everything that is measured to what you are trying to achieve ● It is the filter that allows you to ignore the potential mass of data you can collect Analytics Concepts Implementing Analytics Experimentation Reporting
  23. 23. Key Performance Indicators (KPIs) ● KPIs are derived from the product vision and tell you how well your product is meeting the vision. ○ They are product focused and only indicate the performance of the product. ● KPIs are used to set targets for the performance of the product ● The KPIs need to reflect the current stage your product is at Analytics Concepts Implementing Analytics Experimentation Reporting
  24. 24. Metrics: what should you be measuring? Engagement vs Transactional Apps Analytics Concepts Implementing Analytics Experimentation Reporting
  25. 25. Growth Transactions MonetizationRetention The four buckets of metrics... Analytics Concepts Implementing Analytics Experimentation Reporting
  26. 26. Growth: Gaining new users How many new users do you have and where do they come from? ● # of daily/weekly/monthly new user signups ● Metrics by acquisition channel Analytics Concepts Implementing Analytics Experimentation Reporting Growth ● DAU/WAU/MAU ● Trial Users/Paying subscribers
  27. 27. Transactions & Engagement: Increasing usage of the app How much are your users engaging with the product? ● Engagement: Typically consumption ● Transactions: Average Order Value Analytics Concepts Implementing Analytics Experimentation Reporting Transactions ● Views/Watch Time (per user) ● Likes/Shares/Comments
  28. 28. Retention: Ensuring that existing users come back Are your users coming back for more? ● How many of your users are coming back within 24 hrs, 7 days and 28 days? Analytics Concepts Implementing Analytics Experimentation Reporting ● Avg. Session length ● DAU/MAU Retention
  29. 29. Monetization: Converting usage into dollars How effectively are you converting usage into revenue? ● Ad-supported or Subscription m10n ● Revenue share/margins Analytics Concepts Implementing Analytics Experimentation Reporting ● Revenue per Watch Hr (RPH) ● Avg Revenue Per User (ARPU) Monetization
  30. 30. A word of caution... Most of these metrics, on their own, might make you feel good, but they don’t offer clear guidance for what to do… Every new feature that is being considered should move the needle on the key metrics in A/B tests otherwise the feature may be of questionable value Analytics Concepts Implementing Analytics Experimentation Reporting
  31. 31. Experiments (aka. A/B Tests) A/B experiments produce the most actionable of all metrics, because they explicitly refute or confirm a specific hypothesis Analytics → “What is going on with x?” Experiments → “How do we improve x?” Experimentation informs: Planning of (new) experiments Product backlog Development prioritisation Analytics Concepts Implementing Analytics Experimentation Reporting
  32. 32. Experimentation Cycle Plan Monitor Implement Analytics Concepts Implementing Analytics Experimentation Reporting Start with a question and formulate a hypothesis. Identify (in)dependent variables Implement in code (or tool) and be patient Do not change the control! Segment, segment, segment Ensure statistical validity
  33. 33. Closing the loop Ask yourself these two questions: ● What do these results mean for development prioritisation? ● Why did I get these results? The result whether positive or negative is immaterial, what is material is that you learnt something from the test Keep experimenting - ensure it’s in the product & engineering culture Analytics Concepts Implementing Analytics Experimentation Reporting
  34. 34. Part IV: Reporting
  35. 35. Reporting: The danger zone ● Don’t get caught up in the data/analytics! If you don’t report your findings, you’re wasting your time ● Beautiful visualizations are great, but less is more ● Some tips: ○ Do it in a manner that is easily grasped by everyone in the company ○ Allow for as much input/interaction from the audience as possible Analytics Concepts Implementing Analytics Experimentation Reporting
  36. 36. Reporting: Managing stakeholders ● When presenting to senior stakeholders, you’ll likely get many “what if?” follow up questions ● Advice: come prepared with multiple scenarios. If possible, complement with a spreadsheet that allows custom inputs Projected MAU Watch Hours per MAU RPH Total Revenue Probability Expected Revenue Best Case 300M 20 $0.020 $120M 10% $12M Expected Case 280M 15 $0.015 $63M 70% $44M Worst Case 200M 12 $0.012 $29M 20% $6M TOTAL $62M Analytics Concepts Implementing Analytics Experimentation Reporting
  37. 37. Reporting: What about dashboards? ● Dashboards can be very useful, but also very dangerous to PMs ● Pros: ○ Audience can self-serve data needs ○ PM does not waste time with data requests ○ Can be widely published and shared ● Cons: ○ Audience can make incorrect assumptions about data ○ PM may have to maintain them → Huge time sink ○ Orphaned data is a real problem Analytics Concepts Implementing Analytics Experimentation Reporting
  38. 38. Tying it all together: Google Big Data
  39. 39. Google Big Data Solutions Google offers an integrated, serverless Big Data platform for data-driven applications
  40. 40. Today we’ll look at three products A Product Manager can use these products to analyze, explore, and present data in an effective way: 1. BigQuery: fully managed, low cost analytics data warehouse. Use SQL to query massive datasets. 2. Dataprep: data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis 3. Data Studio: turns data into dashboards and reports that are easy to read, share, and customize.
  41. 41. BigQuery Demo Goals: ● Pick a public dataset ● Explore schema ● Lightweight querying Codelab Link
  42. 42. Dataprep Demo Goals: ● Import a CSV file ● Create a ‘Flow’ then a recipe ● Explore data transformations ● Export dataset Link
  43. 43. Data Studio Demo Goals: ● Import data source ● Create a few charts ● Make and customize a table ● Add filters ● Publish Link
  44. 44. Part-time Product Management Courses in San Francisco, Silicon Valley, Los Angeles, New York, Austin, Boston, Seattle, Chicago, Denver, London, Toronto www.productschool.com
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With millions of new data points every moment, how are Product Managers expected to make sense of it all? This talk outlined the steps required to distill and synthesize data to drive actionable product decisions. The most effective Product Managers are those who know their data: they can justify product priority and roadmap changes, calibrate resource asks and manage their own time more effectively. This lecture equipped the audience with the tools necessary to draw insight from unstructured data using Google’s cloud analytics suite.

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