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Data-Driven Product Design

E37807353c2df74f78a25a267f17dccc?s=47 Keen
October 26, 2015

Data-Driven Product Design

Keen IO data engineer, Maggie Jan discusses key strategies and tactics for data-driven product design.

Industry Product Conference, 2015

E37807353c2df74f78a25a267f17dccc?s=128

Keen

October 26, 2015
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Transcript

  1. Data-Driven Product Design Maggie Jan | Data Scientist @Keen_IO

  2. •Intro to Data •Build, Measure, Learn •Product design & how

    data can help •Applying analytics to your business •How to build effective analytics AGENDA
  3. Platform & API for ANALYTICS keen.io

  4. Big Data and Analytics are kind of a thing right

    now.
  5. Johannes Kepler Tycho Brahe

  6. None
  7. None
  8. None
  9. Analytics can help.

  10. What: Measurement of movement towards your business goals. Purpose: To

    iterate to product and market fit before you run out of resources Analytics: In a Nutshell
  11. Understandable If you’re busy explaining data, you’re not busy acting

    on it ex: rides requested What is a good metric? Comparative Maintains context. ex: rides/day Meaningful Centers around your core business goals. ex: revenue
  12. A good metric is behavior changing.

  13. DATA & PRODUCT DESIGN COMPANY Juke Box Company INDUSTRY Internet

    of Things
  14. Good products provide customers with value Premium products provide high

    & dependable value Iterations build Relationships Measured by: Return visits, Retention, Engagement
  15. APPLYING ANALYTICS TO YOUR BUSINESS

  16. • Account creations • Deploys • Purchases • App Launches

    • Donations • Posts • Shares/Tweets/Likes A COMMON GOAL: ENGAGEMENT
  17. COLLECT EVENT DATA

  18. ANALYTICS DB CARS, TVs, ETC. WEBSITES WEBSITES CUSTOMERS DASHBOARDS MOBILE

    APPS queries queries queries events events events VISUALIZE DATA
  19. CASE STUDIES PUBLISHING Goshen College

  20. Common Mistakes to Avoid in Data-Driven Decision Making

  21. Confirmation Bias Leading the Witness Correlation vs Causation Common Pitfalls

  22. None
  23. Be disciplined in how you capture and analyze your data

  24. Gut instinct = Hypothesis Design a Test/Make Changes in Production

    Measure the Results Did we achieve goals? Controlled Experiments try again
  25. CONTROLLED TESTS CASE A/B Testing

  26. Exploratory Tries to find unexpected insights Source of competitive advantage

    via insights no one knew Reporting Keeps you abreast of day-to-day operations Predictable and repeatable Data & Product Design
  27. Maggie Jan @jandwiches Keen IO @keen_io