This presentation was given to startup founders and software people to help them understand how to better measure the success (or failure) of their product by using objective data.

1. How to Measure Your Product
Data Driven Little Bets
Liron Hayun [ UX & Analytics Consultant ] | lironh@gmail.com

2. ANALYTICS UX

6. ANALYTICS
DATABASE
SURVEYS
EMAIL
SERVICE

7. ANALYTICS
DATABASE
SURVEYS
EMAIL
SERVICE

8. 5 Steps to Epiphany
1) Identify business objectives
2) Translate to technical requirements
3) Implement
4) Measure & Learn
5) Maintain

9. Business Objectives
Step 1

10. The 5 Common Business Objectives
★ Ecommerce - sell products/services
★ Lead Generation - collect user info & connect
★ Content - engagement & visits
★ Online Support - finding info at the right time
★ Branding - awareness, engagement & loyalty

11. Practical Guidelines
● Include macro and micro conversions
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● Distill customer-problem-solution hypothesis
● Find the riskiest assumptions

12. Technical Requirements
Step 2

13. How It Works
Users Sessions Interactions

14. Your Product Analytics Server

15. Session
User
Interaction

16. Session

17. Data Types
● Dimensions - characteristics of your users, their
sessions and actions (e.g. country, traffic source).
● Metrics - the quantitative measurements of
users, sessions and actions.

18. Key Metrics
● Pageviews / Screens
● Events
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● Users
● Sessions
● Time on Page
● Bounce Rate

19. Practical Guidelines
● Use a consistent syntax
○ upper/lower case letters
○ name of event actions
○ use of “-”
● Collect campaign data

20. Implementation
Step 3

21. Code Configuration

22. Practical Guidelines
● Build an infrastructure
○ maintain data integrity
○ easily measure new features
○ keep consistent syntax
● Setup goals in your analytics tool (!)

23. Measure & Learn
Step 4

24. AARRR!
1) Acquisition - users come from various channels
2) Activation - users enjoy first visit
3) Retention - users come back
4) Referral - users like product and refer others
5) Revenue - users conduct monetization behavior

25. Analysis Techniques
● Segmentation - isolate and analyze data subsets
to understand behavior (by location, source).
● Context - use benchmarks to understand if your
performance is good or bad (internal / external).
● Exploration - browse your data to find your next
questions (landing/exit pages, bounce rates).

26. Acquisition

27. Sources Quality

28. Activation

29. Retention

30. Retention

31. Referral

32. Revenue

33. Maintain & Refine
Step 5

35. Recommended Tools
➔ Google Analytics - free, robust analytics tool
➔ Optimizely - easy A/B testing
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➔ Google Forms - free surveys, easily embedded
➔ Qualaroo - onsite “nudges”

36. Thank You
Liron Hayun [ UX & Analytics Consultant ] | lironh@gmail.com