This document summarizes key points from a lecture on analytics and optimization. It discusses why analytics are important to quantify success, enable scientific decisions, and avoid relying on opinions. Good analytics should be simple, relevant, unambiguous, and actionable. Website metrics that were covered include traffic sources, popular pages, funnels, and A/B testing. Optimization techniques like multivariate testing were also discussed. The lecture also touched on competitive intelligence, surveys, and analytics tools.

1. 10 — Analytics & Optimization
From Code to Product
gidgreen.com/course

2. Lecture 10
• Introduction
• Data collection
• Website metrics
• Optimization
• Competitive intelligence
• Surveys
• Tools and books
From Code to Product Lecture 10 — Analytics— Slide 2 gidgreen.com/course

3. Why analytics?
• Quantify success/failure
– For yourselves
– For investors
– Against competition
• Scientific decisions
– No blind faith
– Fewer arguments
– Avoid HiPPO = highest paid person’s opinion
From Code to Product Lecture 10 — Analytics— Slide 3 gidgreen.com/course

4. Good analytics
• Simple
• Few in number
• Relevant
• Unambiguous
• Actionable
• Instant (or nearly)
• Repeatable
From Code to Product Lecture 10 — Analytics— Slide 4 gidgreen.com/course

5. AARRR — Metrics for pirates
From Code to Product Lecture 10 — Analytics— Slide 5 gidgreen.com/course
Acquisition Site visit or app download
Activation Registration or usage
Retention Repeat usage
Referral Brings other people
Revenue Generate cash
DaveMcClure,500Startups

6. Some quotes
“What gets measured, gets managed.”
— Peter Drucker
“The only metrics that entrepreneurs
should invest energy in collecting are
those that help them make decisions.”
— Eric Ries, The Lean Startup
From Code to Product Lecture 10 — Analytics— Slide 6 gidgreen.com/course

7. Lecture 10
• Introduction
• Data collection
• Website metrics
• Optimization
• Competitive intelligence
• Surveys
• Tools and books
From Code to Product Lecture 10 — Analytics— Slide 7 gidgreen.com/course

8. In-app analytics
• Home rolled or third party
• Store usage information locally
– ‘Call home’ when online
• Privacy concerns
– Confirmation dialog?
• Complete access to device
– But you will be caught!
• Problem: slow iteration
From Code to Product Lecture 10 — Analytics— Slide 8 gidgreen.com/course

9. In-app integration
From Code to Product Lecture 10 — Analytics— Slide 9 gidgreen.com/course

10. Reporting app events
From Code to Product Lecture 10 — Analytics— Slide 10 gidgreen.com/course

11. Web analytics
• All activity visible to site
– Users don’t expect privacy
• Web servers log requests
– Also: Javascript solutions
• Page view centric
– Other events require integration
– Coffee break?
– Events not sessions
From Code to Product Lecture 10 — Analytics— Slide 11 gidgreen.com/course

12. A web server log line
www.websudoku.com
24.186.55.113
[06/May/2012:08:13:02 -0400]
"GET / HTTP/1.1”
200
1045
"http://www.google.com/search?q=sudoku”
"Mozilla/5.0 (iPhone; CPU iPhone OS 5_1
like Mac OS X) AppleWebKit/534.46 (KHTML,
like Gecko) Mobile/9B179 Safari/7534.48.3"
From Code to Product Lecture 10 — Analytics— Slide 12 gidgreen.com/course

13. Javascript tracking code
<script type="text/javascript”>
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-1165533-3']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type =
'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ?
'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0];
s.parentNode.insertBefore(ga, s);
})();
</script>
From Code to Product Lecture 10 — Analytics— Slide 13 gidgreen.com/course

14. Web metrics alternatives
From Code to Product Lecture 4 — UI Design— Slide 14 gidgreen.com/course
Server logs Javascript Home-made
Integration None Via HTML Server code
Convenience
Download +
analyze
Web-based
access
Up to you
Delay None Up to 24 hours Up to you
Reporting Varies Advanced Up to you
Other events Hard Via API Easy
Data leakage None Total! None

15. Track web users by…
• IP address
– Given for every web request
– Good for geography
– But: proxies, classrooms, router resets
• Cookies
– Track user browser over long term
– But: clearing, multi-browsing, first request
– Customization of web server
From Code to Product Lecture 10 — Analytics— Slide 15 gidgreen.com/course

16. Track web users by…
• Log in
– Reliable for registered users
– But: anonymous users, multiple accounts
– Requires custom logging tools
• Solution: combine!
– Intelligently tie IPs, cookies and accounts
– Example: user registration
• Data always incomplete
From Code to Product Lecture 10 — Analytics— Slide 16 gidgreen.com/course

17. Lecture 10
• Introduction
• Data collection
• Website metrics
• Optimization
• Competitive intelligence
• Surveys
• Tools and books
From Code to Product Lecture 10 — Analytics— Slide 17 gidgreen.com/course

18. Basic website metrics
From Code to Product Lecture 10 — Analytics— Slide 18 gidgreen.com/course

19. Immediate questions
• When does one visit end?
– GA: 30 minutes without activity
• What makes a visitor unique?
– GA: Tracking cookie
• How is duration calculated?
– GA: Time between first and last pages
• What makes a visitor new?
– GA: Never visited your site before
From Code to Product Lecture 10 — Analytics— Slide 19 gidgreen.com/course

20. Geography
From Code to Product Lecture 6 — BM — Advertising— Slide 20 gidgreen.com/course

21. Demographics
From Code to Product Lecture 6 — BM — Advertising— Slide 21 gidgreen.com/course

22. Frequency report
From Code to Product Lecture 10 — Analytics— Slide 22 gidgreen.com/course

23. Sources of traffic
• Type-in (no referrer)
– Includes browser bookmarks
• Search engines
– Navigational search = type-in
• Referrals
– Website links or social media
• Paid advertising
• Email campaigns
From Code to Product Lecture 10 — Analytics— Slide 23 gidgreen.com/course

24. The multitouch problem
• There’s history before the referrer
– Who deserves the credit, e.g. affiliates
• So who gets the credit?
– Last click (standard)
– First click (unrealistic)
– Even split
– Split weighted to last
From Code to Product Lecture 10 — Analytics— Slide 24 gidgreen.com/course

25. Search engine queries
From Code to Product Lecture 10 — Analytics— Slide 25 gidgreen.com/course
Also: internal site search

26. Popular pages
From Code to Product Lecture 10 — Analytics— Slide 26 gidgreen.com/course

27. Landing/entry pages
From Code to Product Lecture 10 — Analytics— Slide 27 gidgreen.com/course
“You can’t choose your home page” — A. Kaushik

28. Clickmaps and heatmaps
From Code to Product Lecture 10 — Analytics— Slide 28 gidgreen.com/course

29. Conversion funnel
From Code to Product Lecture 10 — Analytics— Slide 29 gidgreen.com/course
Source:www.searchenginejournal.com

30. Sampling methods
• Popular site => lots of data
– Burden to collect, slow to analyze
• Don’t record all events
– Choose important pages
– Random subset of visitors
– Random subset of pageviews
• Sub-sample when analyzing
– By page or visitor
From Code to Product Lecture 10 — Analytics— Slide 30 gidgreen.com/course

31. Staleness due to changes in…
• Content
• User familiarity
– Early adopters vs ...
• Search engine rankings
• Market needs
• Devices
• Cookies
From Code to Product Lecture 10 — Analytics— Slide 31 gidgreen.com/course

32. Lecture 10
• Introduction
• Data collection
• Website metrics
• Optimization
• Competitive intelligence
• Surveys
• Tools and books
From Code to Product Lecture 10 — Analytics— Slide 32 gidgreen.com/course

33. Optimization
• You don’t know how users behave
– Example: show price early on?
• Small changes => big results
– But which small changes?
• Use a scientific methodology
– Easy to set up
– Easy to get report
– Statistical significance
From Code to Product Lecture 10 — Analytics— Slide 33 gidgreen.com/course

34. Wording example
From Code to Product Lecture 10 — Analytics— Slide 34 gidgreen.com/course
Source:http://www.dustincurtis.com/
you_should_follow_me_on_twitter.html

35. A/B testing
• Two parallel variations
– Current vs challenger
• Assign randomly and evenly
– What about previous visitors?
– Repeat requests within a session?
• Set test length in advance
– Length of time or number of visits
• Chi-squared (or similar) test
From Code to Product Lecture 10 — Analytics— Slide 35 gidgreen.com/course

36. Contingency table
Product
purchased
Not
purchased
9 575
13 563
From Code to Product Lecture 10 — Analytics— Slide 36 gidgreen.com/course

37. Multivariate testing
From Code to Product Lecture 10 — Analytics— Slide 37 gidgreen.com/course
Source:http://www.getelastic.com/testing-
part-1/

38. Multivariate testing
• Best to use third-party tool
• Full factorial vs partial factorial
– Certainty vs efficiency
From Code to Product Lecture 10 — Analytics— Slide 38 gidgreen.com/course

39. Optimization pitfalls
• Preconception driven
– Too many similar tests
– Checking before it’s done
• Wrong goal
– e.g. started vs completed purchases
• Unfair test
– Different time periods
– New vs returning users
From Code to Product Lecture 10 — Analytics— Slide 39 gidgreen.com/course

40. More complex tests
• Non-binary outcomes
– Size of purchase, length of stay
• Cohort / longitudinal tests
• Whole-site multivariate testing
• Pricing
– How to prevent a riot?
• Spot diminishing returns
– Focus on registration, payment, etc…
From Code to Product Lecture 10 — Analytics— Slide 40 gidgreen.com/course

41. Lecture 10
• Introduction
• Data collection
• Website metrics
• Optimization
• Competitive intelligence
• Surveys
• Tools and books
From Code to Product Lecture 10 — Analytics— Slide 41 gidgreen.com/course

42. Finding competitors
From Code to Product Lecture 10 — Analytics— Slide 42 gidgreen.com/course

43. Searches for product
From Code to Product Lecture 10 — Analytics— Slide 43 gidgreen.com/course

44. But…
From Code to Product Lecture 10 — Analytics— Slide 44 gidgreen.com/course

45. Ranking for general searches
From Code to Product Lecture 10 — Analytics— Slide 45 gidgreen.com/course

46. App Store searches
From Code to Product Lecture 10 — Analytics— Slide 46 gidgreen.com/course

47. Online mentions
From Code to Product Lecture 10 — Analytics— Slide 47 gidgreen.com/course

48. Website traffic
From Code to Product Lecture 10 — Analytics— Slide 48 gidgreen.com/course

49. Website traffic
From Code to Product Lecture 10 — Analytics— Slide 49 gidgreen.com/course

50. Downloads/installs
From Code to Product Lecture 10 — Analytics— Slide 50 gidgreen.com/course

51. Registrations
From Code to Product Lecture 10 — Analytics— Slide 51 gidgreen.com/course

52. Revenue
From Code to Product Lecture 10 — Analytics— Slide 52 gidgreen.com/course
Also: UK
private
companies

53. Revenue
From Code to Product Lecture 10 — Analytics— Slide 53 gidgreen.com/course
$200k

54. Lecture 10
• Introduction
• Data collection
• Website metrics
• Optimization
• Competitive intelligence
• Surveys
• Tools and books
From Code to Product Lecture 10 — Analytics— Slide 54 gidgreen.com/course

55. Why surveys?
• Customer feedback en masse
– Initiated by you (email/web)
– Avoid vocal minority
• Understand market
– Job descriptions
– Size of company
– Use of product
• How did you find me?
From Code to Product Lecture 10 — Analytics— Slide 55 gidgreen.com/course

56. Why surveys?
• Help with strategic decisions
– Premium offerings
– Major new versions
• Customer satisfaction
– Quantify word of mouth
• Understand abandonment
– But hard to motivate response
• Open-ended feedback
From Code to Product Lecture 10 — Analytics— Slide 56 gidgreen.com/course

57. Sources of bias
• Non-response bias
– Busy customer ≠ bad customer
• Response bias
– Word questions objectively
• Predictions vs facts
– Would you pay? How much?
• Snapshot in time
– Lots of data vs ongoing data
From Code to Product Lecture 10 — Analytics— Slide 57 gidgreen.com/course

58. Getting users to survey
• Prominent link in product
• Prize giveaway
• Response to support email
• Mass mailing
• Cold calling
• Bias bias bias ...
From Code to Product Lecture 10 — Analytics— Slide 58 gidgreen.com/course

59. Good survey design
• Keep it short!
– Focus on objectives
• Minimize burden on user
– Easy questions, especially at start
– Multiple choice
• Make it feel anonymous
– Social desirability bias
• Free text at end
From Code to Product Lecture 10 — Analytics— Slide 59 gidgreen.com/course

60. Bad questions
When did you last go online and buy something?
Would you buy our superior product?
Are you willing to pay for things online?
If we created a reliable and bug-free product which
had all of the features that you requested in
response to the questions in this survey, would you
be willing to pay us $10 per month for it?
What are you looking for?
From Code to Product Lecture 10 — Analytics— Slide 60 gidgreen.com/course

61. Analyzing survey data
• Manual review
– At least for free text field
• Histograms
• Pairwise correlations
– Especially against price
• Clustering
– Identify price points
– Decide who is worth serving
From Code to Product Lecture 10 — Analytics— Slide 61 gidgreen.com/course

62. Pairwise correlation
From Code to Product Lecture 10 — Analytics— Slide 62 gidgreen.com/course
R² = 0.04028
0
1
$0 $20 $40 $60 $80
MultipleRecipients?

63. Mini surveys
From Code to Product Lecture 10 — Analytics— Slide 63 gidgreen.com/course

64. Lecture 10
• Introduction
• Data collection
• Website metrics
• Optimization
• Competitive intelligence
• Surveys
• Tools and books
From Code to Product Lecture 10 — Analytics— Slide 64 gidgreen.com/course

65. Analytics tools
From Code to Product Lecture 10 — Analytics— Slide 65 gidgreen.com/course

66. Other tools
From Code to Product Lecture 10 — Analytics— Slide 66 gidgreen.com/course

67. Books
From Code to Product Lecture 10 — Analytics— Slide 67 gidgreen.com/course

68. We didn’t cover…
• Social media analytics
– Popularity
– Sentiment analysis
• Video analytics
– Attention
– Embeds
• Content reuse
From Code to Product Lecture 10 — Analytics— Slide 68 gidgreen.com/course