Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Making Your UX Process
Effective and Persuasive
with Web Analytics
O’Neil | Rahul
Somesh Rahul
@SomeshRahul
Daniel O’Neil
@phoenix1189
Namaste! We’re Information Architects at
The Understanding Group (TUG)
How Somesh Got Here
How Daniel Got Here
Intended Takeaways
1. Be able to apply a framework for balancing quantitative
and qualitative research methods.
2. Have a ...
Why Web Analytics
What Web Analytics Can and Can’t do
Analytics track actions, not intent!
Web Analytics and User Experience
Behaviors can infer intent
Quantitative guides Qualitative
Search Analytics for Your
Sit...
The Web Analytics Framework
Business Goals and User Needs
Market, Audience, Seasonality
Testable On-Site Behavior
Website Analytics Works Best When Th...
Business Goals /
User Needs
Website Goals
Described by:
Hypotheses
Market
Audience
Seasonality
Constrained and Organized b...
Goals and Hypotheses
Business Goals /
User Needs
Website Goals
Described by:
Hypotheses
Websites Run on Goals
A “Goal” is a measurable outcome resulting in a user
completing some desired activity on your websit...
The Best Goals are Existentially Critical
– Is it a “Holiday Bonus” question?
– If this goal stopped happening, would your...
BUT...Goals Can’t Say Why or How
– Goals describe behavior badly.
– Goals can’t describe intent at all.
Hypotheses Link
Goals, Behavior,
and Theories
about Intent.
Hypotheses
A hypothesis suggests a functional change based on a
theory of action that has a measurable outcome.
Goals and Hypotheses
– Goals link clear up or down numbers to the outcome of
a specific site behavior.
– Hypotheses provid...
Filters and Segments
Market
Audience
Seasonality
Constrained and Organized by:
Filters, Segments, Time
User Segments
shops like
consumer
designer
involved
in specifying
VIP at
current
contract
customer
VIP at
contract
prospec...
Problems User Segments Addresses
Problems User Segments Addresses
Filters
- Todo: something about filters here
5
minutes
Descriptive Analytics
Testable On-Site
Behavior
Tested by:
Descriptive
Analytics
Flow, Page Navigation, Nonstatistical
narratives
Statistical
An...
User Flows
- Structure User Interviews
- Create User Journey
- Find Path of Least Resistance
Stand-alone
Integrated
External
/ Social
Statistical Analytics
Testable On-Site
Behavior
Tested by:
Descriptive
Analytics
Flow, Page Navigation, Nonstatistical narratives
Statistical
An...
What is Statistically Significant?
Determining whether the differences seen in data is more
than random chance.
Why Use It?
- Addresses the HiPPO problem.
- Saves time by getting to outcomes faster.
- Uncovers subtle effects.
- Confro...
Quantifying a “measurable outcome”
If goals have been set up properly, outcomes can be
measured using simple statistics. A...
Appropriate A/B Tests Should:
- Be immediately apparent to anyone looking comparing
the pages.
- Be defined in a functiona...
Typical A/B Test Candidates
Question Testing For Best Testing Tool
Is the navigation layout
affecting conversion rate?
Con...
What Statistics Don’t Tell You
- Why a test failed. This can be just as critical as a
success.
- Why it succeeded.
- How t...
Marrying User Experience &
Web Analytics
Your UX Process
Abstracted UX Process
Discovery
Research and
Analysis
Design and
Testing
Discovery
Discovery
● Establish clearly the “Why” and
“Who” for the site.
● Organizational goals are
articulated and prior...
Research and Analysis
Research and
Analysis
● Research how your users
approach your current site.
● Evaluate the website’s...
Design and Testing
Design and
Testing
● Specify the site structure.
● Determine the ways in which the
goals will be achiev...
Qualitative and Quantitative Research
Discovery
Research and
Analysis
Design and
Testing
Qualitative
Research
(UX)
Quantit...
Thank You!
Upcoming SlideShare
Loading in …5
×

Web Analytics Workshop

498 views

Published on

Analytics to strengthen your UX process

Published in: Education
  • Be the first to comment

  • Be the first to like this

Web Analytics Workshop

  1. 1. Making Your UX Process Effective and Persuasive with Web Analytics O’Neil | Rahul
  2. 2. Somesh Rahul @SomeshRahul Daniel O’Neil @phoenix1189 Namaste! We’re Information Architects at The Understanding Group (TUG)
  3. 3. How Somesh Got Here
  4. 4. How Daniel Got Here
  5. 5. Intended Takeaways 1. Be able to apply a framework for balancing quantitative and qualitative research methods. 2. Have a grasp of several key Google Analytics tools that are most relevant to UX practices. 3. Learn through labs, workshops and case studies how web analytics is applied to actual UX projects.
  6. 6. Why Web Analytics
  7. 7. What Web Analytics Can and Can’t do
  8. 8. Analytics track actions, not intent!
  9. 9. Web Analytics and User Experience Behaviors can infer intent Quantitative guides Qualitative Search Analytics for Your Site by - Lou Rosenfeld
  10. 10. The Web Analytics Framework
  11. 11. Business Goals and User Needs Market, Audience, Seasonality Testable On-Site Behavior Website Analytics Works Best When They are Measuring the Distillation of True Value
  12. 12. Business Goals / User Needs Website Goals Described by: Hypotheses Market Audience Seasonality Constrained and Organized by: Filters, Segments, Time Testable On-Site Behavior Tested by: Descriptive Analytics Flow, Page Navigation, Nonstatistical narratives Statistical Analytics A/B testing of Page Variations and Dimension Segments
  13. 13. Goals and Hypotheses
  14. 14. Business Goals / User Needs Website Goals Described by: Hypotheses
  15. 15. Websites Run on Goals A “Goal” is a measurable outcome resulting in a user completing some desired activity on your website. Typical goals are: – Confirmation page at the end of a sales transaction. – Thank-you page after filling out a contact or quote request form. – Application or content downloads. – Playing a game or watching a video on a site.
  16. 16. The Best Goals are Existentially Critical – Is it a “Holiday Bonus” question? – If this goal stopped happening, would your organization (or your department) still exist? – Most companies should have a few goals filtered through many segments.
  17. 17. BUT...Goals Can’t Say Why or How – Goals describe behavior badly. – Goals can’t describe intent at all.
  18. 18. Hypotheses Link Goals, Behavior, and Theories about Intent.
  19. 19. Hypotheses A hypothesis suggests a functional change based on a theory of action that has a measurable outcome.
  20. 20. Goals and Hypotheses – Goals link clear up or down numbers to the outcome of a specific site behavior. – Hypotheses provide the testable narrative about how the user’s experience on the site affects those goals. – The testable narrative does not have to BE a goal, but should specifically be IN SERVICE OF a goal.
  21. 21. Filters and Segments
  22. 22. Market Audience Seasonality Constrained and Organized by: Filters, Segments, Time
  23. 23. User Segments shops like consumer designer involved in specifying VIP at current contract customer VIP at contract prospect ?investor
  24. 24. Problems User Segments Addresses
  25. 25. Problems User Segments Addresses
  26. 26. Filters - Todo: something about filters here 5 minutes
  27. 27. Descriptive Analytics
  28. 28. Testable On-Site Behavior Tested by: Descriptive Analytics Flow, Page Navigation, Nonstatistical narratives Statistical Analytics A/B testing of Page Variations and Dimension Segments
  29. 29. User Flows - Structure User Interviews - Create User Journey - Find Path of Least Resistance
  30. 30. Stand-alone Integrated External / Social
  31. 31. Statistical Analytics
  32. 32. Testable On-Site Behavior Tested by: Descriptive Analytics Flow, Page Navigation, Nonstatistical narratives Statistical Analytics A/B testing of Page Variations and Dimension Segments
  33. 33. What is Statistically Significant? Determining whether the differences seen in data is more than random chance.
  34. 34. Why Use It? - Addresses the HiPPO problem. - Saves time by getting to outcomes faster. - Uncovers subtle effects. - Confronts our own biases about aesthetic and design.
  35. 35. Quantifying a “measurable outcome” If goals have been set up properly, outcomes can be measured using simple statistics. And simple is all we need! The recommended statistical method for UX professionals is the A/B test.
  36. 36. Appropriate A/B Tests Should: - Be immediately apparent to anyone looking comparing the pages. - Be defined in a functional UX way. - Represent a set of coherent conceptual changes against a single hypothesis.
  37. 37. Typical A/B Test Candidates Question Testing For Best Testing Tool Is the navigation layout affecting conversion rate? Conversion rate by template Google Analytics Experiements (Not out of the box but you can hack it) Which of two landing pages performs better? Conversion rate by page version Google Analytics Experiments Which User segment converts better Conversion rate compared by User segment Advanced segments, Confidence Interval test
  38. 38. What Statistics Don’t Tell You - Why a test failed. This can be just as critical as a success. - Why it succeeded. - How to thoughtfully create testable hypotheses.
  39. 39. Marrying User Experience & Web Analytics
  40. 40. Your UX Process
  41. 41. Abstracted UX Process Discovery Research and Analysis Design and Testing
  42. 42. Discovery Discovery ● Establish clearly the “Why” and “Who” for the site. ● Organizational goals are articulated and prioritized. ● The audience is clearly identified. ● The ultimate measures of success are agreed upon.
  43. 43. Research and Analysis Research and Analysis ● Research how your users approach your current site. ● Evaluate the website’s design and information architecture. ● Synthesize the details into high- level models that represent both user needs and a high-level information architecture.
  44. 44. Design and Testing Design and Testing ● Specify the site structure. ● Determine the ways in which the goals will be achieved through site structure. ● Test.
  45. 45. Qualitative and Quantitative Research Discovery Research and Analysis Design and Testing Qualitative Research (UX) Quantitative Research (WA) Stakeholder Interviews Intention Modeling User Interviews Personas User Journeys Prototypes Live Testing Hypothesis Generation Goal Context User Segments User Flows A/B Tests
  46. 46. Thank You!

×