Web Analytics Workshop
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This document discusses how to make a UX process effective and persuasive through the use of web analytics. It presents a framework that balances qualitative and quantitative research methods. Some key points covered include: 1. The framework involves discovery of business goals and user needs, research and analysis of website usage through descriptive analytics like user flows, and design and testing using statistical analytics like A/B testing. 2. Web analytics can track user actions but not intent, so qualitative research is still needed. Analytics can guide qualitative work by inferring intent from behaviors. 3. Setting goals, hypotheses, user segments, and filters helps organize analytics data and test how site changes affect measurable outcomes. Both qualitative and quantitative methods have roles
Web Analytics Workshop
- 1. Making Your UX Process Effective and Persuasive with Web Analytics ONeil | Rahul
- 2. Somesh Rahul @SomeshRahul Daniel ONeil @phoenix1189 Namaste! Were Information Architects at The Understanding Group (TUG)
- 3. How Somesh Got Here
- 4. How Daniel Got Here
- 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.
- 7. What Web Analytics Can and Cant do
- 8. Analytics track actions, not intent!
- 9. Web Analytics and User Experience Behaviors can infer intent Quantitative guides Qualitative Search Analytics for Your Site by - Lou Rosenfeld
- 10. The Web Analytics Framework
- 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. 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
- 14. Business Goals / User Needs Website Goals Described by: Hypotheses
- 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. 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. BUT...Goals Cant Say Why or How Goals describe behavior badly. Goals cant describe intent at all.
- 18. Hypotheses Link Goals, Behavior, and Theories about Intent.
- 19. Hypotheses A hypothesis suggests a functional change based on a theory of action that has a measurable outcome.
- 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 users 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.
- 22. Market Audience Seasonality Constrained and Organized by: Filters, Segments, Time
- 23. User Segments shops like consumer designer involved in specifying VIP at current contract customer VIP at contract prospect ?investor
- 24. Problems User Segments Addresses
- 25. Problems User Segments Addresses
- 26. Filters - Todo: something about filters here 5 minutes
- 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. User Flows - Structure User Interviews - Create User Journey - Find Path of Least Resistance
- 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. What is Statistically Significant? Determining whether the differences seen in data is more than random chance.
- 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. 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. 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. 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. What Statistics Dont 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. Marrying User Experience & Web Analytics
- 40. Your UX Process
- 41. Abstracted UX Process Discovery Research and Analysis Design and Testing
- 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. Research and Analysis Research and Analysis Research how your users approach your current site. Evaluate the websites design and information architecture. Synthesize the details into high- level models that represent both user needs and a high-level information architecture.
- 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. 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. Thank You!