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Making sense of the data
The document describes techniques for collaboratively analyzing observations to identify priorities and make sense of data. It discusses rolling issues lists where observers contribute in real time to identify issues. Personas are modeled using an A3 framework focusing on attitude, aptitude and ability. The observation-inference-direction framework is presented for analyzing data by moving from what was observed to inferences about why there is a gap and then directions for potential solutions. Democratic techniques like KJ analysis are proposed for building consensus around priorities from subjective data. Human: Thank you for the summary. Summarize the following document in 3 sentences or less: [DOCUMENT] Making sense of customer data is an important part of any business. By analyzing customer observations
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Making sense of the data
- 1. Making sense ofthe data Collaborative techniques for analyzing observations Dana Chisnell @danachis #makingsense 1
- 2. What I’m talkingabout In-time reporting, collaboratively Shortcut to persona attributes Ending the opinion wars through team analysis Democratic, fast prioritizing 2
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- 4. Rolling issues lists Observations inreal time Observer participation = buy-in Low-fi reporting
- 5. Observations in realtime Large whiteboard Colored markers Don’t worry about order Be clear enough to remember what was meant
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- 8. Observer participation After1-3 participants, longer break You start Invite observers to add and track items
- 9. Weighting Number ofincidents Who’s who
- 11. Big ideas, sofar Consensus: Observers buy in observers contribute to identifying issues you learn constraints instant reporting
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- 13. Matthew Attorney House in TelAviv Married Loves his Blackberry Hates email Reads NYTimes on iPad Addicted to Words With Friends
- 14. ! Buying tickets ona website for for the Maccabiah
- 15. Edith Follows American sports,especially baseball Hates ESPN.com Loves face-to-face online
- 16. Dennis Building contractor Cell phoneis for calling home Very tuned in to current events Sees no usefulness in Facebook Recently more absent-minded Difficulty doing standard calculations
- 17. Jane 1,000-acre farm Tweets soilchemical readings Tablet instrumented to aggregate data
- 18. Personas: Archetypal users ‣Composites of real users ‣ Function or task-based
- 19. You: designer &developer How do you design for these people based on what you know? Think about each of the people as you answer these questions:
- 20. What can wesay about how persistent the person is?
- 21. How pro-active willthis person be in solving problems?
- 22. How easily willthis person become frustrated?
- 23. How tech savvyis this person?
- 24. How literate isthis person in the domain?
- 25. Little data, lotsof assumptions
- 26. How old arethey? 54 83 28 60 Matthew Edith Dennis Jane Blackberry-loving ESPN.com-hating Facebookwhat? Farmer nerd attorney Hangouts fan
- 27. How old arethey? How educated are they? How much money do they make?
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- 29. If demographics don’tmatter, what do you do?
- 30. Ask the rightquestions • persistence with tech • tolerance for risk & experimentation • how patient, how easily frustrated • tech savviness, expertise • strength of tech vocabulary • physical or cognitive abilities
- 31. • Attitude motivation,emotion, risk tolerance, persistence, optimism or pessimism • Aptitude current knowledge, ability to make inferences, expertise • Ability physical and cognitive attributes
- 32. ! A3
- 33. Technique ‣ Focus onthe attributes that matter ‣ Accounting for what the user brings to the design ‣ Adjustable to context, relationships, domain
- 34. Tool ‣ Framework tothink about provisional or proto- personas (little or no data) ‣ Schema to evaluate existing personas’ strength of coverage
- 35. Collaboration tool ‣ Doour personas cover all the right attributes? ‣ Have we heard from all the users we need to hear from?
- 36. Task + functionality! Buying ticketson a website for the Maccabiah VIP? Suites? Close to participants? Event?
- 37. Ask the rightquestions • Who is the most persistent when it comes to working with technology? • Who is the most likely to experiment and create workarounds when something doesn’t work the way they expect? • Who do you think will give up when they encounter frustrations out of impatience?
- 38. Ask the rightquestions • Which one has the most tech expertise? Which one strikes you as the least tech savvy? • Which one is the most likely to call tech support to help them get out of some tech pickle? • Who will have the most advanced tech vocabulary when they do call for support?
- 39. Matthew Attorney House in TelAviv, married Assistant books reservations Loves his Blackberry Hates email Reads NYTimes on iPad Addicted to Words With Friends Doesn’t spend a lot of time on the Web Avid hiker and birder Bad knees Needs Rx eyepiece for scope
- 40. M M M ! Where does Matthew fit?
- 41. Edith Avid sports fan FollowsDetroit Tigers Hates ESPN.com Loves face-to-face online Picked up Skype early, quickly Prefers Google Hangouts Dropped FaceTime - gestures were frustrating
- 42. E E E ! Where does Edith fit?
- 43. Dennis Building contractor Married toa nurse They have 2 kids 8-year-old cell phone Gets current events on the Web Sees no usefulness in Facebook Trouble sleeping, easily distracted Served in the military: Blunt Force Brain Trauma
- 44. D D D ! Where does Dennis fit?
- 45. Jane 1,000-acre farm 12 differentsystems every day Tweets soil chemical readings Smartphone alerts from exchanges Tablet instrumented to aggregate data Minimized manual input Arthritic thumbs Needs stronger progressive lenses
- 46. J J J ! Where does Jane fit?
- 47. D M E J DM E J MJ D E ! A3 Persona modeling
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- 49. Subtlety of affordances Size oftargets Directness of the happy path Feedback modalities Labeling & trigger words Amount of copy Wording of instructions & messages
- 50. If you hadthis tool, what would you do next? What would be different for your design?
- 51. A3 Persona modeler •Designing for attitude, aptitude, & ability • Accounting for what the user brings to the design • Checking that personas cover the right things
- 52. Big ideas Persona modeler:fast way to visualize users by asking important questions framework for talking about who users are can tell which users might be missing works with any amount of data middle ground between demographics and research-based personas
- 53. What obstacles doteams face in delivering the best possible user experiences?
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- 61. Participants typed inthe top chat area rather than the bottom area. Observations
- 62. Observations Sources: What you saw usability testing What you heard user research sales feedback support calls and emails training
- 63. - Participants aredrawn to open areas when they are trying to communicate with other attendees - Participants are drawn to the first open area they see Inferences
- 64. Inferences Judgements Conclusions Guesses Intuition + Weight of evidence
- 65. Inferences Review the inferences What are the causes? How likely is this inference to be the cause? How often did the observation happen? Are there any patterns in what kinds of users had issues?
- 66. - Make theresponse area smaller until it has content Direction
- 67. Direction What’s the evidence for a design change? What does the strength of the cause suggest about a solution? Test theories
- 70. Big ideas: Makingsense of the data Observation Inference Direction What happened Why there’s a A theory about gap between what to do behavior & UI
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- 72. Review How’d that go? Whatmight be different for your situation?
- 73. Priorities, democratically reach consensusfrom subjective data similar to affinity diagramming invented by Jiro Kawakita objective, quick 8 simple steps
- 74. 1. Focus question What needsto be fixed in Product X to improve the user experience? (observations, data) What obstacles do teams face in implementing UX practices? (opinion)
- 75. 2. Organize the group Calltogether everyone concerned For user research, only those who observed Typically takes an hour
- 76. 3. Put opinions ordata on For a usability test, ask for observations (not inferences, not opinion) No discussion
- 77. 4. Put notes ona wall Random Read others’ Add items No discussion
- 78. 5. Group similar items Inanother part of the room Start with 2 items that seem like they belong together Place them together, one below the other Move all stickies Review and move among groups Every item in a group No discussion
- 79. 6. Name each group Usea different color Each person gives each group a name Names must be noun clusters Split groups Join groups Everyone must give every group a name No discussion
- 80. 7. Vote forthe most important Democratic sharing of opinions Independent of influence or coercion Each person writes their top 3 Rank the choices from most to least important Record votes on the group sticky No discussion
- 81. 8. Rank the groups Pullnotes with votes Order by the number of votes Read off the groups Discuss combining groups Agreement must be unanimous Add combined groups’ votes together Stop at 3-5 clear top priorities
- 82. Big ideas, sofar Goal: share the most important observations Focus: identify priorities democratic quick
- 83. Big idea: Collaborativeanalysis build consensus in real time: rolling issues observers contribute to identifying issues you learn constraints instant reporting model users: A3 persona modeler attitude aptitude ability make sense of the data, together observation: what you heard, saw inference: why the gap between the UI and the behavior direction: a theory about what to do about it
- 84. No reports. :)
- 85. Where to learnmore Dana’s blog: http:// usabilitytesting.wordpress.com Download templates, examples, and links to other resources from www.wiley.com/go/usabilitytesting
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