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Making sense of the data


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  • 1. Making sense of the dataCollaborative techniques foranalyzing observationsDana Chisnell@danachis#makingsense 1
  • 2. What I’m talking about In-time reporting, collaboratively Shortcut to persona attributes Ending the opinion wars through team analysis Democratic, fast prioritizing 2
  • 3. RollingIssuesLists
  • 4. Rolling issueslistsObservations in real timeObserver participation = buy-inLow-fi reporting
  • 5. Observations in real time Large whiteboard Colored markers Don’t worry about order Be clear enough to remember what was meant
  • 6. Rolling issues
  • 7. Angela CoulterRolling issues
  • 8. Observer participation After 1-3 participants, longer break You start Invite observers to add and track items
  • 9. Weighting Number of incidents Who’s who
  • 10. Big ideas, so farConsensus: Observers buy in observers contribute to identifying issues you learn constraints instant reporting
  • 11. A3Personastudio
  • 12. MatthewAttorneyHouse in Tel AvivMarriedLoves his BlackberryHates emailReads NYTimes on iPadAddicted to Words With Friends
  • 13. !Buying tickets on awebsite for for theMaccabiah
  • 14. EdithFollows American sports, especiallybaseballHates ESPN.comLoves face-to-face online
  • 15. DennisBuilding contractorCell phone is for calling homeVery tuned in to current eventsSees no usefulness in FacebookRecently more absent-mindedDifficulty doing standard calculations
  • 16. Jane1,000-acre farmTweets soil chemical readingsTablet instrumented to aggregate data
  • 17. Personas: Archetypal users‣ Composites of real users‣ Function or task-based
  • 18. 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:
  • 19. What can we say about how persistent the person is?
  • 20. How pro-active will this person be in solving problems?
  • 21. How easily will this person become frustrated?
  • 22. How tech savvy is this person?
  • 23. How literate is this person in the domain?
  • 24. Little data, lots of assumptions
  • 25. How old are they? 54 83 28 60Matthew Edith Dennis JaneBlackberry-loving Facebookwhat? Farmer nerdattorney Hangouts fan
  • 26. How old are they? How educated are they?How much money do they make?
  • 27. These don’t matter.
  • 28. If demographics don’t matter,what do you do?
  • 29. Ask the right questions• persistence with tech• tolerance for risk & experimentation• how patient, how easily frustrated• tech savviness, expertise• strength of tech vocabulary• physical or cognitive abilities
  • 30. • Attitude motivation, emotion, risk tolerance, persistence, optimism or pessimism• Aptitude current knowledge, ability to make inferences, expertise• Ability physical and cognitive attributes
  • 31. ! A3
  • 32. Technique‣ Focus on the attributes that matter‣ Accounting for what the user brings to the design‣ Adjustable to context, relationships, domain
  • 33. Tool‣ Framework to think about provisional or proto- personas (little or no data)‣ Schema to evaluate existing personas’ strength of coverage
  • 34. Collaboration tool‣ Do our personas cover all the right attributes?‣ Have we heard from all the users we need to hear from?
  • 35. Task +functionality!Buying tickets on awebsite for theMaccabiahVIP?Suites?Close to participants?Event?
  • 36. Ask the right questions• 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?
  • 37. Ask the right questions• 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?
  • 38. MatthewAttorneyHouse in Tel Aviv, marriedAssistant books reservationsLoves his BlackberryHates emailReads NYTimes on iPadAddicted to Words With FriendsDoesn’t spend a lot of time on the WebAvid hiker and birderBad kneesNeeds Rx eyepiece for scope
  • 39. M M M! Where does Matthew fit?
  • 40. EdithAvid sports fanFollows Detroit TigersHates ESPN.comLoves face-to-face onlinePicked up Skype early, quicklyPrefers Google HangoutsDropped FaceTime - gestures were frustrating
  • 41. E E E! Where does Edith fit?
  • 42. DennisBuilding contractorMarried to a nurseThey have 2 kids8-year-old cell phoneGets current events on the WebSees no usefulness in FacebookTrouble sleeping, easily distractedServed in the military: Blunt Force Brain Trauma
  • 43. D D D! Where does Dennis fit?
  • 44. Jane1,000-acre farm12 different systems every dayTweets soil chemical readingsSmartphone alerts from exchangesTablet instrumented to aggregate dataMinimized manual inputArthritic thumbsNeeds stronger progressive lenses
  • 45. J J J! Where does Jane fit?
  • 46. D M E J DM E J MJ D E! A3 Persona modeling
  • 47. How dodesigndecisionschange withtheseattributes?
  • 48. Subtlety ofaffordancesSize of targetsDirectness of thehappy pathFeedback modalitiesLabeling & triggerwordsAmount of copyWording ofinstructions &messages
  • 49. If you had this tool, what would you donext?What would be different for yourdesign?
  • 50. A3 Persona modeler• Designing for attitude, aptitude, & ability• Accounting for what the user brings to the design• Checking that personas cover the right things
  • 51. Big ideasPersona modeler: fast way to visualize users byasking 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
  • 52. What obstacles do teams facein delivering the best possibleuser experiences?
  • 53. Guess the reason
  • 54. Observationto Direction
  • 55. Observationsto directionObservationInferenceDirection
  • 56. Participants typed in the top chat area ratherthan the bottom area.Observations
  • 57. Observations Sources: What you saw usability testing What you heard user research sales feedback support calls and emails training
  • 58. - Participants are drawn to open areas when they are trying to communicate with other attendees - Participants are drawn to the first open area they seeInferences
  • 59. Inferences Judgements Conclusions Guesses Intuition + Weight of evidence
  • 60. 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?
  • 61. - Make the response area smaller until it has content Direction
  • 62. Direction What’s the evidence for a design change? What does the strength of the cause suggest about a solution? Test theories
  • 63. Big ideas: Making sense of the data Observation Inference Direction What happened Why there’s a A theory about gap between what to do behavior & UI
  • 64. KJAnalysis
  • 65. ReviewHow’d that go?What might be differentfor your situation?
  • 66. Priorities, democraticallyreach consensus fromsubjective datasimilar to affinitydiagramminginvented by Jiro Kawakitaobjective, quick8 simple steps
  • 67. 1. FocusquestionWhat needs to be fixed inProduct X to improve the userexperience?(observations, data)What obstacles do teams face inimplementing UX practices?(opinion)
  • 68. 2. Organizethe groupCall together everyoneconcernedFor user research, only thosewho observedTypically takes an hour
  • 69. 3. Put opinionsor data onFor a usability test, ask forobservations(not inferences, not opinion)No discussion
  • 70. 4. Put noteson a wallRandomRead others’Add itemsNo discussion
  • 71. 5. Groupsimilar itemsIn another part of the roomStart with 2 items that seem likethey belong togetherPlace them together, one belowthe otherMove all stickiesReview and move among groupsEvery item in a groupNo discussion
  • 72. 6. Name eachgroupUse a different colorEach person gives each group anameNames must be noun clustersSplit groupsJoin groupsEveryone must give every groupa nameNo discussion
  • 73. 7. Vote for themost importantDemocratic sharing of opinionsIndependent of influence orcoercionEach person writes their top 3Rank the choices from most toleast importantRecord votes on the group stickyNo discussion
  • 74. 8. Rank thegroupsPull notes with votesOrder by the number of votesRead off the groupsDiscuss combining groupsAgreement must be unanimousAdd combined groups’ votestogetherStop at 3-5 clear top priorities
  • 75. Big ideas, so farGoal: share the most important observationsFocus: identify priorities democratic quick
  • 76. Big idea: Collaborative analysis 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
  • 77. No reports. :)
  • 78. Where to learn moreDana’s blog: http://usabilitytesting.wordpress.comDownload templates, examples, andlinks to other resources
  • 79. DanaChisnelldana@usabilityworks.netwww.usabilityworks.net415.519.1148@danachis