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Making sense of the dataCollaborative techniques for analyzing observations
Dana Chisnell@danachis#makingsense
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What I’m talking aboutIn-time reporting, collaboratively
Shortcut to persona attributes
Ending the opinion wars through team analysis
Democratic, fast prioritizing
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Rolling Issues Lists
Rolling issues listsObservations in real time
Observer participation = buy-in
Low-fi reporting
Observations in real timeLarge whiteboard
Colored markers
Don’t worry about order
Be clear enough to remember what was meant
Rolling issues
Rolling issuesAngela Coulter
Observer participationAfter 1-3 participants, longer break
You start
Invite observers to add and track items
Weighting
Number of incidents
Who’s who
Consensus: Observers buy in
observers contribute to identifying issuesyou learn constraintsinstant reporting
Big ideas, so far
A3 Persona studio
MatthewAttorney House in Tel Aviv MarriedLoves his BlackberryHates emailReads NYTimes on iPadAddicted to Words With Friends
!
Buying tickets on a website for for the Maccabiah
EdithFollows American sports, especially baseballHates ESPN.com Loves face-to-face online
DennisBuilding contractor Cell phone is for calling home Very tuned in to current eventsSees no usefulness in FacebookRecently more absent-mindedDifficulty doing standard calculations
Jane1,000-acre farm Tweets soil chemical readings Tablet instrumented to aggregate data
Personas: Archetypal users‣ Composites of real users
‣ Function or task-based
You: designer & developerHow do you design for these people based on what you know?
Think about each of the people as you answer these questions:
What can we say about how persistent the person is?
How pro-active will this person be in solving problems?
How easily will this person become frustrated?
How tech savvy is this person?
How literate is this person in the domain?
Little data, lots of assumptions
MatthewBlackberry-loving attorney
How old are they?
54 83 28 60
EdithESPN.com-hating Hangouts fan
DennisFacebookwhat?
JaneFarmer nerd
How old are they? How educated are they?
How much money do they make?
These don’t matter.
If demographics don’t matter, what do you do?
• persistence with tech
• tolerance for risk & experimentation
• how patient, how easily frustrated
• tech savviness, expertise
• strength of tech vocabulary
• physical or cognitive abilities
Ask the right questions
•Attitudemotivation, emotion, risk tolerance, persistence, optimism or pessimism
•Aptitudecurrent knowledge, ability to make inferences, expertise
•Ability physical and cognitive attributes
!
A3
Technique‣ Focus on the attributes that matter
‣ Accounting for what the user brings to the design
‣ Adjustable to context, relationships, domain
Tool‣ Framework to think about provisional or proto-
personas (little or no data)
‣ Schema to evaluate existing personas’ strength of coverage
Collaboration tool‣ Do our personas cover all the right attributes?
‣ Have we heard from all the users we need to hear from?
Task + functionality!
Buying tickets on a website for the MaccabiahVIP?
Suites?
Close to participants?
Event?
• 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?
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?
Ask the right questions
MatthewAttorney House in Tel Aviv, marriedAssistant books reservations Loves his BlackberryHates emailReads NYTimes on iPadAddicted to Words With FriendsDoesn’t spend a lot of time on the Web Avid hiker and birderBad knees Needs Rx eyepiece for scope
!
Where does Matthew fit?
M
M
M
EdithAvid sports fanFollows Detroit Tigers Hates ESPN.com Loves face-to-face onlinePicked up Skype early, quickly Prefers Google Hangouts Dropped FaceTime - gestures were frustrating
!
Where does Edith fit?
E
E
E
DennisBuilding contractor Married to a nurseThey have 2 kids8-year-old cell phone Gets current events on the Web Sees no usefulness in FacebookTrouble sleeping, easily distractedServed in the military: Blunt Force Brain Trauma
!
Where does Dennis fit?
D
D
D
Jane1,000-acre farm 12 different systems every dayTweets soil chemical readings Smartphone alerts from exchangesTablet instrumented to aggregate dataMinimized manual inputArthritic thumbsNeeds stronger progressive lenses
!
Where does Jane fit?
J
J
J
!
A3 Persona modeling
M
M
M
E
E
E
D
D
D
J
JJ
How do design decisions change with these attributes?
Subtlety of affordances
Size of targets
Directness of the happy path
Feedback modalities
Labeling & trigger words
Amount of copy
Wording of instructions & messages
If you had this tool, what would you do next?What would be different for your design?
• Designing for attitude, aptitude, & ability
• Accounting for what the user brings to the design
• Checking that personas cover the right things
A3 Persona modeler
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
Big ideas
What obstacles do teams facein delivering the best possible user experiences?
Guess the reason
Observation to Direction
Observations to direction
Observation Inference Direction
Observations
Participants typed in the top chat area rather than the bottom area.
ObservationsSources:
usability testing
user research
sales feedback
support calls and emails
training
What you saw
What you heard
Inferences
- Participants are drawn to open areas when they are trying to communicate with other attendees - Participants are drawn to the first open area they see
InferencesJudgements
Conclusions
Guesses
Intuition
+ Weight of evidence
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?
Direction
- Make the response area smaller until it has content
DirectionWhat’s the evidence for a design change?
What does the strength of the cause suggest about a solution?
Test theories
Big ideas: Making sense of the data
Observation What happened
Inference Why there’s a gap between behavior & UI
Direction A theory about what to do
KJ Analysis
ReviewHow’d that go? What might be different for your situation?
Priorities, democratically
reach consensus from subjective data
similar to affinity diagramming
invented by Jiro Kawakita
objective, quick
8 simple steps
1. Focus questionWhat needs to be fixed in Product X to improve the user experience?(observations, data)
What obstacles do teams face in implementing UX practices? (opinion)
2. Organize the groupCall together everyone concerned
For user research, only those who observed
Typically takes an hour
3. Put opinions or data on For a usability test, ask for observations
(not inferences, not opinion)
No discussion
4. Put notes on a wallRandom
Read others’
Add items
No discussion
5. Group similar items In another 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
6. Name each groupUse a 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
7. Vote for the 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
8. Rank the groupsPull notes 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
Goal: share the most important observations
Focus: identify priorities
democraticquick
Big ideas, so far
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
No reports. :)
Where to learn more
Dana’s blog: http://usabilitytesting.wordpress.com
Download templates, examples, and links to other resources from www.wiley.com/go/usabilitytesting
www.usabilityworks.net 415.519.1148
@danachis
Dana Chisnell
