from chaos to action: turning usability testing data into actionable insights without going insane

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From Chaos to Action: Turning Usability Testing Data Into Actionable Insights Without Going Insane Carlos Rosemberg

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From Chaos to Action: Turning Usability Testing Data Into Actionable

Insights Without Going Insane

Carlos Rosemberg

How data is helping design

Analytics

Social media

Usability tests

A/B tests

Sales data

Customer service logs

Surveys

Interviews

Contextual research

other…

QuantitativeWHO

WHAT

WHEN

WERE

QualitativeWHY

HOW

Numerical data Non numerical data

Data driven design is a way to reduce guess-work into design

process, making it more effective

What about the efficiency in this process?

Let’s see three major problems in data driven design

Drinking from the firehoseOverwhelmed by the data

Analysis paralysisOverthinking, no outcomes

"The uncomfortable”, by Katerina Kamprani

Disconnection between research findings and solutions

How to avoid being overwhelmed by the data and choosing “not-so-good" solutions in a product development perspective?

D I V E R G E N T / C O N V E R G E N T T H I N K I N G

Tim Brown (based on Richard Nisbett’s work), 2009

T H E D O U B L E D I A M O N D

The British Design Council, 2005

Reframing to usability testing activities

OBSERVED ISSUES

SOLUTION PRIORITIZATION

Prioritize potential solutions according multiple criteria

DATA COLLECTION

Collect and generate data from usability tests

ISSUE PRIORITIZATION

Normalize and prioritize issues according multiple criteria

IDEA GENERATION

Generate potential solution ideas

RESEARCH QUESTIONS

ISSUES LIST

PROBLEM LANDSCAPE

POTENTIAL SOLUTIONS

SOLUTIONS BY ROI

Data collection

Focus on usability (effectiveness, efficiency and satisfaction)

Issues are friction events that hurt effectiveness and efficiency (mistakes, paths, system errors, etc).

An issue can occur with multiple participants.

Comments can be positive, negative or neutral.

Satisfaction can be collected as a scale (be careful!).

B A S I C I S S U E L O G F R O M U S A B I L I T Y T E S T I N G

ID WHERE TASK DESCRIPTION P1 P2 P3

1 Login page Login using a social network

Didn’t recognize the Twitter option X

2 Main menu Create a post Didn’t find the option for adding a post X X

An example of aggregated data log from 3 participants (P) as seen in Lewis/Sauro (2012)

Participants

C O L L E C T I N G S AT I S F A C T I O N L E V E L S

P1 P2 P3 P4 AVERAGE

Satisfaction rating 2 3 4 4 3,25

An example of aggregated data log from 3 participants (P).

Participants

Issue prioritization

Issues are prioritized multidimensionally.

Common dimensions are severity and frequency.

F R E Q U E N C Y

SE

VE

RIT

Y

3rd 2nd

2nd 1st

W H I C H I S S U E S A R E M O R E I M P O R TA N T ?

C A L C U L AT I N G I S S U E I M PA C T

ID TASK WHERE ISSUE SEVERITY P1 P2 P3 FREQUENCY IMPACT

1Login using a social network

Login page

Didn’t recognize the Twitter option 5 1 1 5 x 1 = 5

2 Create a post

Main menu

Took more than 3s to find the "add post" option 1 1 1 2 1 x 2 = 2

3 Create a post

Main menu

Struggled opening and closing the hamburger menu

3 1 1 2 3 x 2 = 6

A simple data log (Lewis/Sauro, 2012). Other parameters can be added.

1 (trivial) = user stops to figure out3 (major) = user accomplishes with difficulties (try and error)5 (blocker) = user doesn't accomplish the task

IMPACT = SEVERITY x FREQUENCY

FREQUENCY is the sum OR proportion of occurrences of a given issue for all participants

Entering into solution phase: Going beyond recommendations

Idea generation

Recommendations and solutions are not the same thing

Solution ideas are specific.

Some issues have obvious solutions, some not.

A solution can address multiple issues, in different levels.

Solutions prioritization

Solutions are prioritized multidimensionally

Common dimensions: effectiveness, effort

Final objective: find the ROI (Return On Investment)

2nd

E F F O R T

EF

FE

CT

IVE

NE

SS

3rd2nd

1st

W H I C H S O L U T I O N S S H O U L D B E D O N E ?

C A L C U L AT I N G S O L U T I O N R O I

ID SOLUTION IDEA I1 (5 )

I2 (2 )

I3 (6 )

EFFECTIVENESS EFFORT ROI

1 Make the Twitter login button bigger and use original blue color

3 15(3 x 5 + 0 + 0)

1 15 (15 / 1)

2Make the Twitter login button bigger and put the email login in another screen state

3 15(3 x 5 + 0 + 0)

3 5 (15 / 3)

3 Remove the hamburger menu and reorganize options hierarchically

3 6 42(0 + 3 x 2 + 6 x 6)

5 8,4 (42 / 5)

Finding the optimal path for solving UX issues

1 = Minimum effort3 = Medium effort5 = Maximum effort

SOLUTION ROI = Effectiveness / Viability

EFFECTIVENESS = Sum of all Idea Potentials x Issue Impact (I1 + I2 + I3)

Idea Potential for solving the issue. Values can be none, 1, 3 and 5.

In summary

End-to-end workflow (product development oriented).

Get all the team involved in the process.

Issues and solutions are mutually traceable.

Can be done collaboratively.

Can be extended to other techniques than usability testing.

Limitation

It does not include in prioritization the positive

attitudes and behaviors, only usability issues: This

data is logged apart.

And of course, don't forget

Numbers don't tell everything. Always remember to

take in account the QUALITATIVE data when

understanding behaviors.

https://goo.gl/RR4hEd

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T H A N K Y O UCarlos Rosembergcarlosrosemberg.com @carlbberg