Educational presentation by Lucinda Burtt (Head of Product at Fairfax) explaining in detail how to develop a product using qualitative and quantitative research, data and A/B testing.

1. _
Designing with data
Lucinda Burtt - Fairfax Media
@LucindaBurtt

2. _
Intro
What makes great product design?

3. why what
want

4. Experience Design
Discern how the user will
interact with the product,
allow them to do what they
are trying to do

5. Visual Design
Make the interface
communicate what it can do
so the user wants to interact
with it

6. Research &
Optimisation
To discover who our users
are and ask: why would they
use our product?
Follow up: will they use it?

7. _
Why UX design and data?
Definitions & Lean UX

8. _
Facts or information used to
calculate, analyse or plan something
Merriam-Webster
data

9. “
Research is formalised curiosity.
It is poking and prying with a purpose
Zora Neale Hurston

10. A set of principles (not a process) for
creating digital experiences that are
valued by end users
The three foundations of Lean UX
● Design thinking
Innovation through observation
● Agile software development
Continuous delivery
● Build-Measure-Learn
Lean Startup feedback loop
Source: Lean UX (2013), Jeff Gothelf

11. Further reading: Stanford d.school

12. Source: Lean UX (2013), Jeff Gothelf

13. Principles of Lean UX
Aiming for
Early customer validation
Outcomes, not outputs
Collaborative design
Measuring KPIs
Applying appropriate tools
Further reading: ‘The Lean UX Manifesto’ - smashingmagazine.com, Anthony Viviano
Instead of
Releasing unknown end-user value
Working on the next ‘cool’ feature
Siloed product design & development
Undefined success metrics
Following a rigid plan

14. Early customer
validation
● Learning about real user
needs
● 360° view of the
customer
● Seeking customer
validation or early
failure
Surveys
User forms
Call centre
Usability tests
Card sorting
Support emails
Review sites
Analytics
Diary studies
Focus groups
Outbound calls

15. Outcomes, not
output
● What problem are we
trying to solve?
● Hypothesis statements:
we believe that...
● User stories/JTBD

16. Collaborative
design
● Design studio method:
create, pitch, critique
● Style guides and pattern
libraries
● Cross-functional:
product, business
analyst, design & dev
teams

17. Measuring KPIs
● Acquisitions
● Activation
● Retention
● Referral
● Revenue

18. Applying
appropriate tools
● Done, not perfect
● User personas
● Identifying assumptions
● Prototyping

19. _
Making meaning from data
What data to use?

20. Source: ‘When to use which UX methods’ - nngroup.com, Christian Rohrer

21. What data to collect?
Qualitative
The “why” and “how”
Understanding user logic
Provides perspective, direct
Looks like: usability testing, personas
Quantitative
The “how many/much” and “will they…”
Understanding user patterns
Provides tactical insight, indirect
Looks like: A/B testing, analytics
(heatmaps)

22. Phases of product
development
● Strategize
(Qualitative/Quantitative)
New ideas and opportunities
● Execute
(Qualitative)
“Go/No-Go” decisions
● Assess
(Quantitative)
Measurement of success
Source: ‘When to use which UX methods’ - nngroup.com, Christian Rohrer

23. Lean user testing
● Less users, more often
● Sketches validate value,
visual mockups test
brand effectiveness,
prototypes provide
direct insight
● Results fed straight into
development

24. User personas
● Fictional yet realistic,
data-based description
of a typical/target user
● Each persona
represents a cluster of
user behaviours/needs
● A tool to create
empathy between us
and the user

25. Heatmaps
● Graphical/visual
representation of data
● Insights into attention
and distraction metrics
● Identifying problem
areas
● Behavioural

26. A/B testing
● Testing intuitions and
assumptions
● Validating our qual
insights
● Understanding the wide
impact of design
decisions
● Tactical

27. What to A/B test first?
The PIE framework
● Potential
How much improvement can be
made?
● Importance
How valuable is the traffic to those
pages?
● Ease
How complex is the test to
implement?
Source: ‘How to prioritise [CRO] tests using PIE’ - widerfunnel.com, Chris Goward

29. Case Study
Newswell WOFFs

31. _
Moving to data informed design
Conclusion

32. “
If I had asked people what they wanted, they would
have said faster horses
Henry Ford

33. Data informed design
● Focus on key decisions
Testable, tactical
● Define your hypothesis
● Choose your KPIs wisely
Breadth of data
● Benchmark
Set a control, compare like-for-like
● Analyse & Apply
Design, collect, analyse, iterate
Further reading: ‘Data informed design’ - uxforthemasses.com, Neil Turner

34. _
Thank you!
16 June 2016
@LucindaBurtt