The document discusses the importance of data-informed design, distinguishing it from data-driven design. It emphasizes the necessity for designers to integrate business insights into their work, utilize various data sources for informed decision-making, and improve user experience through iterative design. The text also outlines steps to develop a data-informed approach and addresses common challenges faced in implementing data-informed design practices.

1. Big Design Conference • Sept 21, 2018
MORE THAN
Data-informed Design
A FEELING

2. How Designers are Building Careers in Silicon Valley
from KPCB

3. We don’t have enough
people with business
sense to fill those seats.
Source: How Designers are Building Careers in Silicon Valley

4. What we see is the design
leader who cares about
business, who is moving
into more product roles.
Source: How Designers are Building Careers in Silicon Valley

5. To achieve great
design, you need great
business thinking and
doing…
Source: How Designers are Building Careers in Silicon Valley

6. Who are you?
1

7. Courtney Clark
Managing Director of User Experience • Forum One
@circlish
https://www.linkedin.com/in/clarkcourtney/

9. What is the difference between
data-informed and data-driven?
2

10. Intuition Data-informed Data-driven
No or minimal data used.
Design intuition, hunches.
Using both expertise and data to
analyze and make decisions.
Blindly following the data.
No humans or intuition used.

11. Source: Metrics Driven Design, Joshua Porter

12. Source: Metrics Driven Design, Joshua Porter

13. Why data-informed design?
3

14. Discover Improve
Justify Tell Better Stories
Support discussion.
Justify decisions.
Speak approvers’ language.
Support your cause.
Uncover new information.
Discover possibilities.
Optimize. Increase conversions.
Measure before / after.
Grow Skill
Communicate with analysts.
Improve your portfolio.
Be Strategic
Confirm you’re headed in the
right direction.

15. Create the Best Possible Product
Achieving business goals. Supporting primary audience.

16. You Probably Have Some Data
Use it!

17. What data can I use to
inform my design?
4

18. Da·ta (ˈdadə,ˈdādə/) n. facts and
statistics collected together for
reference and analysis

19. Digital Analytics
Search Data
Social Media Data
Email Engagement Data
Grant Data
Fundraising Data
Financial Data
Impact Data
Research Data
Volunteer Data
Event Attendance Data
Demographic Data
Brand Sentiment Data
Brand Lift Data
Competitor, Comparator Data
Net Promoter Score
So much data!

20. What People Do
What People Say
Why & How
to Fix
How Many,
How Much
First-click
Testing
Interviews
Usability
Testing
Source: Nielsen Norman Group
A/B Testing
Feedback
Widget
Email Surveys

21. HEART Framework
Source: Google Ventures
Happiness, Engagement, Adoption,
Retention, Task Success

22. Source: Google Ventures, Digital Telepathy

23. Source: Google Ventures, Digital Telepathy
H E A R T
Happiness Engagement Adoption Retention Task Success
Measures of user
attitudes, often
collected via survey.
Level of user
involvement.
Gaining new users of
a product or feature.
The rate at which
existing users are
returning.
Efficiency,
effectiveness, and
error rate.
Examples
● Satisfaction
● Perceived ease of
use
● Net-promoter
score
Examples
● Number of visits
per user per week
● Number of photos
uploaded per user
per day
● Number of shares
Examples
● Upgrades to the
latest version
● New subscriptions
created
● Purchases made
by new users
Examples
● Number of active
users remaining
present over time
● Renewal rate or
failure to retain
(churn)
● Repeat purchases
Examples
● Search result
success
● Time to upload a
photo
● Profile creation
complete

24. Source: Google Ventures, Digital Telepathy
H E A R T
Happiness Engagement Adoption Retention Task Success
Measures of user
attitudes, often
collected via survey.
Level of user
involvement.
Gaining new users of
a product or feature.
The rate at which
existing users are
returning.
Efficiency,
effectiveness, and
error rate.
Examples
● Satisfaction
● Perceived ease of
use
● Net-promoter
score
Examples
● Number of visits
per user per week
● Number of photos
uploaded per user
per day
● Number of shares
Examples
● Upgrades to the
latest version
● New subscriptions
created
● Purchases made
by new users
Examples
● Number of active
users remaining
present over time
● Renewal rate or
failure to retain
(churn)
● Repeat purchases
Examples
● Search result
success
● Time to upload a
photo
● Profile creation
complete
Survey Digital Analytics Usability Testing

25. Source: Google Ventures, Digital Telepathy
Goals Signals Metrics
Get goals from different team
members. Build consensus.
Best predictors of associated
goals.
Data you’ll track over time.
Example
For people to enjoy, discover, and engage
with content.
Example
The amount of time people spend
engaging with content.
Example
Average engagement time with content per
day.
Key Questions
● How will the user experience help?
● Are you interested in increasing the
engagement of existing users or in
attracting new users?
Key Questions
● How easy or difficult is each signal to
track?
● Is your product instrumented to log
the relevant actions, or could it be?
● Is this signal sensitive to changes in
your design?
Key Questions
● Will you actually use these numbers
to help you make a decision?
● Do you really need to track them
over time, or is a current snapshot
sufficient?

26. Source: Google Ventures, Digital Telepathy

27. Only need to use
categories or data
relevant to your goal
or product.

28. How to build a data-informed
approach?
5

29. 1. Set up a meeting with your business analyst,
analytics team, data person
2. Inventory the data you have on your project now
3. Fill out the HEART worksheet
4. Design!
5. Iterate, test, improve
6. Reflect and debrief with your team
Get Started

30. Ask yourself:
What data do we have?
How will we get data to validate this?

31. What challenges will I face with
data-informed design?
6

32. Data Availability & Skills

33. GOOD BAD UGLY
90% of nonprofits are
collecting data
49% don’t know ways
their org is collecting
13% never or rarely use
data.
Source: Everyaction • 2016 • The State of Nonprofit Data white paper

34. Not collecting enough data 36%
Source: Everyaction • 2016 • The State of Nonprofit Data white paper
Lack of tools to help analyze data
Data isn’t kept in one place
Don’t have enough experience using data
Not enough time, or personnel to focus on data
42%
46%
55%
79%

35. Over-indexing on Data-driven

36. I had a recent debate
over whether a border
should be 3, 4 or 5
pixels wide, and was
asked to prove my case.
Source: Goodbye Google, Douglas Bowman

37. Source: Metrics Driven Design, Joshua Porter

38. Wait, why should I care about this?
7

39. To achieve great
design, you need great
business thinking and
doing…
Source: How Designers are Building Careers in Silicon Valley

40. Discover Improve
Justify Tell Better Stories
Support discussion.
Justify decisions.
Speak approvers’ language.
Support your cause.
Uncover new information.
Discover possibilities.
Optimize. Increase conversions.
Measure before / after.
Grow Skill
Communicate with analysts.
Improve your portfolio.
Be Strategic
Confirm you’re headed in the
right direction.

41. Now what?
8

42. 1. Set up a meeting with your business analyst,
analytics team, data person
2. Inventory the data you have on your project now
3. Fill out the HEART worksheet
4. Design!
5. Iterate, test, improve
6. Reflect and debrief with your team
Get Started

43. Keep Reading
Data Informed Design, Not Data-Driven
How to Choose the Right UX Metrics
The Agony and Ecstasy of Building with Data
Data-informed Design (5 Things I Learned the Hard Way)
Data-driven vs Data-informed Design in Enterprise Products

44. Questions
& Answers

45. Courtney Clark
Managing Director of User Experience • Forum One
@circlish
https://www.linkedin.com/in/clarkcourtney/