This presentation will bring big data into the context of UX research by describing how big data can inform usability in three ways, focusing primarily on strategy and quantitative models. A case study involving field research will be explained and the audience will act as the UX team to help build the model at each stage to better understand the theory and final product that resulted. Quantitative models help make product research more interpretable by developing testable, causal relationships between product features and business outcomes (e.g., feel of product and product satisfaction), going beyond descriptive statistics for each feature and attribute. In this way, stakeholders know not just what features are performing or underperforming, but whether those are impacting the overall performance of the product on key outcomes.
3. We solve customer
problems by utilizing
research & analytics, in a
customer centered design
process to deliver
experiences that meet or
exceed customer
expectations.
4. 04
Hi
UX
@
Cox
We
are
a
group
of
User
Experience
Strategists,
Researchers,
Data
ScienFsts,
Visual
Designers
&
Prototypers
that
come
together
on
a
regular
basis
to
solve
customer
and
business
problems.
We
are
passionate
about
understanding
customer
behaviors
and
creaFng
experiences
that
are
delighSul
in
a
way
that
makes
recommenders
out
of
our
customers.
We
use
different
techniques
to
understand
our
customers
behaviors
through
research
and
analyFcs
to
inform
design
to
drive
innovaFve
soluFons.
These
soluFons
then
allow
us
to
drive
adopFon,
increased
usage,
lowering
cost
and/or
increasing
sales
depending
on
the
experience
domain.
UX
5. The
team:
Research
&
AnalyFcs
+
Design
9
4Research
&
Analytics
Design
We
now
have
4
UX
UX
Strategists,
3
Visual
Designers,
1
Content
Strategist.
We
combined
the
Research
&
AnalyFcs
funcFon
to
start
generaFng
insights.
Interns
and
fresh
graduates
play
a
criFcal
role
in
the
group,
our
promise
is
to
develop
these
individuals
for
the
next
level.
5
Design Interns/
fresh graduates
4
R+A Interns/
fresh graduates
05
7. We
drive
design
from
data
and
this
could
be
qualitaFve
and
quanFtaFve
in
nature
Data
The
Physical
space
&
context
in
which
the
experience
unfolds
for
the
customer
needs
to
be
considered
Physical
Different
types
of
properFes
and
devices
need
to
be
considered
in
an
experience
Digital
Approach to Design
We
drive
design
through
the
eyes
of
the
customer
&
u:lizing
insights
gleamed
from
data
&
analysis.
The
manifestaFon
of
the
design
can
be
in
the
digital
space
or
in
the
physical
space
regardless,
considers
the
impact
of
physical
&
digital
space
on
the
design
of
the
experience.
Data
Digital
Physical
8. 08
Design
We
think
about
design
as
the
creaFon
of
a
plan
for
the
construcFon
of
a
product
or
service;
we
think
about
experience
design
in
terms
of
human
to
human
&
human
to
computer
interacFons.
We
think
about
it
in
terms
of
customer
behaviors
and
uFlizing
design
to
facilitate
those
behaviors
in
a
way
that
benefits
the
customer
as
well
as
the
business.
03
PROTOTYPING
Rudimentary
working
model
of
a
product
or
informaFon
system,
usually
built
to
try
new
ideas
or
as
model
to
learn
from.
04
CONTENT
STRATEGY
Planning,
development,
and
management
of
content—wrifen
or
in
other
media.
01
UX
STRATEGY
Taking
the
informaFon
about
the
user
and
informaFon
about
the
business
and
turning
that
into
an
approach
for
the
User
Experience.
02
VISUAL
DESIGN
Method
of
communicaFon,
and
problem-‐
solving
through
the
use
of
type,
space
and
image.
9. 09
Design Process
We
use
an
iteraFve
design
process
to
create
delighSul
product
&
service
experiences.
We
uFlize
our
understanding
of
customer
behaviors
in
combinaFon
with
our
understanding
of
technology
to
create
soluFons
that
help
customers
and
our
business.
The
iteraFve
process
helps
us
test
soluFons
in
controlled
environments
or
within
markets.
Research
Get
it
Built
Launch
Go
Deep
+
Go
Broad
_
Get
It
Started
Research
&
Analy:cs
Monitor
10. 10
Design– Blueprints
Blueprints
provide
the
foundaFon
to
the
digital
experience
3
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Charcoal Black
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Dark Gray
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Gray
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Primary Action
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Secondary Action
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7
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11. 11
Approach to Research & Analytics
Health
This
is
a
view
into
the
health
of
the
product/service
in
the
field.
InteracFons
of
customers
with
product/service
or
a
product/service
interacFon
with
systems
result
in
the
end
experience
for
the
customer.
Both
of
these
interacFons
at
the
highest
level
make
up
the
customer’s
percepFon
of
a
product/service.
DiagnosFc
Diagnosis
begins
with
a
symptom
or
problem
experienced,
we
use
the
symptom
to
invesFgate
through
the
available
data
likely
causes.
Strategic
This
is
typically
an
12-‐18month
view
of
things
to
focus
on
a
product/service.
These
are
typically
driven
by
quanFtaFve
models
that
help
in
ascertaining
investments
in
the
product/service
and/or
in
a
porSolio
of
product/service.
Health
Diagnos:c
Strategic
12. 12
Research & Analytics
DESIGN
RESEARCH
Number
of
invesFgaFve
techniques
used
to
add
context
and
insight
to
the
design
process.
PRE-‐LAUNCH
ASSESSMENT
Assessing
the
risk
associated
with
the
launch
of
a
product
or
service
in
relaFonship
to
the
ease
of
use/usability
of
the
product/service.
POST
LAUNCH
ASSESSMENT
Understanding
the
performance
of
a
product/service
in
the
field.
Typically
administered
as
a
large
scale
survey.
MODELING
Understanding
human
behaviors
and
represenFng
them
using
mathemaFcal
equaFons
in
order
to
drive
usability
and/or
saFsfacFon
associated
with
product/service.
CROSS
CHANNEL
ANALYTICS
Understanding
of
customer
behavior
or
customer
related
acFviFes
across
channels
like
web,
IVRU
&
call
center
in
addiFon
to
acFviFes
performed
by
agent
on
behalf
of
the
customer.
360
ANALYSIS
Complete
analysis
of
a
product
or
service
across
design
research,
surveys,
analyFcs
and
percepFon
informaFon
provided
customers
in
surveys.
13. 13
UX Research Methods
*Inspired
from
landscape
of
UX
research
methods
from
ChrisFan
Rohrer
What People Do
What People Say
Why & How
To Fix
Usability Testing - lab
Benchmark Testing - lab
User Production
Focus Groups
Interviews - phone
Intercept Surveys
Observational
Interviews – lab or field
Product Surveys
Web Analytics
Business Intelligence /
Data Mining
A/B Testing
How many &
How much
USER Field Surveys
hfp://www.nngroup.com/arFcles/which-‐ux-‐research-‐methods/
Concept Testing
Diary/Camera Studies
Ethnographic Field Studies
We
use
specific
methods
to
address
different
set
of
problems.
The
different
types
of
studies
provide
insights
and
help
us
in
understanding
issues
from
a
qualitaFve
and
a
quanFtaFve
standpoint.
14. 14
Research & Analytics
DESIGN
RESEARCH
Number
of
invesFgaFve
techniques
used
to
add
context
and
insight
to
the
design
process.
PRE-‐LAUNCH
ASSESSMENT
Assessing
the
risk
associated
with
the
launch
of
a
product
or
service
in
relaFonship
to
the
ease
of
use/usability
of
the
product/service.
POST
LAUNCH
ASSESSMENT
Understanding
the
performance
of
a
product/service
in
the
field.
Typically
administered
as
a
large
scale
survey.
MODELING
Understanding
human
behaviors
and
represenFng
them
using
mathemaFcal
equaFons
in
order
to
drive
usability
and/or
saFsfacFon
associated
with
product/service.
CROSS
CHANNEL
ANALYTICS
Understanding
of
customer
behavior
or
customer
related
acFviFes
across
channels
like
web,
IVRU
&
call
center
in
addiFon
to
acFviFes
performed
by
agent
on
behalf
of
the
customer.
360
ANALYSIS
Complete
analysis
of
a
product
or
service
across
design
research,
surveys,
analyFcs
and
percepFon
informaFon
provided
customers
in
surveys.
15. 15
Research + Analytics: Measurement Framework
User
Experience
Measurement
Framework
Loyalty
Trust
Delight
Usable
Usefulness
16. 16
Research + Analytics: Primary Scales
Usability
&
SaFsfacFon
The
SaFsfacFon
Scale
is
a
standardized
measure
of
how
well
a
service/product
meets
customer
needs.
It
is
calculated
from
a
12-‐item
amtudinal
survey
scored
on
a
Likert
scale.
The
System
Usability
Scale
(SUS)
is
a
standardized
measure
of
the
perceived
usability
of
a
system.
It
is
calculated
from
a
10-‐item
amtudinal
survey
scored
on
a
Likert
scale.
Exceed
Expecta:ons
Scores
among
the
top
10%
of
products
Below
Expecta:ons
Scores
among
the
bofom
40%
of
products
Meets
Expecta:ons
Scores
in
the
upper
40%
of
products
Way
Below
Expecta:ons
Scores
among
the
bofom
10%
of
products
10th
50th
90th
Percen:le
Percen:le
Percen:le
-‐4.0
-‐3.5
-‐3.
-‐2.5
-‐2.0
-‐1.
-‐1.0
-‐1.5
-‐0.5
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Meets
Expecta:ons
Way
Below
Expecta:ons
Below
Expecta:ons
Exceed
Expecta:ons
-‐1.3
1.3
0.0
17. From the dawn of
civilization until 2003,
humankind generated
five exabytes of data.
Now we produce five
exabytes every two
days…and the pace is
accelerating.
Eric Schmidt,
Google 2010
18. 18
BIG DATA
Three
V’s
of
Big
Data
Variety – Different types of
data elements, structured to
unstructured
Velocity – Batch, Real ;me,
streams etc.
Volume – Size in TB, tables,
transac;ons & records
VARIETY
VELOCITY
VOLUME
BIG
DATA
19. 19
Big Data Maturity
Where
we
are
to
where
we
are
going
Setup
40%
ReporFng
40%
Analysis
10%
Modeling
10%
Typical
Setup
10%
ReporFng
10%
Analysis
40%
Modeling
40%
Target
State
20. 20
Cross Channel Analytics Platform
Our
Big
Data
PlaSorm
CUSTOMER
ATTRIBUTES
IVR
CALL CENTER
DIGITAL PROPERTIES
• EASIER ACCESS TO DATA
• ROOT CAUSE/DRILL
DOWN ANALYSIS
• COHORT ANALYSIS
• PREDICTIVE MODELING
• TEXT ANALYTICS
CLOUD
SURVEY
WORK ORDERS
21. Big Data in 360 AnalysisUFlizing
data
from
qualitaFve,
quanFtaFve,
percepFon
&
behavioral
informaFon.
AnalyFcs
Surveys
Usability
TesFng
Unstructured
Data
360
Analysis
Customer
behaviors
through
our
digital
properFes,
machine
to
machine
interacFons,
back
end
system
logs,
Test
&
Target
Analy:cs
Surveys
conducted
through
our
digital
properFes
other
surveys
conducted
through
tradiFonal
means
Surveys
Lab
&
online
usability
tesFng,
upfront
design
research
Usability
Tes:ng
Customer
comments
through
different
sources,
notes
from
agents,
arFcle
feedback
Unstructured
Data
We
uFlize
data
collected
through
different
sources
to
create
a
comprehensive
view
of
insights
where
we
could
tell
a
story
around
the
why,
what
and
how
customers
feel
as
they
experience
our
products
or
services.
360
21
22. 22
Modeling - Basics
Capture:
The
rich
and
complex
elements
that
shape
an
experience.
Math:
Represent
the
experience
captured
through
mathemaFcal
models
Predict:
UFlize
the
models
to
predict
the
changes
in
the
experience
23. In
addiFon
to
my
team
here
at
Cox,
I
would
not
be
in
this
posiFon
without
my
modeling
gurus
Clyde
Heppner
&
Tuan
Tran.
My
sincerest
thanks
to
them
for
being
paFent
with
me
and
my
quesFons
as
well
as
sharing
the
wealth
of
experience.
Sarah
has
a
PhD
in
CogniFve
Sciences
from
Georgia
State
University,
with
a
background
in
memory
and
decision
making.
Sarah Cavrak
Megan’s
background
is
a
unique
blend
of
industrial
engineering,
staFsFcs,
and
psychology,
with
qualitaFve
and
quanFtaFve
analyFc
skills
in
industrial
and
educaFonal
semngs
.
Megan Lutz
Sheri
has
over
25
years
professional
experience
with
experFse
in
UX
and
product
research
and
strategy,
leading
top
performing
cross
funcFonal
teams
and
delivering
highly
successful
interacFve
product
soluFons
that
effecFvely
align
business
and
customer
needs
Sheri
Leslie
has
a
PhD
in
CogniFve
Sciences
from
Georgia
State
University,
with
a
background
in
memory
and
decision
making.
With
five
years
of
professional
experience
within
the
UX
field,
Leslie
has
experFse
in
qualitaFve
and
quanFtaFve
research
methodologies
Leslie
Jagan
has
over
13+
years
of
professional
experience,
with
a
background
in
decision
science,
data
warehousing,
reporFng,
predicFve
and
prescripFve
analyFcs.
Jagan
24. Feel free to say hi!
We are friendly and social
6305
B,
Peachtree
Dunwoody
Rd.
Atlanta,
GA
30338
404-‐234-‐0444
t.s.balaji@cox.com