Usability <> Web Metrics ; Advancing analytics through the lessons of usability ...and improving user experience along the way

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    Usability <> Web Metrics ; Advancing analytics through the lessons of usability ...and improving user experience along the way - Presentation Transcript

    1. Human Factors Interna/onal UX
<>
AX
 Advancing
analy0cs
through
the
lessons
of
usability ...and
improving
user
experience
along
the
way 5

December
2008 David
Mahaffey Director
‐
Human
Factors
Interna2onal Kath
Straub,
PhD Chief
Scien2st
‐
Human
Factors
Interna2onal
    2. Analy0cs
is
just
data.
    3. 4
    4. NIH
types
know
a
lot
about
 how
to
handle
data.
    5. Observe Describe Explain Predict
    6. What
we’ve
learned
 doing
usability may
help
analy0cs
 move
faster

    7. Analy0cs
is
also
be
about
 what
can happen.
    8. Analy0cs
is
one
part of
a
bigger
picture.
    9. Measurable Business
Objec0ves Usability
 Web Tes0ng Analy0cs Best
Prac0ces Review 29
    10. How
did
we
get
here
....?
    11. UX



<>



AX
    12. UX



<>



AX AX
is
hard
    13. So
many
reports.
So
little
time
...
    14. So
many
reports.
So
little
actionable
information
... 29
    15. Sounding
familiar?
    16. Ins0tu0onaliza0on
of
Usability • Grassroots/Piecemeal effort • Tactical response • Train wreck • Notice of the champion • Organizational commitment • Recognized as skill • Common Method • Tools & Training 29
    17. How
do
we
go
there?
    18. Which
comes
first? The
data
or
the
ques2ons.
    19. Define the question(s) Ask: Who cares? Pick the data Collect and track Present just enough Present to drive action
    20. Measurable
site
goals?
    21. Conversion
does
not
 always
mean
purchase
...
    22. Analy0cs
and
usability
‐
similari0es
‐
Planning hEp://ins2tute.nih.gov/index hEp://ins2tute.nih.gov/grants hEp://ins2tute.nih.gov/grants/deadlines 
 Institute home Health Grants Research Health Pg 1 Deadlines Research Pg 1a Health Pg 2 Grants Pg 2 Research Pg 2 Grants Pg 3 Research Pg 3 Grants Pg 4 Research Pg 4 29
    23. Pain
points
... URL
naming
conven0on hEp://SiteHomepage/default.asp hEp://SiteHomepage/Default.asp hEp://SiteHomepage/default.aspx Institute home Health Grants Research Health Pg 1 Grants Pg 1 Research Pg 1a Health Pg 2 Grants Pg 2 Research Pg 2 Grants Pg 3 Research Pg 3 Grants Pg 4 Research Pg 4 29
    24. Some
Key
Performance
Indicators
    25. Visit
cycle Engage & Attract Commit Convert Persuade Unengaged Unconverted Unpersuaded (Bounce) 28
    26. 
“Typical”
Measures
    27. “Typical”
Measures Engage & Attract Commit Convert Persuade captured Visits
=
#
site
entries 30
    28. “Typical”
Measures Engage & Attract Commit Convert Persuade Bounce 31
    29. “Typical”
Measures Engage & Attract Commit Convert Persuade captured converted Tradi0onal
Conversion
=

Converted
/

Captured 32
    30. 
Some
addi0onal
measures
to
consider
....
    31. Addi0onal
Measures
‐
Engaged
Visits Engage & Attract Commit Convert Persuade engaged Engaged
Visits
=
#
site
visits
that
move
N*
pages
into
the
site 34
    32. Addi0onal
Measures
‐
Engagement
Index Engage & Attract Commit Convert Persuade engaged Engagement
Index
=
Engaged
visits*
/
Site
entries *Engaged
visits
are
site
visits
that
move
[1
to
N]
clicks
into
the
site
 35
    33. Addi0onal
Measures
‐
Persuaded
Visits Engage & Attract Commit Convert Persuade persuaded Persuaded
Visits*
=
#
site
visits
that
enter
a
conversion
funnel *Can
be
computed
for
individual
funnels
or
summed
over
funnels 36
    34. Addi0onal
Measures
‐
Persuasion
Index Engage & Attract Commit Convert Persuade persuaded Persuasion
Index*
=
Commieed
visits

/
Visits
to
PageX *This
is
a
page
level
measure
 37
    35. Addi0onal
Measures
‐
Engaged
conversions Engage & Attract Commit Convert Persuade engaged converted Engaged
Conversions
=
Converted
/
Engaged
visits 38
    36. Addi0onal
Measures
‐
Commieed
Conversion Engage & Attract Commit Convert Persuade persuaded converted Persuaded
Conversion*
=
Converted
/
Conversion
commitments *This
is
a
page
(sequence)
measure
 39
    37. Types
of
conversion Engage & Attract Commit Convert Persuade Persuaded
Conversion Engaged
Conversion Tradi0onal
Conversion 40
    38. What
are
we
measuring? Engage & Attract Commit Convert Persuade Persuasion Usability Conversion Engagement 41
    39. How
does
this
reflect
site
performance? Name awareness & SEO Effectiveness 25000 Content match Good labels Meaningful 20000 Content 15000 Usability 10000 5000 0 Visits Engaged Persuaded Converted
    40. Performance
ques0ons
&
measures Ques2on Measure Evaluates Brand
 How
many
visits
to
the
site? Visits Awareness,
SEO Engaged
Visits,
 Right
content,
 How
many
visits
move
past
the
first
page? Engagement
Index right
labels How
many
visits
move
into
a
“Conversion”
 Persuaded
Visits,
 Persuasion sequence? Persuasion
Index What
propor2on
of
engaged
visitors
 Engaged
 Persuasion
+
 convert? Conversions Usability What
propor2on
of
commiEed
visitors
 Persuaded
 Usability convert? Conversions 1st
Impression
 content
+
 What
propor2on
of
visits
convert? Conversions Persuasion
+
 Usability
    41. 
Analy0cs
that
drive
usability
improvements
    42. Contribu0on
Index
–
Across
key
tasks 1 +1 +1 +1 +1 17
    43. Contribu0on
Index
–
Across
key
tasks 1 +1 18
    44. Contribu0on
Index
–
Across
key
tasks 1 +1 +1 +1 +1 x2 +1 +1 +1 +1 +1 Same page 19
    45. Contribu0on
Index
–
Across
tasks =1 +1 +1 +1 = 4 1 x2 Same page 20
    46. Contribu0on
Index 
Which
page
/
pages
contribute
most
to
the
 • success
of
the
site? 
Iden2fy
key
“final”
pages • 
Where
people
off • •
Key
informa2on
pages
(Submission
dates;
Workshop
 desc2p2ons;
Ac2ve
Mechanisms) 
Look
at
common
paths
to
those
pages
(possibly
 • the
culminate
in
drop
off)
    47. Behavioral
Segmenta0on 24
    48. Behavioral
Segmenta0on Grant seeker • Funded work • Mechanism • Deadlines • Funding Strategy • Reviewers • Portfolio contact 24
    49. Behavioral
Segmenta0on 
Which
clusters
of
pages
do
people
tend
to
visit? • 
Do
the
clusters
match
 • up
with
personas? 
Which
addi2onal
pages
 • should
people
be
seeing
 to
accomplish
the
 persona
tasks?
    50. Behavioral
Segmenta0on
    51. Using
Analy0cs
to
Validate
User
Research Membership benefits Auto Car maintenance Insurance tools & tips Extended Research car warranty Auto Loan
    52. Using
Analy0cs
to
conduct
User
Research A
|
B
[|C]
Tes0ng Which
link
makes
sense
to
Rudy? Ruth
L.
Kirschstein
Na0onal
Research
Service
Award NRSA Predoctoral
Grant
    53. Performance
ques0ons
&
measures Ques0on Measure Evaluates Which
pages
are
most
cri2cal
to
site
 Contribu2on Central
content success? Seduc2ve
 Are
visitors
seeing
the
right
clusters
of
 Behavioral
 placement
of
 things? Segmenta2on associated
 content Which
labels
/
images
engage
best? A|B Testing Labeling
    54. Toward
integrated
user
experience
measures
    55. Measurable Business
Objec0ves Usability
 Web Tes0ng Analy0cs Best
Prac0ces Review 29
    56. The
web‐2‐ci0zen
rela0onship
is
expanding
    57. Speaker
Contact
Informa0on David
Mahaffey david.mahaffey@humanfactors.com Kath
Straub kath@humanfactors.com For more informa/on about HFI Ed
Frease Business
Director
‐
Government elf@humanfactors.com 800.242.4480

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