3. Business results depend on satisfying members
You are not your average member
Learning about members requires direct contact
Knowledge about members must be actionable
Decisions should be based on members
8. Personas lead to
better decisions
Personas for Design
Programs and conferences, interaction design,
visual design, content development, user testing
Personas for Marketing
Framework for marketing campaigns, branding,
messaging, market research
Personas for Strategy
Framework for business decisions,
offerings, channel usage, features
10. The Landscape of User Research and TestingUser Interviews
QUALITATIVE (INSIGHTS)
GOALS & ATTITUDES
(ASPIRATIONAL)
BEHAVIORS
(ACTUAL)
User Surveys
Usability Testing
Site Traffic/
Log File Analysis
Eye Tracking
Field Studies
(Contextual Inquiry)
Shadow Shopping
(Shop-Along)
Intercepts
Customer Support Data
Card Sorting
Focus Groups
Diary/Journal
Studies
Participatory Design
User Advisory
Panel
Automated Usability Testing
User Reports
Collages
QUANTITATIVE (VALIDATION)
A/B Testing
12. Member Interviews
• Cross-section of members, non-members, leadership, staff, other
audiences
• 6 town halls is a good starting point
• Informal, loosely structured conversations
13. User Interviews: Topics
• History and context
• Goals and behaviors
▫ Needs/triggers for usage, typical process, channel usage,
feature and content usage, gaps, wish list
• Attitudes and motivators
▫ Description of experience, likes/dislikes, influencers, psychological
drivers
• Opportunities
▫ Reaction to new ideas, features, content, improvements
• Observation of actual behavior (field studies, usability tests)
15. Personas
Understand how people will
actually use the site
Segmentation: Marketing vs. Personas
Marketing
Sell to people
Age
Income
Gender
Other
demographics
Goals
Behaviors
Attitudes
16. Segmentation by Goals
What are different people trying to do with OLA?
Hierarchy of Goals
Other goals:
• Read
• Learn
• Network
Be happy
Be independent
Grow your career
Understand process
Learn about points
Motivator
Motivator
Goal
Need
Task
17. Segmentation by Behaviors and Attitudes
How do users differ based on what they do or how they think?
Behaviors:
• Frequency of activity
• Frequency of visits to
the website
• Learning event attendance
including SC
• Division engagement for various
needs
• Use of competitors
Attitudes:
• Knowledge about
librarianship
• Motivators affecting users’
likelihood to
use and attend
• Perception of the OLA brand
• etc.
18. Segmentation by Behaviors and Attitudes
Explore different combinations
FrequencyofOLAactivity
Knowledge about librarianship
The risk-taker who thinks
he knows more than he
actually does
The novice who needs a
lot of guidance
The pro who wants to use
site tools and doesn’t
need help
The smart one who wants
validation of what she
already knows
19. Segmentation by Behaviors and Attitudes
Segmentation
Levelofpreparation
Desired level of personal interaction
Me
20. Segmentation: The Tests
Your segments should…
• Explain key differences you’ve
observed among members
• Be different enough from
each other
• Feel like real people
• Be described quickly
• Cover almost all users and
avoid edge cases
• Clearly affect decision making
24. Surveys: What
• Questions to gather data on segmentation attributes (dependent
variables)
▫ Goal for using OLA site, SC and programs.
Importance of each possible goal to the user
▫ Knowledge of librarianship (age/stage)
Age, stage, need
User’s self-perception of their expertise needs
• Questions to test the segmentation against
(independent variables)
▫ Other behaviors (site/channel usage, feature usage, etc.)
▫ Other attitudes (toward OLA, about self, etc.)
▫ New features and content to test
25. Surveys: What
Recommended order of questions:
• Current goals, usage, and behavior, including channel usage
• History with OLA and libraries
• User of or importance of existing features and content
• Satisfaction with existing OLA offerings
• Importance of new features and content
• Psychographic questions
• Demographic questions
26. Site Traffic Analysis
• Entry pages
• Referrers
• Exit pages
• Common paths
• Feature usage
• Search terms
• Conversion rate
• Duration
• Frequency
27. Quantitative Nirvana: Complete User Portrait
• Survey data
What the
user does
• Site traffic analysis
• CRM data
• Self-reported survey data
What the
user says
What the
user is
worth • CRM data
• Self-reported survey
data
42. Testing and Measuring Success
• QA process
• Usability testing
• Log files
• Survey
• Predictive modeling
43. Stephen Abram, MLS, FSLA
Consultant Dysart & Jones
Cel: 416-669-4855
Stephen.abram@gmail.com
Stephen’s Lighthouse Blog
http://stephenslighthouse.com
Facebook, Google+, LinkedIn: Stephen Abram
FourSquare, Pinterest, Tumblr: Stephen Abram
Twitter, Quora, Yelp, etc.: sabram
SlideShare: StephenAbram1
Thanks
Editor's Notes
Topics to be Explored:Teaching & learningOnline learning, changes in teaching, experiential learning, etc. TechnologyTop trendsDigitization & Digital mediaPublishing TrendsThe marketplace for educationAcademic research Scholarly communicationLearning spacesPhysical & virtual
