SlideShare ist ein Scribd-Unternehmen logo
1 von 27
USER INSIGHTS, DATA DRIVEN DESIGN, AND
STAKEHOLDER BUY IN.
Matthew Martin
@atomaton
Christina Spencer
@c_m_spencer
#euroia
A Case Study in Mobile Strategy
JSTOR powers the research and learning
of 6 million users each month.
JSTOR
9300 2Kjournals
130,154,067
content accesses per year
2,000,000+
search requests per day
9million articles
2
million plant
specimens
institutions
170
countries
31Kbooks
@atomaton @c_m_spencer
INSTITUTIONAL
PARTICIPATION
U.S. &
CANADA
4974
LATIN AMERICA
& CARIBBEAN
728
EUROPE
2509
ASIA, AUSTRALIA
& NEW ZEALAND
1598
MIDDLE EAST,
INDIA & AFRICA
1886
Where do we begin?
Assertion worksheet
In Context Mobile Interviews
Qualitative Log Analysis
Experience Mapping
MOBILE
STRATEGY
@atomaton @c_m_spencer
A Problem well stated is a
problem half solved”
Dependencies
Impacts
Benefits
Risks
Gaps
Data
ASSERTION
WORKSHEET
“
@atomaton @c_m_spencer
Example
@atomaton @c_m_spencer
We require a deeper understanding of
the existing mobile landscape and how
current JSTOR users are interacting
with jstor.org across devices.
@atomaton @c_m_spencer
Digital
Natives
Digital
Natives
Digital
Natives
Digital
Natives
Jstor.org Mobile Usage
WHAT DOES THIS MEAN?
“We’re not like
Facebook! This
doesn’t apply to us”
“They would use
JSTOR on mobile. We
need to enhance our
mobile experience”
“Students don’t do
REAL research on
phones”
@atomaton @c_m_spencer
Goal:
Understand our current mobile users.
How do they use jstor.org via mobile
devices and how do these activities fit
into their larger workflows
Methods:
1. In Context Mobile Interviews
2. Qualitative Log Analysis
@atomaton @c_m_spencer
IN CONTEXT MOBILE INTERVIEWS
INTERCEPT SURVEY
Participants were recruited
live on jstor.org via intercept
survey
1.Those that opted in were contacted
within 30 minutes by phone for a 10
minute interview.
INTERVIEW QUESTIONS
@atomaton @c_m_spencer
Qualitative Log Analsysis:
In depth analysis of a single users actions
and workflow.
@atomaton @c_m_spencer
In Depth Analysis of a
single users actions
and workflow.
QUALITATIVE
LOG ANALYSIS
@atomaton @c_m_spencer
Location:
Mobile usage
while in
proximity of
a computer
THEMES IN
MOBILE USAGE
@atomaton @c_m_spencer
Combination
of computer
and mobile
usage
Re-Searching
EXPERIENCE MAP
a model of how people experience a:
• Product
• Service
• Environment
• Computer system
The activity of mapping builds shared
knowledge and consensus across teams
and stakeholders
@atomaton @c_m_spencer
PublishInformation Need AccessFind/Discover Consume &
Comprehend
Print
Find
1. Execute Query
2. Review Results
3. Refine Query
Analyze & Validate Collect & Organize Make
Re-Write
Institution Proxy
Purchase Funnel
Login/Register
Formulate Query
Need recognized
and accepted
Event
Assignment
Discover:
Serendipity Annotate
Discuss
Differentiate
Verify
Upload
Share
Tag
Save
Monitor
Ingest Deliver
Ingest Deliver
Ingest Deliver
Ingest Deliver
A
B
C
P
Download
Read
Compose
Review
Edit
"I want to know
how to cite work"
"I want to share what I
found"
"How do I copy and
paste from the content
on this site"
"What are others
doing on the platform"
"Is there related
content?"
"What article is more
relevant than the next"
"How did I get here, this
looks interesting"
"Am I going in the
right direction?"
"I want immediate
access!!"
I am going to leave
if I have to wait."
Uncertainty Optimism ConfusionFrustationDoubt Clarity Senseof Direction/ Confidence Satisfactionor Disappointment
STAGESACTIONS/TOUCHPOINTS
DEVICE
PRIORITYTHOUGHTSFEELINGS
Device Priority
Stage
Action/Touchpoints
Thoughts
Feelings
“The work is great, very fast moving, I
don’t get bored by wondering what to do
next. Plus the constant supply of food
makes it even more fun!” — QA
Implement approaches
that are technology
and device agnostic
and give users control
of where, when, and
how they interact with
our content and
servies
@atomaton @c_m_spencer
@atomaton @c_m_spencer
Matthew Martin is an
Experience Architect with
over 10 years of practice
knowledge designing for
multiple devices, websites,
and software within waterfall
and Agile working
environments
As a User Researcher
Christina Spencer, employing
a wide range of methods
enhancing understanding of
users, and the context in
which the products and
services of ITHAKA are
relevant in their lives.
@atomaton@c_m_spencer

Weitere ähnliche Inhalte

Was ist angesagt?

Combining Methods: Web Analytics and User Research
Combining Methods: Web Analytics and User ResearchCombining Methods: Web Analytics and User Research
Combining Methods: Web Analytics and User ResearchUser Intelligence
 
Qualitative vs quantitative survey questions
Qualitative vs quantitative survey questionsQualitative vs quantitative survey questions
Qualitative vs quantitative survey questionsPollfish
 
Feedback Should Not Be An Echo Chamber
Feedback Should Not Be An Echo ChamberFeedback Should Not Be An Echo Chamber
Feedback Should Not Be An Echo ChamberTom Grant
 
OpsStars 2019 - Using Customer Journeys to Supercharge your Revenue Engine R...
OpsStars 2019 - Using Customer Journeys to Supercharge your Revenue Engine  R...OpsStars 2019 - Using Customer Journeys to Supercharge your Revenue Engine  R...
OpsStars 2019 - Using Customer Journeys to Supercharge your Revenue Engine R...Christine Crandell
 
Measuring Content Effectiveness
Measuring Content EffectivenessMeasuring Content Effectiveness
Measuring Content EffectivenessAndrea L. Ames
 
Iterative Discovery and Analysis: Workflow / Activity and Capability Model
Iterative Discovery and Analysis: Workflow / Activity and Capability ModelIterative Discovery and Analysis: Workflow / Activity and Capability Model
Iterative Discovery and Analysis: Workflow / Activity and Capability ModelJoe Lamantia
 
Fairness in Search & RecSys 네이버 검색 콜로키움 김진영
Fairness in Search & RecSys 네이버 검색 콜로키움 김진영Fairness in Search & RecSys 네이버 검색 콜로키움 김진영
Fairness in Search & RecSys 네이버 검색 콜로키움 김진영Jin Young Kim
 
How to Analyze Survey Data
How to Analyze Survey Data How to Analyze Survey Data
How to Analyze Survey Data QuestionPro
 
SAS Customer Decision Hub: migliora l’engagement con i tuoi clienti analizzan...
SAS Customer Decision Hub: migliora l’engagement con i tuoi clienti analizzan...SAS Customer Decision Hub: migliora l’engagement con i tuoi clienti analizzan...
SAS Customer Decision Hub: migliora l’engagement con i tuoi clienti analizzan...SAS Italy
 
Maxdiff webinar_10_19_10
 Maxdiff webinar_10_19_10 Maxdiff webinar_10_19_10
Maxdiff webinar_10_19_10QuestionPro
 
Practical Explainable AI: How to build trustworthy, transparent and unbiased ...
Practical Explainable AI: How to build trustworthy, transparent and unbiased ...Practical Explainable AI: How to build trustworthy, transparent and unbiased ...
Practical Explainable AI: How to build trustworthy, transparent and unbiased ...Raheel Ahmad
 
Taming the Data Lake with Scalable Metrics Model Framework
Taming the Data Lake with Scalable Metrics Model FrameworkTaming the Data Lake with Scalable Metrics Model Framework
Taming the Data Lake with Scalable Metrics Model FrameworkRamkumar Ravichandran
 
Revelation Influencer Studies -- Discovering B2B Customer Opinions
Revelation Influencer Studies -- Discovering B2B Customer OpinionsRevelation Influencer Studies -- Discovering B2B Customer Opinions
Revelation Influencer Studies -- Discovering B2B Customer OpinionsTom Brown ✪STRATEGY RESEARCH
 
UCSF Life Sciences Week 1 Therapeutics
UCSF Life Sciences Week 1 TherapeuticsUCSF Life Sciences Week 1 Therapeutics
UCSF Life Sciences Week 1 TherapeuticsStanford University
 
Digital APAC- Predictive Analytics
Digital APAC- Predictive AnalyticsDigital APAC- Predictive Analytics
Digital APAC- Predictive AnalyticsKaviChaurasia
 
The IoT Academy training part3 AI model
The IoT Academy training part3 AI modelThe IoT Academy training part3 AI model
The IoT Academy training part3 AI modelThe IOT Academy
 

Was ist angesagt? (20)

Combining Methods: Web Analytics and User Research
Combining Methods: Web Analytics and User ResearchCombining Methods: Web Analytics and User Research
Combining Methods: Web Analytics and User Research
 
Selling Text Analytics to your boss
Selling Text Analytics to your bossSelling Text Analytics to your boss
Selling Text Analytics to your boss
 
Qualitative vs quantitative survey questions
Qualitative vs quantitative survey questionsQualitative vs quantitative survey questions
Qualitative vs quantitative survey questions
 
Feedback Should Not Be An Echo Chamber
Feedback Should Not Be An Echo ChamberFeedback Should Not Be An Echo Chamber
Feedback Should Not Be An Echo Chamber
 
OpsStars 2019 - Using Customer Journeys to Supercharge your Revenue Engine R...
OpsStars 2019 - Using Customer Journeys to Supercharge your Revenue Engine  R...OpsStars 2019 - Using Customer Journeys to Supercharge your Revenue Engine  R...
OpsStars 2019 - Using Customer Journeys to Supercharge your Revenue Engine R...
 
Actionability of insights
Actionability of insights Actionability of insights
Actionability of insights
 
Measuring Content Effectiveness
Measuring Content EffectivenessMeasuring Content Effectiveness
Measuring Content Effectiveness
 
Iterative Discovery and Analysis: Workflow / Activity and Capability Model
Iterative Discovery and Analysis: Workflow / Activity and Capability ModelIterative Discovery and Analysis: Workflow / Activity and Capability Model
Iterative Discovery and Analysis: Workflow / Activity and Capability Model
 
Fairness in Search & RecSys 네이버 검색 콜로키움 김진영
Fairness in Search & RecSys 네이버 검색 콜로키움 김진영Fairness in Search & RecSys 네이버 검색 콜로키움 김진영
Fairness in Search & RecSys 네이버 검색 콜로키움 김진영
 
How to Analyze Survey Data
How to Analyze Survey Data How to Analyze Survey Data
How to Analyze Survey Data
 
SAS Customer Decision Hub: migliora l’engagement con i tuoi clienti analizzan...
SAS Customer Decision Hub: migliora l’engagement con i tuoi clienti analizzan...SAS Customer Decision Hub: migliora l’engagement con i tuoi clienti analizzan...
SAS Customer Decision Hub: migliora l’engagement con i tuoi clienti analizzan...
 
900 keynote abbott
900 keynote abbott900 keynote abbott
900 keynote abbott
 
Maxdiff webinar_10_19_10
 Maxdiff webinar_10_19_10 Maxdiff webinar_10_19_10
Maxdiff webinar_10_19_10
 
Practical Explainable AI: How to build trustworthy, transparent and unbiased ...
Practical Explainable AI: How to build trustworthy, transparent and unbiased ...Practical Explainable AI: How to build trustworthy, transparent and unbiased ...
Practical Explainable AI: How to build trustworthy, transparent and unbiased ...
 
Taming the Data Lake with Scalable Metrics Model Framework
Taming the Data Lake with Scalable Metrics Model FrameworkTaming the Data Lake with Scalable Metrics Model Framework
Taming the Data Lake with Scalable Metrics Model Framework
 
Revelation Influencer Studies -- Discovering B2B Customer Opinions
Revelation Influencer Studies -- Discovering B2B Customer OpinionsRevelation Influencer Studies -- Discovering B2B Customer Opinions
Revelation Influencer Studies -- Discovering B2B Customer Opinions
 
UCSF Life Sciences Week 1 Therapeutics
UCSF Life Sciences Week 1 TherapeuticsUCSF Life Sciences Week 1 Therapeutics
UCSF Life Sciences Week 1 Therapeutics
 
Digital APAC- Predictive Analytics
Digital APAC- Predictive AnalyticsDigital APAC- Predictive Analytics
Digital APAC- Predictive Analytics
 
The IoT Academy training part3 AI model
The IoT Academy training part3 AI modelThe IoT Academy training part3 AI model
The IoT Academy training part3 AI model
 
Ai in Society
Ai in SocietyAi in Society
Ai in Society
 

Andere mochten auch

Arma Presentation 2008.10.16
Arma Presentation 2008.10.16Arma Presentation 2008.10.16
Arma Presentation 2008.10.16naffeldt
 
User Checks - Agile Usability Testing
User Checks - Agile Usability TestingUser Checks - Agile Usability Testing
User Checks - Agile Usability TestingAnouschka Scholten
 
Optimize Digital Marketing Success with Your Site Launch or Redesign
Optimize Digital Marketing Success with Your Site Launch or RedesignOptimize Digital Marketing Success with Your Site Launch or Redesign
Optimize Digital Marketing Success with Your Site Launch or RedesignPerficient, Inc.
 
Data Driven Design
Data Driven DesignData Driven Design
Data Driven DesignTanya M.
 
Chris Dayley Searchlove Boston - Powerful A/B Testing User Insights
Chris Dayley Searchlove Boston - Powerful A/B Testing User InsightsChris Dayley Searchlove Boston - Powerful A/B Testing User Insights
Chris Dayley Searchlove Boston - Powerful A/B Testing User InsightsChris Dayley
 
The five slash
The five slashThe five slash
The five slashbetterfly
 

Andere mochten auch (6)

Arma Presentation 2008.10.16
Arma Presentation 2008.10.16Arma Presentation 2008.10.16
Arma Presentation 2008.10.16
 
User Checks - Agile Usability Testing
User Checks - Agile Usability TestingUser Checks - Agile Usability Testing
User Checks - Agile Usability Testing
 
Optimize Digital Marketing Success with Your Site Launch or Redesign
Optimize Digital Marketing Success with Your Site Launch or RedesignOptimize Digital Marketing Success with Your Site Launch or Redesign
Optimize Digital Marketing Success with Your Site Launch or Redesign
 
Data Driven Design
Data Driven DesignData Driven Design
Data Driven Design
 
Chris Dayley Searchlove Boston - Powerful A/B Testing User Insights
Chris Dayley Searchlove Boston - Powerful A/B Testing User InsightsChris Dayley Searchlove Boston - Powerful A/B Testing User Insights
Chris Dayley Searchlove Boston - Powerful A/B Testing User Insights
 
The five slash
The five slashThe five slash
The five slash
 

Ähnlich wie User Insights, Data Driven Design, and Stakeholder Buy In

Measuring Relevance in the Negative Space
Measuring Relevance in the Negative SpaceMeasuring Relevance in the Negative Space
Measuring Relevance in the Negative SpaceTrey Grainger
 
An Introduction to Usability
An Introduction to UsabilityAn Introduction to Usability
An Introduction to Usabilitydirk.swart
 
12.10.14 Slides, “The SHARE Notification Service”
12.10.14 Slides, “The SHARE Notification Service”12.10.14 Slides, “The SHARE Notification Service”
12.10.14 Slides, “The SHARE Notification Service”DuraSpace
 
Social media solution for s hinsei bank in japan
Social media solution for s hinsei bank in japanSocial media solution for s hinsei bank in japan
Social media solution for s hinsei bank in japanChetan Goenka
 
How to evaluate the whole web (without being Google)
How to evaluate the whole web (without being Google)How to evaluate the whole web (without being Google)
How to evaluate the whole web (without being Google)Dixon Jones
 
(Web User Interfaces track) "Getting the Query Right: User Interface Design o...
(Web User Interfaces track) "Getting the Query Right: User Interface Design o...(Web User Interfaces track) "Getting the Query Right: User Interface Design o...
(Web User Interfaces track) "Getting the Query Right: User Interface Design o...icwe2015
 
Understanding Products Driven by Machine Learning and AI: A Data Scientist's ...
Understanding Products Driven by Machine Learning and AI: A Data Scientist's ...Understanding Products Driven by Machine Learning and AI: A Data Scientist's ...
Understanding Products Driven by Machine Learning and AI: A Data Scientist's ...Amanda Casari
 
Applications for Social Networking Strategies in an Agency Context
Applications for Social Networking Strategies in an Agency ContextApplications for Social Networking Strategies in an Agency Context
Applications for Social Networking Strategies in an Agency ContextJohn Brisbin
 
Practical Machine Ethics @ SXSW2019
Practical Machine Ethics @ SXSW2019Practical Machine Ethics @ SXSW2019
Practical Machine Ethics @ SXSW2019Jesus Ramos
 
Analytics in business
Analytics in businessAnalytics in business
Analytics in businessNiko Vuokko
 
IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS A scientometric analysis of cloud c...
IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS A scientometric analysis of cloud c...IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS A scientometric analysis of cloud c...
IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS A scientometric analysis of cloud c...IEEEMEMTECHSTUDENTPROJECTS
 
Presentation for ArenaLviv 2017
Presentation for ArenaLviv 2017Presentation for ArenaLviv 2017
Presentation for ArenaLviv 2017Andriy Dyadyura
 
Descubrimiento de Insights a través de Text Mining: cómo y para qué analizar ...
Descubrimiento de Insights a través de Text Mining: cómo y para qué analizar ...Descubrimiento de Insights a través de Text Mining: cómo y para qué analizar ...
Descubrimiento de Insights a través de Text Mining: cómo y para qué analizar ...Big-Data-Summit
 
The path to be a data scientist
The path to be a data scientistThe path to be a data scientist
The path to be a data scientistPoo Kuan Hoong
 
Websites are a symptom, not the cause
Websites are a symptom, not the causeWebsites are a symptom, not the cause
Websites are a symptom, not the causeSally Lait
 
Four Types of Data Analytics.pdf
Four Types of Data Analytics.pdfFour Types of Data Analytics.pdf
Four Types of Data Analytics.pdfJeniferJenkins2
 
Juliette Melton - Mobile User Experience Research
Juliette Melton - Mobile User Experience ResearchJuliette Melton - Mobile User Experience Research
Juliette Melton - Mobile User Experience ResearchWeb Directions
 
EDW 2015 cognitive computing panel session
EDW 2015 cognitive computing panel session EDW 2015 cognitive computing panel session
EDW 2015 cognitive computing panel session Steve Ardire
 

Ähnlich wie User Insights, Data Driven Design, and Stakeholder Buy In (20)

Measuring Relevance in the Negative Space
Measuring Relevance in the Negative SpaceMeasuring Relevance in the Negative Space
Measuring Relevance in the Negative Space
 
An Introduction to Usability
An Introduction to UsabilityAn Introduction to Usability
An Introduction to Usability
 
12.10.14 Slides, “The SHARE Notification Service”
12.10.14 Slides, “The SHARE Notification Service”12.10.14 Slides, “The SHARE Notification Service”
12.10.14 Slides, “The SHARE Notification Service”
 
on-the-horizon
on-the-horizonon-the-horizon
on-the-horizon
 
Social media solution for s hinsei bank in japan
Social media solution for s hinsei bank in japanSocial media solution for s hinsei bank in japan
Social media solution for s hinsei bank in japan
 
How to evaluate the whole web (without being Google)
How to evaluate the whole web (without being Google)How to evaluate the whole web (without being Google)
How to evaluate the whole web (without being Google)
 
(Web User Interfaces track) "Getting the Query Right: User Interface Design o...
(Web User Interfaces track) "Getting the Query Right: User Interface Design o...(Web User Interfaces track) "Getting the Query Right: User Interface Design o...
(Web User Interfaces track) "Getting the Query Right: User Interface Design o...
 
Understanding Products Driven by Machine Learning and AI: A Data Scientist's ...
Understanding Products Driven by Machine Learning and AI: A Data Scientist's ...Understanding Products Driven by Machine Learning and AI: A Data Scientist's ...
Understanding Products Driven by Machine Learning and AI: A Data Scientist's ...
 
Applications for Social Networking Strategies in an Agency Context
Applications for Social Networking Strategies in an Agency ContextApplications for Social Networking Strategies in an Agency Context
Applications for Social Networking Strategies in an Agency Context
 
Practical Machine Ethics @ SXSW2019
Practical Machine Ethics @ SXSW2019Practical Machine Ethics @ SXSW2019
Practical Machine Ethics @ SXSW2019
 
Analytics in business
Analytics in businessAnalytics in business
Analytics in business
 
IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS A scientometric analysis of cloud c...
IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS A scientometric analysis of cloud c...IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS A scientometric analysis of cloud c...
IEEE 2014 DOTNET CLOUD COMPUTING PROJECTS A scientometric analysis of cloud c...
 
Presentation for ArenaLviv 2017
Presentation for ArenaLviv 2017Presentation for ArenaLviv 2017
Presentation for ArenaLviv 2017
 
Descubrimiento de Insights a través de Text Mining: cómo y para qué analizar ...
Descubrimiento de Insights a través de Text Mining: cómo y para qué analizar ...Descubrimiento de Insights a través de Text Mining: cómo y para qué analizar ...
Descubrimiento de Insights a través de Text Mining: cómo y para qué analizar ...
 
Seminar
SeminarSeminar
Seminar
 
The path to be a data scientist
The path to be a data scientistThe path to be a data scientist
The path to be a data scientist
 
Websites are a symptom, not the cause
Websites are a symptom, not the causeWebsites are a symptom, not the cause
Websites are a symptom, not the cause
 
Four Types of Data Analytics.pdf
Four Types of Data Analytics.pdfFour Types of Data Analytics.pdf
Four Types of Data Analytics.pdf
 
Juliette Melton - Mobile User Experience Research
Juliette Melton - Mobile User Experience ResearchJuliette Melton - Mobile User Experience Research
Juliette Melton - Mobile User Experience Research
 
EDW 2015 cognitive computing panel session
EDW 2015 cognitive computing panel session EDW 2015 cognitive computing panel session
EDW 2015 cognitive computing panel session
 

User Insights, Data Driven Design, and Stakeholder Buy In

  • 1. USER INSIGHTS, DATA DRIVEN DESIGN, AND STAKEHOLDER BUY IN. Matthew Martin @atomaton Christina Spencer @c_m_spencer #euroia A Case Study in Mobile Strategy
  • 2. JSTOR powers the research and learning of 6 million users each month. JSTOR 9300 2Kjournals 130,154,067 content accesses per year 2,000,000+ search requests per day 9million articles 2 million plant specimens institutions 170 countries 31Kbooks @atomaton @c_m_spencer
  • 3. INSTITUTIONAL PARTICIPATION U.S. & CANADA 4974 LATIN AMERICA & CARIBBEAN 728 EUROPE 2509 ASIA, AUSTRALIA & NEW ZEALAND 1598 MIDDLE EAST, INDIA & AFRICA 1886
  • 4. Where do we begin? Assertion worksheet In Context Mobile Interviews Qualitative Log Analysis Experience Mapping MOBILE STRATEGY @atomaton @c_m_spencer
  • 5. A Problem well stated is a problem half solved” Dependencies Impacts Benefits Risks Gaps Data ASSERTION WORKSHEET “ @atomaton @c_m_spencer
  • 7. We require a deeper understanding of the existing mobile landscape and how current JSTOR users are interacting with jstor.org across devices. @atomaton @c_m_spencer
  • 13. WHAT DOES THIS MEAN? “We’re not like Facebook! This doesn’t apply to us” “They would use JSTOR on mobile. We need to enhance our mobile experience” “Students don’t do REAL research on phones” @atomaton @c_m_spencer
  • 14. Goal: Understand our current mobile users. How do they use jstor.org via mobile devices and how do these activities fit into their larger workflows Methods: 1. In Context Mobile Interviews 2. Qualitative Log Analysis @atomaton @c_m_spencer
  • 15. IN CONTEXT MOBILE INTERVIEWS INTERCEPT SURVEY Participants were recruited live on jstor.org via intercept survey 1.Those that opted in were contacted within 30 minutes by phone for a 10 minute interview.
  • 17.
  • 18. Qualitative Log Analsysis: In depth analysis of a single users actions and workflow. @atomaton @c_m_spencer
  • 19. In Depth Analysis of a single users actions and workflow. QUALITATIVE LOG ANALYSIS @atomaton @c_m_spencer
  • 20. Location: Mobile usage while in proximity of a computer THEMES IN MOBILE USAGE @atomaton @c_m_spencer Combination of computer and mobile usage Re-Searching
  • 21. EXPERIENCE MAP a model of how people experience a: • Product • Service • Environment • Computer system
  • 22. The activity of mapping builds shared knowledge and consensus across teams and stakeholders @atomaton @c_m_spencer
  • 23. PublishInformation Need AccessFind/Discover Consume & Comprehend Print Find 1. Execute Query 2. Review Results 3. Refine Query Analyze & Validate Collect & Organize Make Re-Write Institution Proxy Purchase Funnel Login/Register Formulate Query Need recognized and accepted Event Assignment Discover: Serendipity Annotate Discuss Differentiate Verify Upload Share Tag Save Monitor Ingest Deliver Ingest Deliver Ingest Deliver Ingest Deliver A B C P Download Read Compose Review Edit "I want to know how to cite work" "I want to share what I found" "How do I copy and paste from the content on this site" "What are others doing on the platform" "Is there related content?" "What article is more relevant than the next" "How did I get here, this looks interesting" "Am I going in the right direction?" "I want immediate access!!" I am going to leave if I have to wait." Uncertainty Optimism ConfusionFrustationDoubt Clarity Senseof Direction/ Confidence Satisfactionor Disappointment STAGESACTIONS/TOUCHPOINTS DEVICE PRIORITYTHOUGHTSFEELINGS
  • 25. “The work is great, very fast moving, I don’t get bored by wondering what to do next. Plus the constant supply of food makes it even more fun!” — QA Implement approaches that are technology and device agnostic and give users control of where, when, and how they interact with our content and servies @atomaton @c_m_spencer
  • 27. Matthew Martin is an Experience Architect with over 10 years of practice knowledge designing for multiple devices, websites, and software within waterfall and Agile working environments As a User Researcher Christina Spencer, employing a wide range of methods enhancing understanding of users, and the context in which the products and services of ITHAKA are relevant in their lives. @atomaton@c_m_spencer

Hinweis der Redaktion

  1. Data. External: Digital Native CHRIS
  2. Data. External: Digital Native CHRIS
  3. Data. External: Digital Native CHRIS
  4. Data. External: Digital Native CHRIS
  5. Data. Internal: Big Data/ Usage Analytics Conflicting data CHRIS
  6. There were lots of opinions about what this meant. In reality we had no good data to back up any of these opinion, which is what lead us to conduct a round of research on our current mobile users. No data: including stakeholders. He needed data to get everyone on the same pg.
  7. In Context Interviews. Visual Example CHRIS Value of contacting these individuals in the context of their mobile usage. Memory fades quickly- if I ask you what websites you visited on your phone last Thursday and where you were, how accurate could you be?
  8. Prompt: The results of these interviews were used in combination with the insights from the Qualitative Log Analysis.
  9. Qualitative Log Analysis Visual Example CHRIS