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@pnoreault #LavaCon
Improve the UX of Your Content and
Prove It
Pam Noreault – ACI Worldwide
Tara Knapp – ACI Worldwide
Contact us
Tara Knapp
Manager, Information Development
ACI Worldwide
tara.knapp@aciworldwide.com
taraknapp@gmail.com
@tara...
Journey
• Beginning
• Imagine - What could be
• Our road
• Content quality
• (Re)evolution
• What’s stopping you?
Beginnin
g
Imagine!
Customers are your biggest advocates.
Where is your content on a scale of 1 to
3?
1 = Hot Mess 2 = Partial Mess 3 = Utopia
Methodology before
Methodology now – inching upwards
Personas
(product level)
User Research and Analysis
(release level)
*usage patterns
Info...
How we select content to fix
1. Select deliverable
2. Select content to uplift
Before model After
models
Before overview
After overview
Before topic
After topic
Road led us here – BUT prove the changes made a
difference
Contextual overviews
Concise/clear content
Reduced
content/e...
How many of you are doing content validation with
customers?
Validation methodology
Model for PDF Documents
• Uplifted four documents
Methodology
• Teams of 4-7 writers per document
•...
Validation protocol
Directions
• Think aloud
• Tell us when you have completed the task or you give up
Test Protocol
• Eac...
Sample task
You are a operations manager put in charge of monitoring
the system.
Task: Use the user guide to determine you...
?
Percentage of tasks completed correctly
0%
20%
40%
60%
80%
100%
Document 1 Document 2 Document 3 Document 4
Old Doc New Doc
Results – Document 1 (Average before & after)
Simple 1 1.5 2 2.5 3 3.5 4 4.5 5 Complex
Effective 1 1.5 2 2.5 3 3.5 4 4.5 5...
Results – Document 2 (Average before & after)
Simple 1 1.5 2 2.5 3 3.5 4 4.5 5 Complex
Effective 1 1.5 2 2.5 3 3.5 4 4.5 5...
Results – Document 3 (Average before & after)
Simple 1 1.5 2 2.5 3 3.5 4 4.5 5 Complex
Effective 1 1.5 2 2.5 3 3.5 4 4.5 5...
Results – Document 4 (Average before & after)
Simple 1 1.5 2 2.5 3 3.5 4 4.5 5 Complex
Effective 1 1.5 2 2.5 3 3.5 4 4.5 5...
Average overall ranking – 1 poor to 7 great
0
1
2
3
4
5
6
7
Document 1 Document 2 Document 3 Document 4
Old Doc New Doc
• Content located faster in 3 of 4 new models.
• Validation tasks completed with increased
success in 2 of 4 new models.
•...
Laugh and cry moments
• Surfing & browsing
• Clueless & perfectionist
• Change haters
• Fear of failure
• When is done rea...
Customers are their biggest advocates.
What the writers learned
Lessons the writers came up with
• Do a dry run
• Observing the users’ choices can be as
useful as the data
• Cannot predi...
1. We require user research and content validation, where
appropriate.
2. We get creative…..
• Collaborate with people who...
What are you waiting for?
Pam Noreault
• pam.noreault@aciworldwide.com
• pamnoreault@gmail.com
• @pnoreault (Twitter)
• Pam Noreault (LinkedIn)
Tara...
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Improve the UX of Your Content and Prove It

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LavaCon 2015 presentation by Tara Knapp and myself about our UX methodology and the content validation journey.

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Improve the UX of Your Content and Prove It

  1. 1. @pnoreault #LavaCon Improve the UX of Your Content and Prove It Pam Noreault – ACI Worldwide Tara Knapp – ACI Worldwide
  2. 2. Contact us Tara Knapp Manager, Information Development ACI Worldwide tara.knapp@aciworldwide.com taraknapp@gmail.com @tara_knapp (Twitter) Tara Knapp (LinkedIn) Pam Noreault SR Manager, Information Development ACI Worldwide pam.noreault@aciworldwide.com pamnoreault@gmail.com @pnoreault (Twitter) Pam Noreault (LinkedIn)
  3. 3. Journey • Beginning • Imagine - What could be • Our road • Content quality • (Re)evolution • What’s stopping you?
  4. 4. Beginnin g
  5. 5. Imagine! Customers are your biggest advocates.
  6. 6. Where is your content on a scale of 1 to 3? 1 = Hot Mess 2 = Partial Mess 3 = Utopia
  7. 7. Methodology before
  8. 8. Methodology now – inching upwards Personas (product level) User Research and Analysis (release level) *usage patterns Information Model (release level) User Stories User-Centered Content Information Model (deliverable) Concise writing Topic-based writing Task-based writing Writing for translation Writing for accessibility Validation Testing
  9. 9. How we select content to fix 1. Select deliverable 2. Select content to uplift
  10. 10. Before model After models
  11. 11. Before overview After overview
  12. 12. Before topic After topic
  13. 13. Road led us here – BUT prove the changes made a difference Contextual overviews Concise/clear content Reduced content/eliminated clicks Topic-based (text scanning) Accessibility – checklist of fixes Translation – checklist of fixes
  14. 14. How many of you are doing content validation with customers?
  15. 15. Validation methodology Model for PDF Documents • Uplifted four documents Methodology • Teams of 4-7 writers per document • Tested each document with at least 2 users from 2 customers • Tested 1 hour via WebEx • 4 tasks tested on each doc • Presented 2 docs – old + new • Order of docs alternated
  16. 16. Validation protocol Directions • Think aloud • Tell us when you have completed the task or you give up Test Protocol • Each task was timed. • Each task was completed successfully/unsuccessfully. Testers could give up. • After all tasks were completed on one doc, testers rated the content. Scale: from 1 to 5. • After all tasks were completed for both docs, testers rated their overall experience. Scale: from 1 (poor) to 7 (great). • Data recorded in a Google form • Sessions recorded with permission
  17. 17. Sample task You are a operations manager put in charge of monitoring the system. Task: Use the user guide to determine your two areas of responsibility in terms of configuration.
  18. 18. ?
  19. 19. Percentage of tasks completed correctly 0% 20% 40% 60% 80% 100% Document 1 Document 2 Document 3 Document 4 Old Doc New Doc
  20. 20. Results – Document 1 (Average before & after) Simple 1 1.5 2 2.5 3 3.5 4 4.5 5 Complex Effective 1 1.5 2 2.5 3 3.5 4 4.5 5 Ineffective Clean 1 1.5 2 2.5 3 3.5 4 4.5 5 Confusing Clear 1 1.5 2 2.5 3 3.5 4 4.5 5 Cluttered Valuable 1 1.5 2 2.5 3 3.5 4 4.5 5 Not Valuable Simple 1 1.5 2 2.5 3 3.5 4 4.5 5 Complex Effective 1 1.5 2 2.5 3 3.5 4 4.5 5 Ineffective Clean 1 1.5 2 2.5 3 3.5 4 4.5 5 Confusing Clear 1 1.5 2 2.5 3 3.5 4 4.5 5 Cluttered Valuable 1 1.5 2 2.5 3 3.5 4 4.5 5 Not Valuable
  21. 21. Results – Document 2 (Average before & after) Simple 1 1.5 2 2.5 3 3.5 4 4.5 5 Complex Effective 1 1.5 2 2.5 3 3.5 4 4.5 5 Ineffective Clean 1 1.5 2 2.5 3 3.5 4 4.5 5 Confusing Clear 1 1.5 2 2.5 3 3.5 4 4.5 5 Cluttered Valuable 1 1.5 2 2.5 3 3.5 4 4.5 5 Not Valuable Simple 1 1.5 2 2.5 3 3.5 4 4.5 5 Complex Effective 1 1.5 2 2.5 3 3.5 4 4.5 5 Ineffective Clean 1 1.5 2 2.5 3 3.5 4 4.5 5 Confusing Clear 1 1.5 2 2.5 3 3.5 4 4.5 5 Cluttered Valuable 1 1.5 2 2.5 3 3.5 4 4.5 5 Not Valuable
  22. 22. Results – Document 3 (Average before & after) Simple 1 1.5 2 2.5 3 3.5 4 4.5 5 Complex Effective 1 1.5 2 2.5 3 3.5 4 4.5 5 Ineffective Clean 1 1.5 2 2.5 3 3.5 4 4.5 5 Confusing Clear 1 1.5 2 2.5 3 3.5 4 4.5 5 Cluttered Valuable 1 1.5 2 2.5 3 3.5 4 4.5 5 Not Valuable Simple 1 1.5 2 2.5 3 3.5 4 4.5 5 Complex Effective 1 1.5 2 2.5 3 3.5 4 4.5 5 Ineffective Clean 1 1.5 2 2.5 3 3.5 4 4.5 5 Confusing Clear 1 1.5 2 2.5 3 3.5 4 4.5 5 Cluttered Valuable 1 1.5 2 2.5 3 3.5 4 4.5 5 Not Valuable
  23. 23. Results – Document 4 (Average before & after) Simple 1 1.5 2 2.5 3 3.5 4 4.5 5 Complex Effective 1 1.5 2 2.5 3 3.5 4 4.5 5 Ineffective Clean 1 1.5 2 2.5 3 3.5 4 4.5 5 Confusing Clear 1 1.5 2 2.5 3 3.5 4 4.5 5 Cluttered Valuable 1 1.5 2 2.5 3 3.5 4 4.5 5 Not Valuable Simple 1 1.5 2 2.5 3 3.5 4 4.5 5 Complex Effective 1 1.5 2 2.5 3 3.5 4 4.5 5 Ineffective Clean 1 1.5 2 2.5 3 3.5 4 4.5 5 Confusing Clear 1 1.5 2 2.5 3 3.5 4 4.5 5 Cluttered Valuable 1 1.5 2 2.5 3 3.5 4 4.5 5 Not Valuable
  24. 24. Average overall ranking – 1 poor to 7 great 0 1 2 3 4 5 6 7 Document 1 Document 2 Document 3 Document 4 Old Doc New Doc
  25. 25. • Content located faster in 3 of 4 new models. • Validation tasks completed with increased success in 2 of 4 new models. • Content rating higher in 3 of 4 new models. • Overall content ranking higher in 2 of 4 new models.
  26. 26. Laugh and cry moments • Surfing & browsing • Clueless & perfectionist • Change haters • Fear of failure • When is done really done • Aha moments • Technical snafus • Testing heavy-duty reference content was a bust
  27. 27. Customers are their biggest advocates. What the writers learned
  28. 28. Lessons the writers came up with • Do a dry run • Observing the users’ choices can be as useful as the data • Cannot predict how users will do the tasks • Define what “done” means • Reference-based content should not be tested with the same methodology as task- based content • Rebooting your computer prior to testing has its benefits • Repeated contact with customers removes the fear factor over time
  29. 29. 1. We require user research and content validation, where appropriate. 2. We get creative….. • Collaborate with people who work with customers • Monitor and mine data from social networking sites • Join LinkedIn groups - ask questions & post surveys • Seek input from people who represent the same personas as our users • Participate in customer-focus groups (Design Partner Programs) Re(evolution)
  30. 30. What are you waiting for?
  31. 31. Pam Noreault • pam.noreault@aciworldwide.com • pamnoreault@gmail.com • @pnoreault (Twitter) • Pam Noreault (LinkedIn) Tara Knapp • tara.knapp@aciworldwide.com • taraknapp@gmail.com • @tara_knapp (Twitter) • Tara Knapp (LinkedIn)

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