Eyeballs, Feature Creep, Pixels: Find out what your users are really doing

1. Eyeballs, feature creep, pixels: • Find out what your users are really doing • Prepared for: • UNECE Work Session on Statistical Dissemination and Communication • 12 September 2006 • Washington, D.C. • Prepared by User Centric: • Wendy Yee, Director, Ph.D. User Centric, Inc. September 2006

2. Usability trends from the past couple years that you can use Presentation available at: http://www.usercentric.com/UC/unece.html Questions?wyee@usercentric.com Outline • Our baseline as information designers • How we know where users are looking • What eye tracking tells us • User attention and feature creep • Leveraging your users’ tendencies for good User Centric, Inc. September 2006

3. Usability Usability Easy to learn Efficient to use Easy to remember Prevents errors Satisfying to use User Centric, Inc. September 2006

4. Our baseline as information designers User Centric, Inc. September 2006

5. human Our baseline… Users are… • Busy • Distracted • Prone to jumping to conclusions • Impatient • Sloppy readers • Link ideas in ways that we can’t predict • Prone to skipping ahead • Inconsistently pay attention • Creative • Sometimes illogical • Sometimes apply information they have learned to new contexts (a little information can be dangerous) User Centric, Inc. September 2006

6. Time a user takes to decide whether a Web page or news article is worth reading 5 seconds Time a user will pause on words or an object before continuing to look around 250 msec Time a user spends looking at an individual word when reading carefully 100 msec “something sort of like…?” What a user will often say when you ask what he or she is looking for Our baseline… People as information consumers in the Modern Age User Centric, Inc. September 2006

7. How we know where users are looking User Centric, Inc. September 2006

8. How to know where users are looking Why we need to know where users are looking • Discovering where users are looking (via eyetracking) helps us: • Gain insight into users’ cognitive processes • Collect more information than observable behavior (e.g., clicks) • Provides more objective information than user self-report • We use eye tracking in combination with other methods (usability testing, user interviews etc.) • Time on task and task error rate do not always tell the whole story • Eye movements help reveal the process, often not fully conscious, that led to these observable outcomes • On a per-design basis, eye tracking can help: • Assess decision making processes • Determine search strategies and user expectations • Explain ineffective or inefficient interaction (usability issues) User Centric, Inc. September 2006

9. How to know where users are looking About Eye Tracking • Eye tracker captures location of the eyes when user is looking at a stimulus • Most common type of eye movements  “Saccadic eye movements” • Saccadic eye movements consist of: • Saccades (“jumps”) • Fixations (“stops”) • Assumption: fixation = attention • Eye tracking should be used to support other usability methods such as usability testing and user interviews Source: Aga Bojko, User Centric User Centric, Inc. September 2006

10. How to know where users are looking Eye Tracking in the 1990’s Source: Poynter Institute, 1991 User Centric, Inc. September 2006

11. How to know where users are looking Current Day Eye Tracking TEST ROOM Tested interface/stimuli Tobii 1750 remote eye-tracking system and 17”monitor Infrared cameras integrated into the monitor (track participant’s eye movements) Moderator (administers tasks & controls the eye-tracking software) Participant User Centric, Inc. September 2006

12. How to know where users are looking What Real-Time Eye Tracking Looks Like User Centric, Inc. September 2006

13. 52 How to know where users are looking ET Uses: Explain Ineffective or Inefficient Interaction • Eye tracking can help explain why certain usability problems occurred. • Example • Very few users were able to chat on AIM without downloading it • Did they not see the “AIM Express” link? • Eye movements revealed that users saw it. They must have not known what the label meant. • Packaging example • Why do users not know what’s in the box? Do they miss the info or do they read it, but without comprehending? AIM Express User Centric, Inc. September 2006

14. How to know where users are looking ET Uses: Determine Search Strategies and User Expectations • If there is a specific target, how do users look for it? • Where did they look first/second? • Helps determine whether placement of elements matches user expectations and if not, where they should be moved. • Example • Task: Purchase a gift card • User expects the link to be either on top of the page or at the bottom Gift Card User Centric, Inc. September 2006

15. How to know where users are looking ET Uses: Assess Decision Making Processes • When multiple relevant targets are present, how do users decide where to click? • Online search example • Which search results are considered prior to the click? • Which elements of a result matter the most? (Title? Description? URL?) • Online retailer example • Task: Purchase a gift for … on … .com • Which options do users consider and which do they miss/ignore before deciding where to click? User Centric, Inc. September 2006

16. Redesigned homepage Membership Ave # fixations till correct click: 5 How to know where users are looking ET Uses: Compare Search Efficiency • Homepage example • Does the redesign help users find important information more efficiently? • Tasks involving key targets • Measures: # errors, time, # fixations, fixation duration, distribution of fixations • If A is better than B in task X, why? • Better target location/graphic treatment? • Less competition from other elements? • Clearer target label? • Drug labels examples • Which of the label templates supports the most efficient search for key information (e.g., dosage strength)? • How do our labels compare to the competitors’ labels in terms of search accuracy and efficiency? Existing homepage Membership Ave # fixations till correct click: 21 User Centric, Inc. September 2006

17. What eye tracking tells us User Centric, Inc. September 2006

18. What eye tracking tells us General Observations: Satisficing • Users’ eyes jump around the screen looking for information that is “about right” • Test participants often are looking for a specific phrase or “something that jumps out at me” • They know what they’re looking for, even if they cannot verbalize it • If they don’t see something that works, they will keep looking until they find a piece of information (or a link) that looks “about right” in the hopes that it will get them closer to where they need to be User Centric, Inc. September 2006

19. What eye tracking tells us General Observations: Hunting and Gathering on Home Pages • Users generally focus on the upper left area of a home page • In a 2004 study of 46 participants published by the Poynter Institute, users most often fixated first in the upper left of the page, then hovered in that area before going left to right • Only after fixating on the upper part of the home page for a while were users likely to explore further down the page Source: Eyetrack III study, The Poynter Institute, http://www.poynterextra.org/eyetrack2004/main.htm (2004) User Centric, Inc. September 2006

20. What eye tracking tells us General Observations: Writing for the Web • Partial reading of headlines and blurbs is common • Users often only look at the left one-third of the blurb. Most people seem to look at the first couple of words -- and only read on if they are engaged by those words. • Using blurbs raises overall reader attention on a Web page • Users spend more time reading (fixating) on Web pages with headlines and blurbs (compared to Web pages with just headlines) Source: Eyetrack III study, The Poynter Institute, http://www.poynterextra.org/eyetrack2004/main.htm (2004) User Centric, Inc. September 2006

21. What eye tracking tells us General Observations: First Two Words and Paragraphs Matter • Users do not read in a word-by-word manner on top level screens -- especially when users are looking for a specific topic or comparing items. • The first two paragraphs must state the most important information, especially the first paragraph (which is most likely to be read). • Load the beginning of headers, paragraphs, and bullet points with information-carrying words that users will notice when scanning down the left side of the screen. They are much more likely to read the first two words than the third word in a line. Source: Jakob Nielsen, F-Shaped Pattern For Reading Web Content http://www.useit.com/alertbox/reading_pattern.html (April 17, 2006) User Centric, Inc. September 2006

22. User attention and feature creep User Centric, Inc. September 2006

23. User attention Excess Functionality is Distracting • Usability tests over the last several years have clearly indicated that the more features an application or Web site display at a time, the more difficult it is for users to choose among them. • Eye tracking data supports this: • When many similar items or features appear together, a user tends to be distracted and look at more things before focusing on a particular item • Uncertain or multiple focal points reduce user understanding and efficiency • Everything becomes a choice – users take longer and have a more difficult time making a decision when there are many similar items to choose among User Centric, Inc. September 2006

24. User attention Statistical Tools Do Not Translate Well to the Web • Nearly all statistical applications require training and domain-specific knowledge • They have a high learning curve and are often intended for use by specialists • Their high level of functionality works against the casual user, which means that tools based on the statistical approaches often are too “heavy” for Web users • Focal point: None SPSS Screenshot User Centric, Inc. September 2006

25. User attention Multiple Query Criteria Can Be Very Distracting • Many query-driven interfaces try to do too many things at once • Different types of queries, different conditions, different sort criteria • What does the user do if they do not recognize the terminology used by the query? • Focal point: Multiple header and fields User Centric, Inc. September 2006

26. User attention Buried Query Tools Can Be Overlooked • On high-visibility sites which routinely offer statistical information, most users (up to 80%) rarely filter the information or customize their view of it • Focal point: The default table in the lower part of the screen • User awareness of the search box is reduced significantly • It is unclear which data sets the user is selecting • Controls that offer 45 combinations for filtering statistical data overwhelm the user • It is also visually unclear how changing the filters impacts the data that is displayed User Centric, Inc. September 2006

27. User attention Multiple Data Sets Need Summaries to Encourage Analysis • Data sets that display ordered data can be simple but powerful tools for facilitating multi-way comparisons • Simple comparison methods such as scrolling windows can be helpful • However, they can also be overwhelming without a summary to aid in the interpretation • Do these lists tell you who is a better tennis player? Or what their strengths and weaknesses are? • Focal point: Multiple User Centric, Inc. September 2006

28. human User attention Ways to Improve User Focus and Reduce Feature Creep • Provide a context for the data • Highlight the “active” filter in a dynamic data presentation whenever possible • Facilitate meaningful comparisons between data points by annotating the data with related facts to provide perspective • Provide the punchline in the context of your data points. This helps users who are in a hurry and encourages review and exploration by users who are not as rushed. • Recognize that many users require a context – they want a story told to them User Centric, Inc. September 2006

29. User attention Data Filters Work Well When They Are Contextual • In contrast, other high-traffic sites offering statistical data focus on the most common types of filtering that their users want. • They provide controls for filtering that are contextually imbedded with the data and encourage exploration. • Focal point: Yellow column • Data sets are already broken out (with clear titles). • Selecting a column header dynamically filters the enter data set and causes the player list to change. • The entire column is highlighted yellow, which helps the user recognize which filter is active. User Centric, Inc. September 2006

30. User attention Group Related Data to Allow Meaningful Comparisons • A summary precedes the data, which helps orient the user • Bar graphs facilitate quick visual comparisons • Specific taxpayer headcounts indicate the size of the affected population • Focal point: Key data highlighted by a color change and black callout box Source: The New York Times, June 5, 2006 User Centric, Inc. September 2006

31. User attention Group Related Data to Allow Meaningful Comparisons • Size of circles allows readers to quickly grasp the relative differences • Saturation of background shade indicates market share • Juxtaposition with a map makes it easier for users to recognize the regions this airline serves – and the impact on their travel plans • Focal point: Large dark circles representing hubs Source: The New York Times, August 22, 2005 User Centric, Inc. September 2006

32. User attention Facilitate Accurate Interpretation of Data • Many users tend not to make accurate on-the-fly comparisons or realize magnitude scales • In addition to indicating the estimated risk to specific populations, this box points out the fold increase to help users understand the magnitude of the change Source: The New York Times, January 2, 2005 User Centric, Inc. September 2006

33. Leverage your users’ tendencies User Centric, Inc. September 2006

34. Leverage users’ tendencies Users Tend to… Adapt By… • Giving them immediate access to information by facilitating scanning: • Use familiar (to users) labels • Create thematic groups and use headers • Use short paragraphs and bullet lists • Placing blurbs and links for the most frequently viewed and searched items near the upper left • Eliminating guesswork by removing ambiguity and overlap from your groups, headers, titles, and blurbs 4. Always summarizing findings on top-level screens. The first two paragraphs of an article or report summary should contain key facts. • Be in a hurry 2. Focus on the upper left quadrant of screens 3. Look for information that is “about right” (satisficing) 4. Not read until they reach a place where they think they’ve arrived and need to start reading detailed information User Centric, Inc. September 2006

35. Leverage users’ tendencies Users Tend to… Adapt By… • Providing meaningful and concise blurbs on home pages and/or top-level screens. Avoid headlines without blurbs (even a 4 word blurb helps clarify your intent). 7. Carefully editing “headlines” and blurbs so that the first couple of words focus on the key finding. Remember, if the user sees words that potentially match what they are looking for, they are more likely to keep reading the entire headline and/or blurb. 8. Providing a limited set of filters in the context that they will most likely be used. Integrate the filters with the data so that the relationships between filter and data are self-evident. Highlight active filters in the data. • Fixate on headlines and blurbs and spend more time on screens with blurbs • Read the first two words of “headlines” or blurbs and then often drop away 8. Not play with filters and query tools unless they understand what the tools will do – or they have a compelling reason User Centric, Inc. September 2006

36. Leverage users’ tendencies Users Tend to… Adapt By… 9. Not assuming that the data will speak for itself. Instead, point out the key data points and explain why they are important in the context of all the data. • Group related data together • Use visual presentations • Summarize the overall findings • Point out key data • Provide context for why key data is important • Provide the punchline immediately adjacent to the data 10. Doing the basic math for the user and showing them the impact of the data • Logical progression is left to right when moving from basic data to relevance and context 9. Not draw conceptual associations or make accurate interpretation points unless specific relationships between data are indicated 10. Not accurately realize the scale of magnitude User Centric, Inc. September 2006

37. T h a n k Y o u User Centric, Inc. 2 Trans Am Plaza Dr. 105Oakbrook Terrace, IL +1.630.376.1188www.usercentric.com User Centric, Inc. September 2006