User Centric, Inc. September 2006 1 Eyeballs, feature creep, pixels: Find out what your users are...

User Centric, Inc. September 2006 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 2006Washington, D.C.

Prepared by User Centric:Wendy Yee, Director, Ph.D.


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

Usability trends from the past couple yearsthat you can use

http://www.usercentric.com/UC/unece.html

Presentation available at:

Questions? [email protected]


Usability

Usability

Easy to learn

Efficient to use

Easy to remember

Prevents errors

Satisfying to use


Our baseline as information designers


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)

Our baseline…

human


People as information consumers in the Modern Age

Our baseline…

5 seconds

250 msec

100 msec

“something sort of like…?”

Time a user takes to decide whether a Web page or news article is worth reading

Time a user will pause on words or an object before continuing to look around

Time a user spends looking at an individual word when reading carefully

What a user will often say when you ask what he or she is looking for


How we 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)

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


Eye Tracking in the 1990’s


Source: Poynter Institute, 1991


Current Day Eye Tracking

Tested interface/stimuli

Tobii 1750 remote eye-tracking system and 17”monitor

Infrared cameras integrated into the monitor (track participant’s eye movements)

Participant

TEST ROOM


Moderator(administers tasks & controls the eye-tracking software)


What Real-Time Eye Tracking Looks Like



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?

52 AIM Express



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



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?



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?

Ave # fixations till correct click: 5

Redesigned homepage

Membership

Ave # fixations till correct click: 21

Existing homepage

Membership



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


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)


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)


General Observations: First Two Words and Paragraphs Matter


Source: Jakob Nielsen, F-Shaped Pattern For Reading Web Contenthttp://www.useit.com/alertbox/reading_pattern.html (April 17, 2006)

• 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.


User attention and feature creep


Excess Functionality is Distracting

User attention

• 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


Statistical Tools Do Not Translate Well to the Web

User attention

• 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


Multiple Query Criteria Can Be Very Distracting

User attention

• 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


Buried Query Tools Can Be Overlooked

User attention

• 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


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 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 attention

human


Data Filters Work Well When They Are Contextual

User attention

• 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.


Group Related Data to Allow Meaningful Comparisons

Source: The New York Times, June 5, 2006

• 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

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 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 attention


Leverage your users’ tendencies


Users Tend to… Adapt By…

1. 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

1. 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

2. Placing blurbs and links for the most frequently viewed and searched items near the upper left

3. 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.

Leverage users’ tendencies


6. Fixate on headlines and blurbs and spend more time on screens with blurbs

7. 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

6. 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.




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

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




User Centric, Inc.

2 Trans Am Plaza Dr. 105Oakbrook Terrace, IL

+1.630.376.1188www.usercentric.com

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User Centric, Inc. September 2006 1 Eyeballs, feature creep, pixels: Find out what your users are...

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