UX Terms Flashcards
What is Thematic Analysis?
Thematic analysis is a qualitative data analysis method that involves reading through a data set (such as transcripts from in depth interviews or focus groups), and identifying patterns in meaning across the data.
Qualitative Data
Consisting of observational findings that identify design features easy or hard to use
- Based on opinions & experiences
- Interviews & observations
- Subjective
- Open-ended questions
- To generate hypothesis or develop ideas
- Answers the “why”
Quantitative Data
In form of one or more metrics (such as task completion rates or task times) that reflect whether the tasks were easy to perform
- Based numbers
- Statistical analysis
- Objective
- Closed-ended questions
- To validate hypothesis
- Answers the “what, where, how, when, and who”
UX benchmarking
Evaluating a product or service’s experience by using UX metrics to gauge its relative performance against a meaningful standard.
Allows us to assess & demonstrate the value of design work.
Can be used to calculate ROI.
UX metric
Numerical data that tells us something about the UX.
Examples: • Average time to make a purchase • Numbers of clicks on a Submit button • Success rate for an application completion • Average
summative evaluations
User research describes how a complete design performs.
Quantitative methods are often used
Benchmarking studies are summative evaluations
summative - I finished my soup, plated it, goes in front of a food critic, writes review, that is a summative review. project is complete. thing that I was forming is done.
formative evaluations
User research informs how the design will evolve, during the design process
Qualitative methods are often used
formative - tasting soup, do I need to modify my approach? how am I doing?
Quantitative methods for benchmarking
Quant usability testing
Analytics
Surveys
Quantitative methods for formative evaluations
A/B testing Tree testing Desirability testing Eyetracking Quant usability testing w/ prototypes
Quantitative usability testing
Participants perform tasks with a design, while researchers collect metrics that describe their performance
analytics
An analytics tool collects metrics that describe how people use a product in real life
Example: MATOMO
surveys
Users respond to questions about what they do or think
happiness
How do our users feel?
Measures of user attitudes or perceptions
Popular metrics:
• Satisfaction rating = How satisfied the participant is with the product
- Ease-of-use = How easy to use the task or product seems to the participant
- Perceived usability = How usable the product seems to the participant
- Subjective success rate = Whether or not the participant thinks they were successful
- Confidence rating = How confident the participant is in their task completion
- Questionnaire scores = Set of questions resulting in a score; like NPS, SUS, SUPR-Q
engagement
How frequently, deeply, or intensely do they use the product?
Level of user involvement
Popular metrics:
• Frequency of return = How often people return (for example: visits per user per week)
- Average time spent = Across all users, how much time is spent in the product
- Average number of sessions per user = Average number of times users return to the product
- Feature usage = How much people use a feature (for example, number of photos uploaded per user per day)
- Conversion rate = What percentage of visitors complete an important goal action (like a purchase/registration)
- Sales, orders, & subscriptions = Count of completed goal actions
adoption
How are we attracting new users?
Initial uptake of a product, service or feature
Popular metrics:
• New accounts/visitors = New people signing up or registering
- Conversion rate = What percentage of visitors complete an important goal action (like a purchase/registration)
- Sales, orders, & subscriptions = Count of completed goal actions
