Is your design successful?
Usability Evaluation for Interaction Designs
First thing first — The golden rule in designing is You can be the designer but you are not the user
As a result, validating our design is critical to determining whether or not it would appeal to potential consumers. Any application’s success depends on its ability to validate its design. The goal of user interface assessment is to make products and services more helpful, understandable, and intuitive to users. We may design an assessment strategy and validate user interfaces using evaluation techniques and usability testing.
In this article, I am going to tell you about evaluation methodologies that users who are not directly involved. Those are,
1. Heuristic Evaluation
2. Walk-Throughs
3. Web analytics
4. A/B Testing
5. Predictive Models
#1. Heuristic evaluation
Heuristic evaluation is a process where experts use rules of thumb to measure the usability of user interfaces in independent walkthroughs and report issues. Evaluators use established heuristics and reveal insights that can help design teams enhance product usability from early in development.
What is a heuristic evaluation useful for?
A heuristic evaluation is useful for assessing the usability of a website, a landing page, a new design, or new functionality.
In 1990, web usability pioneers Jakob Nielsen and Rolf Molich published the landmark article “Improving a Human-Computer Dialogue”.The Nielsen-Molich heuristics state that a system should:
1. Keep users informed about its status appropriately and promptly.
2. Show information in ways users understand from how the real world operates, and in the users’ language.
3. Offer users control and let them undo errors easily.
4. Be consistent so users aren’t confused over what different words, icons, etc. mean.
5. Prevent errors — a system should either avoid conditions where errors arise. (e.g., “Are you sure you want to do this?” messages).
6. Have visible information, instructions, etc. to let users recognize options, actions, etc. instead of forcing them to rely on memory.
7. Be flexible so experienced users find faster ways to attain goals.
8. Have no clutter, containing only relevant information for current tasks.
9. Provide plain-language help regarding errors and solutions.
10. List concise steps in lean, searchable documentation for overcoming problems.
What are the advantages/limitations of a heuristic evaluation?
Advantages
- Helps to identify and fix usability issues
- 2. A relatively quick method of gathering website feedback
Limitations
1. If your evaluators aren’t impartial, bias can creep into the process.
2. Requires 5 evaluators to uncover 75% of usability issues
#2. Walk-Throughs
Walk-throughs offer an alternative approach to heuristic evaluation for predicting users’ problems without doing user testing. walk-throughs involve walking through a task with the product and noting problematic usability features.
Cognitive Walk-Throughs
Cognitive walk-throughs involve simulating how users go about problem-solving at each step in human-computer interaction. A cognitive walk-through, as the name implies, takes a cognitive perspective in which the focus is on evaluating designs for ease of learning — a focus that is motivated by observations that users learn by exploration.
The main steps involved in cognitive walk-throughs are as follows:
1. The characteristics of typical users are identified and documented, and sample tasks have been developed that focus on the aspects of the design to be evaluated.
2. A designer and one or more UX researchers come together to do the analysis.
3. The UX researchers walk through the action sequences for each task, placing it within the context of a typical scenario. As they do this, they try to answer the following
4. As the walk-through is being done, a record of critical information is compiled
5. The design is then revised to fix the problems presented. Before making the fix, insights derived from the walk-through are often checked by testing them with real users.
When doing a cognitive walk-through, it is important to document what works and what doesn’t. A standardized feedback form can be used in which answers are recorded to each question. Any negative answers are carefully documented on a separate form, along with details of the product. For example, how likely a problem is to occur, and how serious it will be for users.
1. Identify the user goal you want to examine.
2. Identify the tasks you must complete to accomplish that goal.
3. Document the experience while completing the tasks.
Pluralistic Walk-Throughs
Pluralistic walk-throughs are another type of well-established walk-through in which users, developers, and usability researchers work together to step through a task scenario. As they do this, they discuss usability issues associated with dialog elements involved in the scenario steps (Nielsen and Mack, 1994). In a pluralistic walk-through, each person is asked to assume the role of a typical user. Scenarios of use, consisting of a few prototype screens, are given to each person who writes down the sequence of actions that they would take to move from one screen to another, without conferring with each other.
The benefits of pluralistic walk-throughs include a strong focus on users’ tasks at a detailed level, that is, looking at the steps taken. This level of analysis can be invaluable for certain kinds of systems, such as safety-critical ones, where a usability problem identified for a single step could be critical to its safety or efficiency. Furthermore, only a limited number of scenarios, and hence paths through the interface, can usually be explored because of time constraints.
#3. Web analytics
What is web analytics?
Web analytics is the measurement and analysis of data to inform an understanding of user behavior across web pages. Analytics platforms measure activity and behavior on a website,
for example: how many users visit, how long they stay, how many pages they visit, which pages they visit, and whether they arrive by following a link or not.
Why web analytics is important
Website analytics provide insights and data that can be used to create a better user experience for website visitors. Understanding customer behavior is also key to optimizing a website for key conversion metrics.
For example, web analytics will show you the most popular pages on your website, and the most popular paths to purchase.
How web analytics work
Most analytics tools ‘tag’ their web pages by inserting a snippet of JavaScript in the web page’s code. Using this tag, the analytics tool counts each time the page gets a visitor or a click on a link. The tag can also gather other information like device, browser, and geographic location (via IP address).
Web analytics services may also use cookies to track individual sessions and to determine repeat visits from the same browser.
Since some users delete cookies, and browsers have various restrictions around code snippets, no analytics platform can claim full accuracy of their data and different tools sometimes produce slightly different results.
Sample web analytics data
Web analytics data is typically presented in dashboards that can be customized by user persona, date range, and other attributes. Data is broken down into categories, such as:
Audience data- number of visits, number of unique visitors, new vs. returning
Audience behavior- common landing pages, number of pages per visit, bounce rate
Campaign data- which campaigns drove the most traffic
Web analytics examples
The most popular web analytics tool is Google Analytics,
The following are some of the most commonly used tools:
Google Analytics — the ‘standard’ website analytics tool, free and widely used
Piwik — an open-source solution similar in functionality to Google.
Adobe Analytics — highly customizable analytics platform.
Kissmetrics — can zero in on individual behavior,
Mixpanel — advanced mobile, web analytics that measure actions than pageviews
Parse.ly — offers detailed real-time analytics, specifically for publishers
CrazyEgg — measures which parts of the page are getting the most attention using ‘heat mapping’
With a wide variety of analytics tools on the market, the right vendors for your company’s needs will depend on your specific requirements. Luckily, Optimizely integrates with most of the leading platforms to simplify your data analysis.
#4. A/B testing
What is A/B testing?
A/B testing (also known as split testing or bucket testing) is a method of comparing two versions of a webpage or app against each other to determine which one performs better.
How A/B testing works
In an A/B test, you take a webpage or app screen and modify it to create a second version of the same page. This change can be as simple as a single headline, button or be a complete redesign of the page. Then, half of your traffic is shown the original version of the page (known as the control), and half is shown the modified version of the page (the variation).
Why you should A/B test
A/B testing allows individuals, teams, and companies to make careful changes to their user experiences while collecting data on the results. This allows them to construct hypotheses and to learn why certain elements of their experiences impact user behavior.
This method of introducing changes to a user experience also allows the experience to be optimized.
A/B testing process
The following is an A/B testing framework you can use to start running tests:
- Collect data: Your analytics will often provide insight into where you can begin optimizing.
2. Identify goals: Your conversion goals are the metrics that you are using to determine whether or not the variation is more successful than the original version
3. Generate hypothesis: Once you’ve identified a goal you can begin generating A/B testing ideas and hypotheses for why you think they will be better than the current version.
4. Create variations: Using your A/B testing software, make the desired changes to an element of your website or mobile app experience.
5. Run experiment: visitors to your site or app will be randomly assigned to either the control or variation of your experience.
6. Analyze results: Once your experiment is complete, it’s time to analyze the results
A/B Testing Examples
These A/B testing examples show the types of results the world’s most innovative companies have seen through A/B testing with Optimizely:
Discovery A/B tested
ComScore A/B tested
Secret Escapes tested variations
#5. predictive models
What are predictive analytics models?
Predictive analytics is the most sought-after model in the industry. The economic value of predictive analytics is often talked about, but little attention is given to how they are developed and used. Predictive analytics models are designed to assess historical data, discover patterns, observe trends and draw up predictions.
Types of predictive models
Forecast models
handles metric value prediction by estimating the values of new data based on learnings from historical data.
For example, a call center can predict how many support calls they will get in a day or a shoe store can calculate inventory they need for the upcoming sales period using forecast analytics.
Classification models
work by categorizing information based on historical data
Outliers Models
While classification and forecast models work with historical data, the outliers model works with anomalous data entries within a dataset.
For example, when identifying a fraudulent transaction, the outlier model can assess the amount of money lost, location, purchase history, time, and nature of the purchase
Time series model
The time series model focuses on data where time is the input parameter.
For example, if a small business owner wants to measure sales for the past four quarters, then a Time Series model is needed.
.Clustering Model
The clustering model takes data and sorts it into different groups based on common attributes.
For example, marketers can divide a potential customer base based on common attributes
Predictive models in the future
The future will see predictive analytics models play an integral role in business processes because of the immense economic value they generate. While not perfect, the value they offer organizations, both public and private, is immense. With possibilities tangible with predictive analytics models, which is why they will be an intangible asset for the future.
Hope the article is useful to have an idea on usability evaluation for Interaction designs. Thank you very much for reading!
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Refferences
https://www.interaction-design.org/
2. Molich, R., and Nielsen, J. (1990). Improving a human-computer dialogue, Communications of the ACM 33, 3 (March), 338–348.
3. Student reference book2- Helen Sharp, Jenny Preece, Yvonne Rogers — Interaction Design_ Beyond Human-Computer Interaction-Wiley (2019)
