Interaction Design Evaluation Methods
Hello everyone!
In this article, I’m going to talk about the methods that are used to evaluate and interaction design. These included heuristic evaluation, walk-throughs, web analytics, A/B testing, and predictive models. So, Let’s start to learn what are these models and why we need them.
Heuristic Evaluation
A heuristic evaluation is a usability review methodology for computer software that helps to spot usability issues within the user interface(UI) style. Heuristic Evaluation is a technique derived by the Nielson Norman group to assess the usability of a digital product. This is usually performed by a set of usability experts who reviews a product against the set of thumb rules derived from the Norman group. These thumb rules sometimes are revised by the usability engineers to accommodate more findings.
The finest way to grade a product’s user experience or usability is by user testing it, which although consumes more resource produce the best results. User feedbacks are pricey and interpreting them is also time-consuming and not all user-centered products have such freedom to these resources. In such cases, the Heuristic Evaluation of your product helps in minimizing the usability problems with much lower consumption of your limited resources.
The Heuristic Principles
1. Visibility of system status
The user should always be made aware of the system’s status at all times with efficient feedback interactions.
2. Match between system and the real world
The system’s communication with the user must be familiar to the user. The user should be able to relate it with their real-world equivalent.
3. User control and freedom
Users should have the control to revert back their actions with the freedom to exit the system when they wish to, at all times.
4. Consistency and standards
The system must follow standard interface conventions with user familiar terminologies that accommodate the same meaning all over the system.
5. Error prevention
Humans are bound to make errors, and the system should support the user in eliminating them.
6. Recognition rather than recall
User assistance must be provided by the system to reduce the user’s memory load.
7. Flexibility and efficiency of use
The systems must prove efficient for both novice and experienced users. Accelerators/ Shortcut commands for the Experienced and obvious alternatives for the novice users.
8. Aesthetic and minimalist design
The user should be presented only with relevant data. More the relevant data easier it is for the system to acquire user focus.
9. Help users recognize, diagnose, and recover from errors
The system must help the user in recovering from errors. Error messages should be constructed with empathy.
10. Help and documentation
Users should be provided with appropriate help documents both online and offline about the system. The documentation must deliver effective steps for the users to accomplish their goals.
To learn and understand more about usability heuristics for user interface design click here.
Though Heuristics will never replace the conventional user testing strategies it still may deliver tons of a few products ’ broken usability and experience. Not every product manager/designer will afford the time, money, or effort to perform user testing and Heuristic evaluations could be the faster yet effective way to solve them.
Walk-Throughs
Walkthrough in software testing is used to review documents with peers, managers, and fellow team members who are guided by the author of the document to gather feedback and reach a consensus. A walkthrough can be pre-planned or organized based on the needs. Generally, people working on the same work product are involved in the walkthrough process.
How do software walkthroughs help users?
The software tour is like having an experienced guide sit next to the new user and show them how to use the application. Even the best-designed software can be difficult to master at first. A good product tour can help novice users feel like experts.
What is Walkthrough Software?
Walkthrough software allows you to create interactive walkthroughs without having to code and program them yourself. They work as a layer that sits on top of any web-based application.
Walkthrough software typically delivers content in one of three ways:
- Loading a javascript in the header of your application,
- Through a browser extension, or
- Via an API.
Most walkthrough techniques do not involve users. There are main two walk-through methods called Cognitive walk-throughs and Pluralistic walk-throughs.
Cognitive Walk-Throughs
Cognitive walkthroughs involve simulating a user’s problem-solving process at each step in the human-computer dialogue, checking to see if the user’s goals and memory for actions can be assumed to lead to the next correct action.
Pluralistic Walk-Throughs
Pluralistic walkthroughs are another type of walkthrough in which users, developers, and usability experts work together to step through a scenario, discussing usability issues associated with dialogue elements involved in the scenario steps.
Web Analytics
Web analytics is a form of interaction logging that was specifically created to analyze users' activity on websites so that designers could modify their designs to attract and retain customers.
Using web analytics, web designers and developers can trace the activity of the users who visit their website. They can see how many people came to the site, how many stayed and for how long, and which pages they visited. They can also find out about where the users came from and much more. Web analytics is therefore a powerful evaluation tool for web designers that can be used on its own or in conjunction with other types of evaluations, particularly user testing. For instance, web analytics can provide a “big-picture” overview of user interaction on a website, whereas user testing with a few typical users can reveal details about UX design problems that need to be fixed.
Benefits of web analytics.
- Determine the returning customers.
- Personalize the website to customers who visit it again and again.
- Monitor the amount of money individual customers or specific groups of customers spend.
- Observe the geographic regions of site visitors and customers.
- Predict the best-selling products.
Web analytics can strongly support qualitative research and testing findings. Some best practices to keep in mind related to this field are:
Encourage a data-driven environment for decision-making. After collecting the relevant data to answer whether you have met (or fail to meet) your goals, find out what you can do to improve your KPIs. Is there high-value content (based on user feedback to the website) that is not getting any traffic? Find out why through user path analysis or engagement analysis of top sources for that page. Leverage the experimentation & testing tools to try out different solutions and find the best placement that generates the most engagement for that page.
Avoid only providing traffic reports. Reporting about visits, page views, top sources, or top pages only skims the surface. Large numbers can be misleading; just because there is more traffic or time spent on site doesn’t mean that there is a success. Reporting these numbers is largely tactical; after all, what do 7 million visits have to do with the success of your program?
Always provide insights with the data. Reporting metrics to your stakeholders with no insights or tie-ins to your business or user goals misses the point. Make the data relevant and meaningful by demonstrating how the website data shows areas of success and of improvement on your site.
Avoid being snapshot-focused in reporting. Focusing on visits or looking only within a specific time period doesn’t capture the richer and more complex web experiences that are happening online now. Pan-session metrics, such as visitors, user-lifetime value, and other values that provide a longer-term understanding of people and users, allow you to evaluate how your website has been doing as it matures and as it interacts with visitors, especially the returning ones.
Communicate clearly with stakeholders. Be consistent in the information you provide, know your audience, and know the weaknesses of your system, and disclose them to your stakeholders.
The most popular web analytics tool is Google Analytics, although there are many others on the market offering specialized information such as real-time activity or heat mapping.
A/B Testing
An A/B test, also known as a split test, is an experiment for determining which of different variations of an online experience performs better by presenting each version to users at random and analyzing the results. So, what is A/B testing? A/B testing demonstrates the efficacy of potential changes, enabling data-driven decisions and ensuring positive impacts.
Benefits of A/B Testing
1. Improved user engagement
Elements of a page, app, ad, or email that can be A/B tested include the headline or subject line, imagery, call-to-action (CTA) forms and language, layout, fonts, and colors, among others. Testing one change at a time will show which affected users’ behavior and which did not. Updating the experience with the “winning” changes will improve the user experience in aggregate, ultimately optimizing it for success.
2. Improved content
Testing ad copy, for instance, requires a list of potential improvements to show users. The very process of creating, considering, and evaluating these lists winnows out ineffective language and makes the final versions better for users.
3. Reduced bounce rates
A/B testing points to the combination of elements that helps keep visitors on-site or app longer. The more time visitors spend on site, the likelier they’ll discover the value of the content, ultimately leading to a conversion.
4. Increased conversion rates
A/B testing is the simplest and most effective means to determine the best content to convert visits into sign-ups and purchases. Knowing what works and what doesn’t helps convert more leads.
5. Higher conversion values
The learnings from A/B testing successfully applied to one experience can be applied to additional experiences, including pages for higher-priced products and services. Better engagement on these pages will demonstrate similar lifts in conversions.
6. Ease of analysis
Determining a winner and a loser of an A/B test is straightforward: which page’s or app’s metrics come closer to its goals (time spent, conversions, etc.,).
And while testing services have evolved to include statistical analysis for users of all levels of spreadsheet expertise, the numbers for a comparison of two experiences are underwhelming in their complexity. The clarity of these stats also undermines the highest-paid person’s opinion (HIPPO) that may otherwise be overvalued.
7. Quick results
Even a relatively small sample size in an A/B test can provide significant, actionable results as to which changes are most engaging for users. This allows for short-order optimization of new sites, new apps, and low-converting pages.
8. Everything is testable
Forms, images, and text are typical items for A/B testing and updating, but any element of a page or app can be tweaked and tested. Headline styling, CTA button colors, form length, etc., can all affect user engagement and conversion rates in ways that may never be known if they’re not tested. No idea need be rejected on a conference call; testing and metrics, not emotions, prove what works and what doesn’t.
9. Reduced risks
By A/B testing, commitments to costly, time-intensive changes that are proven ineffective can be avoided. Major decisions can be well-informed, avoiding mistakes that could otherwise tie up resources for minimum or negative gain.
10. Reduced cart abandonment
For e-commerce, getting a user to follow through with checkout after clicking “buy” on an item is a significant challenge, as most potential customers abandon their carts before paying. A/B testing can help find the optimal combination of tweaks to the order pages that will get users to the finish.
11. Increased sales
Any and all of the above-mentioned A/B testing benefits serve to increase sales volume. Beyond the initial sales boost optimized changes produce, testing provides better user experiences which, in turn, breeds trust in the brand, creating loyal, repeat customers and, therefore, increased sales.
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. It helps to begin with high traffic areas of your site or app to allow you to gather data faster. Look for pages with low conversion rates or high drop-off rates that can be improved.
- 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. Goals can be anything from clicking a button or link to product purchases and e-mail signups.
- 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. Once you have a list of ideas, prioritize them in terms of expected impact and difficulty of implementation.
- Create variations: Using your A/B testing software (like Optimizely), make the desired changes to an element of your website or mobile app experience. This might be changing the color of a button, swapping the order of elements on the page, hiding navigation elements, or something entirely custom. Many leading A/B testing tools have a visual editor that will make these changes easy. Make sure to QA your experiment to make sure it works as expected.
- Run experiment: Kick off your experiment and wait for visitors to participate! At this point, visitors to your site or app will be randomly assigned to either the control or variation of your experience. Their interaction with each experience is measured, counted and compared to determine how each performs.
- Analyze results: Once your experiment is complete, it’s time to analyze the results. Your A/B testing software will present the data from the experiment and show you the difference between how the two versions of your page performed and whether there is a statistically significant difference.
Predictive Models
Predictive modeling uses statistical techniques to predict future user behaviors. Predictive Modeling solutions are analyzing historical and current data and generating a model to help predict future outcomes.
Top 5 Types of Predictive Models
- Classification model: Considered the simplest model, it categorizes data for simple and direct query responses. An example use case would be to answer the question “Is this a fraudulent transaction?”
- Clustering model: This model nests data together by common attributes. It works by grouping things or people with shared characteristics or behaviors and plans strategies for each group at a larger scale. An example is in determining credit risk for a loan applicant based on what other people in the same or a similar situation did in the past.
- Forecast model: This is a very popular model, and it works on anything with a numerical value based on learning from historical data. For example, in answering how much lettuce a restaurant should order next week or how many calls a customer support agent should be able to handle per day or week, the system looks back to historical data.
- Outliers model: This model works by analyzing abnormal or outlying data points. For example, a bank might use an outlier model to identify fraud by asking whether a transaction is outside of the customer’s normal buying habits or whether an expense in a given category is normal or not. For example, a $1,000 credit card charge for a washer and dryer in the cardholder’s preferred big box store would not be alarming, but $1,000 spent on designer clothing in a location where the customer has never charged other items might be indicative of a breached account.
- Time series model: This model evaluates a sequence of data points based on time. For example, the number of stroke patients admitted to the hospital in the last four months is used to predict how many patients the hospital might expect to admit next week, next month or the rest of the year. A single metric measured and compared over time is thus more meaningful than a simple average.
A predictive model is not fixed; it is validated or revised regularly to incorporate changes in the underlying data. In other words, it’s not a one-and-done prediction. Predictive models make assumptions based on what has happened in the past and what is happening now. If incoming, new data shows changes in what is happening now, the impact on the likely future outcome must be recalculated, too. For example, a software company could model historical sales data against marketing expenditures across multiple regions to create a model for future revenue based on the impact of the marketing spend.
Most predictive models work fast and often complete their calculations in real-time. That’s why banks and retailers can, for example, calculate the risk of an online mortgage or credit card application and accept or decline the request almost instantly based on that prediction.
Thank you very much for reading!
Hope to see you again with another article. Till then, Goodbye All!
References :
1. Helen Sharp, Jenny Preece, Yvonne Rogers — Interaction Design — Beyond Human-Computer Interaction-Wiley (2019) — Chapter 16
2. Wikipedia
