Interactive Design Evaluation Methods
Hello everyone, In this article we focus the evaluate methods for interactive designs. These include heuristic evaluation, walk-thorough, web analytics, A/B testing and predictive models. So, Let’s talk about what are these models and how they apply by us.
What is 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 great way to grade a product’s user experience or usability is by user testing it, which although consumes more resource produce the best results.
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.
How to Conduct a Heuristic Evaluation
You can follow these simple steps
- Know what to test and how -Whether it’s the entire product or one procedure, clearly define the parameters of what to test and the objective.
- Know your users and have clear definition of the target audience’s goals, contexts, etc. User personas can help evaluators see things from the user’s perspectives.
- Select 3–5 evaluators, ensuring their expertise in usability and the relevant industry.
- Define the heuristics(around 5–10) — This will depend on the nature of the system/product/design. Consider adopting/adapting the Nielsen-Molich heuristics and/or using/defining others.
- Brief evaluators on what to cover in a selection of tasks, suggesting a scale of severity codes (e.g., critical) to flag issues.
- 1st Walk-through — Have evaluators use the product freely so they can identify elements to analyze.
- 2nd Walk through — Evaluators scrutinize individual elements according to the heuristics. They also examine how these fit into the overall design, clearly recording all issues encountered.
- Debrief evaluators in a session so they can collate results for analysis and suggest fixes.
Advantages of using the Heuristic Evaluation
- It is an inexpensive usability testing methods that can test the product based on number of in-house UX experts
- It is a quick testing tool as it doesn’t require to prepare a representative user sample to do the testing
- It can be used to element the common usability problems that don’t need a feedback from the end user
- It can be used prior to other usability testing methods to focus on the user-specific usability issues
Disadvantage of using the Heuristic Evaluation
- It doesn’t involve the user opinion in the testing. As the UX experts who do the testing are not the same as the end userIt should depend in more than one UX expert evaluators in order to ensure accurate results for the testing
- It should depend on more than one UX expert evaluators in order to ensure accurate results for the testingIt may be difficult to find the experts who can do the evaluation in-house. Also, it may be expensive to hire external evaluators
- It may be difficult to find the experts who can do the evaluation in-house. Also, it may be expensive to hire external evaluatorsIt only works to identify general usability testing issues. Some issues can be identified as a problem when it is not actually a problem for the end users
- It only works to identify general usability testing issues. Some issues can be identified as a problem when it is not actually a problem for the end users
- It can’t be used alone to end up with accurate usability testing, other methods with user involvement should be used as well
Walk-through 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 walk-through can be per-planned or organized based on the needs. Generally, people working on the same work product are involved in the walk-through process.
What is Walk through 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.
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.
Walkthrough software typically delivers content in one of three ways:
- Through a browser extension.
- Via an API
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.
Businesses use web analytics platforms to measure and benchmark site performance and to look at key performance indicators that drive their business, such as purchase conversion rate.
How Web Analytics Work
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).
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:
- number of visits, number of unique visitors
- new vs. returning visitor ratio
- what country they are from
- what browser or device they are on (desktop vs. mobile)
- common landing pages
- common exit page
- frequently visited pages
- length of time spent per visit
- number of pages per visit
- bounce rate
- which campaigns drove the most traffic
- which websites referred the most traffic
- which keyword searches resulted in a visit
- campaign medium breakdown, such as email vs. social media
Web Analytics Examples
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.
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 and a popular alternative, allowing companies full ownership and control of their data
- Adobe Analytics — highly customizable analytics platform (Adobe bought analytics leader Omniture in 2009)
- Kissmetrics — can zero in on individual behavior, i.e. cohort analysis, conversion and retention at the segment or individual level
- Mixpanel — advanced mobile and web analytics that measure actions rather 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.
A/B Testing, also known as split-run testing or bucket test, is an experiment where 2 or more variants of the same thing are compared against each other to see which one performs better. The audience is randomly presented with two versions after which ab testing statistics are used to judge their performance.
How Does A/B Testing Work?
In A/B Testing, the current version(A) of the variable and a slightly modified one(B) is taken. The current version (control) is shown to part of the users and the second version( variation) is presented to the rest. The results of A/B testing are then found on the basis of audience engagement with the variable they were shown. The changes in user engagement are recorded and analyzed through statistical methods, in order to determine if the change received a positive, negative or neutral response and which version can achieve a specific conversion goal.
The A/B Testing Process
- Identify Problem Areas- Data on the different elements of your website will give you the high and low performing players. You can then identify pain points, say, for instance, pages with high bounce rate, and begin strategizing on optimization. For example, if you want to test your emails, you can choose which problem you want to focus on either your email content or headline.
- Set Conversion Goals- Goals become the criteria based on which you will understand whether the original version or modified version garners a greater response from the target audience. Goals could be getting signups, answering surveys or clicking CTAs. If you’re testing the email subject line, then your goal should be focussing on open rates and if it’s the sign up button, then it should be the click rate.
- Ideation- After deciding on your goal, you can start brainstorming ideas for creating version B. Analyze the reason for the change on the basis of factors like relevance, potential impact, difficulty and cost of implementation. In order to get a few ideas for variations you can use a few tools specifically designed for A/B Testing like Google Optimize, Kissmetrics, Unbounce and others.
- Create Variations- Select an idea and create variations for A/B testing. For more accurate modifications, you will need an A/B testing software. Alterations can be changing the color of the background, font, header placement, etc. you can create 2 variants of say, a button, with a colour change to see which one attracts more clicks.
- Run the Test- Once the variation is ready, you start A/B Testing with the variation A and B by randomly assigning them to visitors. Measure and analyze visitor interaction for each variation, analyze results to see how they perform.
Why is A/B Testing important?
A/B Testing enables businesses to understand user behavior with each change while simultaneously collecting data on the results.
The dynamic changes in user engagement patterns allow them to create a hypothesis about how and why certain elements when altered impact user reactions.
An important reason A/B Testing is useful is that it eliminates any guesswork in trying while optimizing website development and instead facilitates improvement based on hard-core statistics.
Benefits of A/B Testing
A/B Testing provides numerous advantages that significantly help to improve the performance of your website.
Higher user engagement- different elements of the website are A/B tested to see which have an impact on user engagement. When positive changes are incorporated into the final design, it increases user engagement and successfully optimizes the website for better results. A win-win for both!
Spike in Conversion Rates- when two versions are tested with visitors, you understand which attracts more interactions from the audience. When the site is designed according to the preferences of visitors, it obviously makes it easy to convert them into buyers.
Lower Bounce Rates- A/B Testing can expose the weaker areas of your website design and user response to changes. Finding the perfect combination of elements for your website can help visitors spend a greater amount of time on it, thus, drastically reducing bounce rates.
Reduced Cart Abandonment- it is a fairly common phenomenon is e-commerce when customers don’t complete the checkout process and exit your site without making a purchase. This is called cart abandonment. With the help of A/B Testing, the customer experience from the checkout page to entering delivery address can be optimized to the fullest by identifying the loopholes in the process.
Better Content Management- With A/B testing, you can test website content, email, ad copy and see whether they are generating the desired amount of engagement or not. After identifying areas that are lacking, you can tweak content in the way which accumulates captures better interactions from users.
Predictive modeling uses statistical techniques to predict future user behaviors. To understand the intricacy of the design of predictive analytics, you must dive deep and comprehend what a predictive model is. A predictive model uses historical data from various sources. You must first normalize the raw data by cleansing it of anomalies and preprocess it to fit a suitable format that would facilitate analysis. Then, apply a statistical model to the data to draw inferences. Each predictive model comprises various indicators — that is, factors that would likely impact future outcomes — that are called independent variables, or predictor variables.
Applying a predictive-analytics algorithm to UX design does not result in changes to a user interface. Instead, the algorithm presents users with relevant information that they need. Here’s a simple illustration of this capability from the eCommerce domain: A user who has recently purchased an expensive mobile phone would likely need to purchase a cover to protect it from dust and scratches. Therefore, that user would receive a recommendation to buy a cover. The eCommerce site might also suggest other accessories such as headphones, memory cards, or antivirus software.
Here are some other examples of predictive modeling. Spam filters use predictive modeling to identify the probability that a given message is a spam. The first mail-filtering program to use naive Bayes spam filtering was Jason Rennie’s ifile program, which was released in 1996. Bayes theorem predicted which email messages were spam and which were genuine. Facebook uses Deep Text, a form of unsupervised machine earning, to interpret the meaning of users’ posts and comments. For example, if someone said, “I like blackberries,” they might mean the fruit or the smartphone. In Customer Relationship Management, predictive modeling targets messaging to those customers who are most likely to make a purchase.
Predictive User Experience
Envision coffee machines that start brewing just when you think it’s a good time for an espresso, office lights that dim when it’s sunny and workers don’t need them, your favorite music app playing a magical tune depending on your mood, or your car suggesting an alternative route when you hit a traffic jam.
Predictability is the essence of a sustainable business model. In a digital world, with millions of users across the globe, prediction definitely has the power to drive the future of interaction. Feeding a historical datasets into a system that uses machine-learning algorithms to predict outcomes makes prediction possible.
A Samart Guide to A/B Testing | Orderhive
A/B Testing, also known as split-run testing or bucket test, is an experiment where 2 or more variants of the same…