Interactive Design Evaluation Methods

Heuristic Evaluation

How to Conduct a Heuristic Evaluation

  1. 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.
  2. 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.
  3. Select 3–5 evaluators, ensuring their expertise in usability and the relevant industry.
  4. 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.
  5. Brief evaluators on what to cover in a selection of tasks, suggesting a scale of severity codes (e.g., critical) to flag issues.
  6. 1st Walk-through — Have evaluators use the product freely so they can identify elements to analyze.
  7. 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.
  8. 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


  1. Loading a JavaScript in the header of your application.
  2. Through a browser extension.
  3. Via an API

Web Analytics

Sample Web Analytics Data

  • 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

  • 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
  • — offers detailed real-time analytics, specifically for publishers
  • CrazyEgg — measures which parts of the page are getting the most attention using ‘heat mapping’

A/B Testing

How Does A/B Testing Work?

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?

Benefits of A/B Testing

Predictive Models

Predictive User Experience





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Niroshan Pushparaj

Niroshan Pushparaj

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