Usability Of user Interfaces

Heuristic Evaluation

Heuristic evaluation is a thorough assessment of a product’s user interface, and its purpose is to detect usability issues that may occur when users interact with a product and identify ways to resolve them.

When we think about the design of a product, the first thought that comes to mind is how something looks:

While all this is technically true for a good design, a great design needs to go the extra mile. How to achieve this?

By making sure your product not only looks awesome but also provides a seamless user experience.

10 of heuristics shown here:

There are several inspection methods for heuristic evaluation:

While the end goal is similar, the efficiency and validity of each are not.

Cognitive walkthrough

User testing

Heuristic analysis

From all three usability inspection methods, heuristic analysis is the most reliable, as tests are more rigorous and systematic.


Walk-throughs offer an alternative approach to heuristic evaluation for predicting users’ problems without doing user testing. As the name suggests, walk-throughs involve walking through a task with the product and noting problematic usability features. While most walk-through methods do not involve users, others, such as pluralistic walk-throughs, involve a team that may include users, as well as developers and usability specialists.

amazing walkthrough examples for apps and websites

Grammarly is a digital writing tool that checks your grammar. You’ve likely heard of it already because it has an impressive 7 million active daily users.

Upon installing the Chrome Extension, Grammarly asks new users to personalize their experience.

Fitness applies to everybody. Every single person needs to stay fit and active in some way. This makes the fitness industry lucrative, but it also makes it very competitive. For a fitness app like MyFitnessPal, an amazing walkthrough is vital for encouraging new users to get started.

After asking a couple of necessary questions, The app creates a custom plan for new users designed to help them with their fitness goals.

Honey is a coupon site that automatically applies online coupons for users when they are shopping on eCommerce websites.

Web Analytics

Web analytics is the process of analyzing the behavior of visitors to a website. This involves tracking, reviewing, and reporting data to measure web activity, including the use of a website and its components, such as webpages, images, and videos.

Data collected through web analytics may include traffic sources, referring sites, page views, paths are taken, and conversion rates. The compiled data often forms a part of customer relationship management analytics (CRM analytics) to facilitate and streamline better business decisions.

why we need web analytics?

Examples for web analytics.

A/B Testing

An A/B test aims to compare the performance of two items or variations against one another. In product management, A/B tests are often used to identify the best-performing option. For example, two variations of a new user interface could be tested, and, in this case, the variation that receives the most user engagement would win the A/B test.

An A/B test is used to determine which version or variant of something will perform more effectively in the market. This strategy is commonly used by marketing and advertising professionals, who show multiple versions of an ad, marketing email, or web page to randomly selected users, and then analyze the results. Product managers can also use A/B testing to develop products that will resonate with users.

There are many benefits to using A/B tests, including:

Examples for A/B Testing

Predictive modeling

Predictive modeling, also called predictive analytics, is a mathematical process that seeks to predict future events or outcomes by analyzing patterns that are likely to forecast future results. The goal of predictive modeling is to answer this question: “Based on known past behavior, what is most likely to happen in the future?

Once data has been collected, the analyst selects and trains statistical models, using historical data. Although it may be tempting to think that big data makes predictive models more accurate, statistical theorems show that, after a certain point, feeding more data into a predictive analytics model does not improve accuracy. The old saying “All models are wrong, but some are useful” is often mentioned in terms of relying solely on predictive models to determine future action

Predictive modeling is often performed using curve and surface fitting, time series regression, or machine learning approaches. Regardless of the approach used, the process of creating a predictive model is the same across methods. The steps are:

Thank you.

Software Engineer under graduate