Usability Evaluation Methods

Heuristic Evaluation

What is Heuristic Evaluation

Heuristic evaluation is a usability engineering method for finding usability problems in a user interface design, thereby making them addressable and solvable as part of an iterative design process. It involves a small set of expert evaluators who examine the interface and assess its compliance with “heuristics,” or recognized usability principles. Such processes help prevent product failure post-release. <source>

about 20 years ago Jakob Nielsen firstly introduced this method and since then this method has evolved with time and nowadays this technique is used in almost every organization for its needs and purposes.

Conducting a Heuristic Evaluation

There are three main steps in the heuristic evaluation process. Those are Planning, Executing, and Reviewing.

  • Planning is understanding objectives and what needs to be evaluated.
  • Executing is going through the product and collecting data about the interfaces, flows, and improvement areas.
  • Reviewing is after the evaluation the experts should summarize their findings and understand problems and give solutions to those problems. The results are usually presented as a report.

Benefits of Heuristic Evaluation

· It is a detailed, technically sound process that assesses the product against very clear criteria.

· Heuristic evaluation tends to focus on fewer, more relevant areas so the problems it identifies tend to be important ones.

· Because several people do so, there is a better chance of getting a range of views and picking up more potential problem areas.

· Setting up the heuristic evaluation is a useful exercise as it forces you to identify the root elements of the product and focuses development on the main issues.

Drawbacks of Heuristic Evaluation

· Many experts are required, and this can be time-consuming and expensive to research and set up.

· You are getting opinions and personal observation rather than hard, empirical data from the exercise, and the experts’ own background, attitudes, preferences might color the verdicts.

<source>

Software Walkthrough

Walkthroughs are programming preparing arrangements that make clients stride by venture through a progression of activities toward a particular learning objective. In a business setting, programming walkthroughs are utilized for preparing. They are executed much of the time during the onboarding interaction however can be utilized at all stages during the client venture.

Walkthroughs are turning out to be more mainstream lately because they offer huge benefits over customary preparing strategies, for example, online courses or homeroom learning meetings.

Process

• Gather information regarding the topic in the document by involving stakeholders, both within and outside the software discipline.

• Describe and justify the contents of the document.

• Reach a common consensus on the document.

• Check and discuss the different solutions to a problem and different suggested alternatives.

<source>

Objective

As a general rule, a walkthrough has a couple of wide destinations: to acquire criticism about the specialized quality or content of the report; and additionally to acquaint the crowd with the substance. A walkthrough is regularly coordinated and coordinated by the creator of the specialized record. Any blend of intrigued or actually qualified workforce might be incorporated as appears to be fitting.

Participants

There are three special roles in software walkthrough,

  • The author: Who presents the software product in a step-by-step manner at the walk-through meeting, and is probably responsible for completing most action items.
  • The walkthrough leader: Who conducts the walkthrough, handles administrative tasks, and ensures orderly conduct.
  • The recorder: Who notes all anomalies (potential defects), decisions, and action items identified during the walkthrough meetings.

Web Analytics

Web analytics is the way toward analyzing the conduct of guests to a website. This includes following, investigating, and revealing information to quantify web action, including the utilization of a site and its segments.

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 to facilitate and streamline better business decisions.

Web examination empowers a business to hold clients, draw in more guests.

Process

  1. Setting goals. The initial phase in the web analytics measure is for organizations to decide objectives and the final products they are attempting to accomplish. These objectives can incorporate expanded deals, consumer loyalty, and brand mindfulness. Business objectives can be both quantitative and subjective.
  2. Collecting data. The second step in web analytics is the assortment and capacity of information. Organizations can gather data straightforwardly from a site or web examination apparatus, like Google Analytics.
  3. Processing data. The next stage of the web analytics funnel involves businesses processing the collected data into actionable information.
  4. Identifying key performance indicators (KPIs). In web analytics, a KPI is a quantifiable measure to monitor and analyze user behavior on a website. Examples include bounce rates, unique users, user sessions, and on-site search queries.
  5. Developing a strategy. This stage includes executing bits of knowledge to form procedures that line up with an association’s objectives. For instance, search questions led nearby can assist an association with fostering a substance procedure dependent on the thing clients are looking for on its site.
  6. Experimenting and testing. Organizations need to try different things with various techniques to track down the one that yields the best outcomes. <source>

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. A/B testing demonstrates the efficacy of potential changes, enabling data-driven decisions and ensuring positive impacts.

A/B test is the shorthand for a simple controlled experiment. in which two samples (A and B) of a single vector-variable are compared. These values are similar except for one variation which might affect a user’s behavior. A/B tests are widely considered the simplest form of controlled experiment. However, by adding more variants to the test, its complexity grows.

Benefits of A/B Testing

1. Improved user engagement

2. Improved content

3. Reduced bounce rates

4. Increased conversion rates

5. Higher conversion values

6. Ease of analysis

7. Quick results

8. Everything is testable

9. Reduced risks

10. Reduced cart abandonment

11. Increased sales

Process of A/B Testing

1. Inculcate a culture of experimentation by A/B testing user experience elements that are easy to change but still have big potential impacts.

2.When considering what to test, look at the sales funnel to determine where losing potential conversions.

3. Test changes only where changes are needed
If it ain’t broke, don’t test changing it.

4. Make A and B significantly different

5. Get ideas from everyone

6. Control for time

7. Run tests in week-long increments

8. Always be innovating

<source>

Predictive Modeling

Prescient Modeling is a factual method wherein likelihood and information mining is applied to an obscure occasion to foresee results.

Predictive modeling, a tool used in predictive analytics, refers to the process of using mathematical and computational methods to develop predictive models that examine current and historical datasets for underlying patterns and calculate the probability of an outcome. The predictive modeling process starts with data collection, then a statistical model is formulated, predictions are made, and the model is revised as new data becomes available.

Predictive Modeling Techniques

  • Logistic regression: a statistical analysis method that predicts the parameters of a logistic model based on prior observations of a data set
  • Decision trees: a flowchart-like tree structure in which each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node holds a class label
  • Time series analysis: refers to methods for illustrating and analyzing time-series data to extract meaningful statistics

Process

  • Clean up data by treating missing data and eliminating outliers
  • Determine whether parametric or nonparametric predictive modeling is most effective
  • Reprocess the data into a format appropriate for the modeling algorithm
  • Specify a subset of data to be used for training the model
  • Train model parameters from the training dataset
  • Conduct predictive model performance monitoring tests to assess model efficacy
  • Validate predictive modeling accuracy on data not used for calibrating the model
  • Deploy the model for prediction

Predictive modeling is a solution to the data discovery challenges in the continuously expanding data deluge in big data management systems.

Thank you for reading!

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Software Engineering Undergraduate at University of Kelaniya

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Pubudu Wickramathunge

Pubudu Wickramathunge

Software Engineering Undergraduate at University of Kelaniya

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