Pasan lahiru dissanayake

Aug 2, 2021

6 min read

Interaction Design Evaluation

Today I am going to talk about some inspection techniques that help evaluate interaction designs. These include heuristic evaluation, walk-throughs, web analytics, A/B testing, and predictive models. These techniques tend to be relatively inexpensive and easy to learn, and they are also very effective, which makes them attractive.

Heuristic Evaluation

What is Heuristic Evaluation?

Heuristic evaluation is a process where experts use rules of thumb to measure the usability of user interfaces in independent walkthroughs and report issues. Evaluators use established heuristics (e.g., Nielsen-Molich’s) and reveal insights that can help design teams enhance product usability from early in development.

Heuristic Evaluation: Ten Commandments for Helpful Expert Analysis

  1. Visibility of system status
  2. Match between system and the real world
  3. User control and freedom
  4. Error prevention
  5. Help users recognize, diagnose, and recover from errors
  6. Consistency and standards
  7. Recognition rather than recall
  8. Flexibility and efficiency of use
  9. Aesthetic and minimalist design
  10. Help and Documentation

Heuristic Evaluation — for Easy-to-use, Desirable Designs

When you apply the Nielsen-Molich heuristics as an expert, you have powerful tools to measure a design’s usability with. However, like any method, there are pros and cons:

Walk-Throughs

Walk-throughs provide an alternative approach to heuristic evaluation to predict user problems without running user tests. As the name implies, walk-throughs involve going 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, use a team that may include users as well as developers and usability specialists.

Here I consider cognitive and pluralistic walk-throughs. Both were originally developed for evaluating desktop systems, but, like heuristic evaluation, can be adapted for other types of interfaces.

Cognitive Walk-Throughs

The Cognitive Walkthrough method is a usability inspection method used to identify usability issues in interactive systems that focuses on how easy it is for new users to accomplish tasks with the system. Cognitive walkthrough is task-specific, while heuristic evaluation takes a holistic view to identify problems not captured by this and other usability inspection methods.

Pluralistic Walk-Throughs

The pluralistic walkthrough is a usability testing method used to identify usability problems with a software or website in order to create the most user-friendly human-machine interface possible. In this method, a group of users, developers, and usability experts walk step-by-step through a task scenario and discuss usability issues related to dialog elements involved in the steps of the scenario.

Web Analytics

What is Web Analytics?

Web Analytics is the methodological study of online/offline patterns and trends. It is a technique that you can employ to collect, measure, report, and analyze your website data. It is normally carried out to analyze the performance of a website and optimize its web usage.

We use web analytics to track key metrics and analyze visitors’ activity and traffic flow. It is a tactical approach to collect data and generate reports.

Importance of Web Analytics

We need Web Analytics to assess the success rate of a website and its associated business. Using Web Analytics, we can −

  • Assess web content problems so that they can be rectified
  • Have a clear perspective of website trends
  • Monitor web traffic and user flow
  • Demonstrate goals acquisition
  • Figure out potential keywords
  • Identify segments for improvement
  • Find out referring sources

Web Analytics Process

The primary objective of carrying out Web Analytics is to optimize the website in order to provide better user experience. It provides a data-driven report to measure visitors’ flow throughout the website.

Take a look at the following illustration. It depicts the process of web analytics.

  • Set the business goals.
  • To track the goal achievement, set the Key Performance Indicators (KPI).
  • Collect correct and suitable data.
  • To extract insights, Analyze data.
  • Based on assumptions learned from the data analysis, Test alternatives.
  • Based on either data analysis or website testing, Implement insights.

A/B Testing

What is A/B testing?

A/B testing (also known as split testing or bucket testing) involves comparing two versions of a website or app to determine which version performs better. A/B testing is essentially an experiment in which users are randomly shown two or more variations of a page and statistical analysis is used to determine which variation performs better for a particular conversion goal.

When you run an A/B test that directly compares a variant to an actual experience, you can ask targeted questions about changes to your website or app and then collect data about the impact of those changes.

Testing takes the guesswork out of website optimization and enables data-driven decisions that shift business conversations from “we think” to “we know.” By measuring the impact of changes on your metrics, you can ensure that each change leads to positive results.

A/B testing process

The following is an A/B testing framework you can use to start running tests:

01.Collect data

02.Identify goals

03.Generate hypothesis

04.Create variations

05.Run experiment

06.Analyze results

Predictive Models

Predictive modeling is a commonly used statistical technique for predicting future behavior. Predictive modeling solutions are a form of data mining technology that analyzes historical and current data to create a model that helps predict future outcomes. Predictive modeling involves collecting data, formulating a statistical model, making predictions, and validating (or revising) the model as additional data becomes available. For example, risk models can be built to combine member information in complex ways with demographic and lifestyle information from external sources to improve the accuracy of risk assumption. Predictive models analyze past performance to assess how likely a customer is to engage in a particular behavior in the future. This category also includes models that look for subtle data patterns to answer questions about customer performance, such as fraud detection models. Predictive models often perform calculations during ongoing transactions, for example, to assess the risk or opportunity of a particular customer or transaction and make a decision. If health insurers could accurately predict secular trends (e.g., utilization), premiums would be set appropriately, profit targets would be met with greater consistency, and health insurers would be more competitive in the marketplace.

Types of Predictive Models:

01.Classification Model

02.Clustering Model

03.Forecasting models

04.Outliers model

05.Time series models

Thankyou!

References:

INTERACTION DESIGN beyond human-computer interaction Fifth Edition