The User Experience (UX) Research process is essential to learn about the current market situation, as well as to understand users’ problems and needs. The use of specific metrics and KPIs (Key Performance Indicators) enables us to obtain relevant information on consumer preferences, emotions and behaviours, facilitating subsequent strategic product development and informed decision making.
Having a good research phase also helps to detect the company’s strengths and weaknesses. It facilitates the creation of functional, accessible solutions that stand out from the competition, resulting in successful products that are aligned with business objectives.
Now that new technologies make it possible to record a large amount of data linked to user experience, it can be difficult to decide which to prioritise. These are divided into two main groups, based on whether they represent behavioural or attitudinal metrics.
In this first part of the article we will be focusing on the role of behavioural metrics, and we will see which ones are considered essential for achieving good UX design.
Behavioural metrics
Behavioural metrics numerically evaluate user behaviour. They are based on concrete measurable actions, such as conversion rate, time spent on a page, task success rate and error rate.
1. Conversion rate
This is the percentage of users who perform a certain task. Although the desired action varies according to the context and the channel being analysed, the conversion rate is usually linked to concepts such as the number of purchases made, the number of subscriptions or the amount of clicks on a call to action, for example.
A high conversion rate indicates that the business objectives established for the product or service are being met, while a low conversion rate may highlight problems with the user experience, design or marketing strategy. There is no specific figure considered high or low, so it is important to understand industry averages and to evaluate the evolution of this parameter over time.
The formula for calculating this metric is as follows:
Conversion Rate (%) = (Number of conversions / Total number of users impacted) x 100
2. Time on page
The time users spend in an environment or screen can indicate the interest generated by the content, as well as revealing the presence of irrelevant or confusing information. It can detect which sections have a higher reading rate or better hold the user’s attention, as well as those that generate rejection and are abandoned in just a few seconds.
Applying changes to a specific screen and analysing how the time that individuals spend on it evolves enables us to understand whether an improvement in its content and usability has been achieved. However, it is important to take into account that this metric can be distorted by visitors who access the page and keep it open in the browser while performing other tasks, including the time during which they are not interacting directly with the product or service.
The formula for calculating it is as follows:
Time on page (seconds or minutes) = Exit time – Entry time
Source: OptinMonster
3. Task success rate
This UX Research metric evaluates users’ ability to complete tasks in a particular environment, thus providing relevant information about the suitability of the environment to meet individuals’ needs. The task success rate is based on the definition of key processes and product objectives, from which it can provide an objective measurement of the usability and efficiency of the system.
Therefore, a high rate indicates that users are able to follow the workflows correctly, without assistance and without making errors. It is an indicator that the system aligns with the expectations of its users; a low rate may indicate the existence of usability, design or functionality problems.
The formula for calculating it is as follows:
Task success rate (%) = (Users who completed the task / Users who tried to complete the task) x 100
4. Error rate
The error rate indicates user errors or confusion within an interface, including performance, design and navigation errors, as well as errors in data entry and task completion. This metric should take both the frequency and the severity of errors into account by assessing the extent to which they interfere with using the environment and achieving its objectives.
A high rate is usually associated with a system that offers interaction difficulties and a negative user experience, leading to frustration, confusion and loss of trust in the brand. Identifying and correcting these errors is therefore essential to reduce the abandonment rate and to improve the quality of the product.
The formula for calculating it is as follows:
Error rate (%) = (Errors committed by users / Total number of interactions) x 100
Source: Dribbble
