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An overview of UX Statistics

An Overview of ux statistics

I have been a stats defier all my life. But in UX Design, we use a lot of metrics – for valuable insight into whether or not a design is working or comparison between two iterations, or even to a competitor’s product. When it comes to UX statistics, many of us have heard that every dollar spent on UX brings between $2 and $100 in return. Many brands like Apple, Uber, Google and even Airbnb are successful due to their human-centric approach, which reflects in exceptional user experiences.

Right off the bat, I want to make one thing clear – there is no UX statistics symbol! Yes, folks. You heard it right. Someone from my workplace asked me about it while we were talking about UX statistics, and I could not give him an answer. So I googled it and even asked a few professionals on LinkedIn, but they could not give me a definitive answer. So I have concluded that it does not exist. If somebody has an answer to that question, please feel free to tell me all about it.

For a UX designer, although the focus is on the user. Sometimes, the number, in the end, turns out to be in thousands or even millions. That is where UX statistics comes in to play.

UX Statistics reflect that:

  • 88% of users won’t revisit a website after a bad user experience.
  • 40% of the people leave the website if it takes more than 3 seconds to load.
  • 91% of unsatisfied users leave without any feedback.

UX research statistics

UX designers have numerous qualitative methods to improve their design like a focus group, user interviews, customer journey mapping or even task analysis. Many of the methods are intuitive and sturdy, but in the end, they remain qualitative.

If you want a better understanding of user research to extend your knowledge base, click here!

These qualitative methods remain widely used, yet we need to take a quantitative approach to understand the users. That is where UX statistics come in. But how to find UX statistics during research? Simple, there are metrics that UX designers use to measure:

Efficiency

Question: How swiftly can a user complete the tasks?

Answer: by measuring click count and page count. Eye-Tracking is also useful to measure efficiency.

Satisfaction

Question: How satisfied is the user after with the product?

Answer: using the System Usability Scale (SUS) or Computer System Usability Questionnaire (CSUQ) or even Net Promotor Score (NPS) to find out.

Effectiveness

Question: Can the user complete the tasks successfully?

Answer: You can measure Errors and Task Success to figure it out.

The exact data that one collects for UX statistics depends on the goals of the product, budget and other resources.

Types of Data

Analyzing and understanding the collected data is decisive because each type has its way of being analyzed. So let me give you a run on the four types of data that can determine your UX statistics and effective ways to analyze them as well.

Nominal Data

Nominal data are categories that are not numerically ordered. User characteristics or locations are some examples of nominal data. UX statistics that are simple and descriptive, utilize nominal data. One can use the chi-square test to compare nominal data. This test decides if the data from both categories are related or not.

Ordinal Data

Ordered categories are called Ordinal data. For example, a user can choose any answer in a multiple-choice question depending upon how it resonates with them. Although comparable, the distance between each answer is meaningless. Therefore, you can not analyze the data using an average rank or other statistical methods. To analyze ordinal data, one must observe the frequencies. One can use the Wilcoxon Rank Sum Test to make the comparison.

Interval Data

Interval data are continuous data where the distances between the values remain meaningful without absolute zero points. An excellent example can be temperature data, where it can be either +25 degrees or -25 degrees, as zero-point for temperature is arbitrary.

A common way to analyze interval data is by comparing the averages, with the help of ANOVA, which uses three or more samples.

Ratio Data

Ratio data are similar to interval data, but with an absolute zero point. Take time as an example: at the end of every hour, the clock resets itself and starts again from zero, making it an absolute zero point. Ratio data can be analyzed using the same tests as interval data; the only difference is Geometric mean. One can use both t-test and ANOVA to analyze the results.

User experience stats

User experience is essential to creating products that customers love. But many people who do research, such as UX designers, product managers, or dedicated researchers, feel that the research is not enough.

But businesses who are in it for the money never do ample research before they start designing their product. The most common excuse is either time or the budget. Well, I am here to tell you that this is where they go wrong! Allow me to share some cold and hard facts that I found online to make my case:

  1. Cost of correcting an error post-development is 100x that of fixing it prior development – The ROI of User Experience.
  2. Developers spend 50% of their time on avoidable rework – The ROI of User Experience.
  3. 3/4 of users prefer to revisit mobile-friendly sites – Google.
  4. 52% of users don’t revisit a website due to its aesthetics. – Digitalpeel.
  5. 13% of users will tell 15+ people about their bad experiences. Slideshare.
  6. 72% will tell 6+ people about good experiences. – Slideshare.
  7. 90% of users stopped using an app due to poor performance. – Toptal
  8. Businesses dissipate $62 billion per year due to poor customer service. – Intechnic
  9. 39% of users will not engage with content unless the illustrations load or if the loading time takes too long – Adobe
  10. Sites with a superior user experience have a visit-to-lead conversions rate of 400% or higher – Forrester.

So the next time you go up to the senior management, share the above UX statistics that can help their business’s bottom line with a superior product.

Conclusion

I hope you got to learn something new after reading this blog! If you wish to learn more about UX statistics, I would recommend reading Quantifying the User Experience and Measuring the User Experience as well. Both books have good reviews on Amazon.

I would like for you to remember that qualitative analysis will offer the why, but quantitative analysis only will offer the what. Both of these aspects are equally important to create a product with a fantastic user experience.

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