Every user has an analytical approach to the product they’ve just bought, regardless of whether they understand it. Let’s say you’ve just bought a toothbrush. You’re comparing it to the previous brushes that you’ve owned. You’re measuring it up to the price you’ve paid for it or even the feeling you have while using it. Businesses, however, need to take a more scientific, statistical, and overall data-based approach to this issue. You can’t just randomly decide that because that one user didn’t feel good about your product, you must incinerate all the units and start fresh. You need data and more objectivity – this is what usability testing is used for. Still, how do businesses get into the depth of these things? How do they decode user behavior and understand their feelings about the products (sometimes even better than the users themselves)?
Decoding User Behavior
They use some practices to get to the bottom of things.
1. What is usability testing?
The biggest question on everyone’s mind is, what is usability testing? This concept envelops the usability of a product or service, how easy it is to use, its effectiveness at its core tasks, and how it meets the user’s expectations.
To summarize, usability testing is a more sophisticated approach to examining standard user experience. This is because great usability usually results in a great user experience.
Traditionally, usability testing is handled in the development stage; however, with the agile development methodology, this development stage never ends. Products are still in development long after being launched, and people understand this concept. People are more willing to pay for early access, provided the pitch is good enough. Still, you’ll face a sizable churn rate if the follow-up updates and patches are not good enough.
So, before you start testing for usability, ensure you understand the process.
2. The most important usability testing KPIs
Just saying that you want to check the product for usability would be like assessing a meal and seeing if it tastes good. It’s highly non-specific and subjective, which means that, from an analytical standpoint, it doesn’t have any kind of value. Instead, what you need to start from is the importance of setting the right KPIs. Some of these KPIs are:
- Task success rate: Most users buy a product with a specific purpose in mind. The first and foremost KPI is whether this task is completed successfully. Every product sometimes fails, so you want the highest potential success over many uses.
- Learnability: It’s incredibly important that new users can master the use of the product quickly. Previously, we’ve talked about the task success rate. Sometimes, the failure is caused by improper product use (but it still counts toward the statistic). The simpler the product is to use (or the more instructive it is), the lower this figure will go.
- Task abandonment rate: Some users will just give up after a while. This will ensure that they don’t keep using your product, that they don’t recommend it, etc.
- Error severity: What happens if you fail? This might prevent people from trying or playing around with the product. What you’re aiming for is a safe product that’s experimentation-friendly.
Task-specific metric: The specific accuracy of the product when it comes to its main task is arguably the most important metric, but it’s also quite hard to define.
Remember that the KPIs used depend on the type of products. For instance, if the product is something that you discard after the first use, measuring useful life or quality degradation after use will be completely pointless.
3. Setting clear objectives- Decoding User Behavior
Imagine if you decided to try a 100m dash and determined that the next dash would be your best score yet. However, you’ve never measured your time before and decided that, after your first measured performance, you’ll arbitrarily decide what your previous best time (approximate) was. There’s simply no scenario where you fail.
This is why bets are never made retroactively and why you need to put your predictions on paper to prove that they were predictions.
Now that you have these KPIs, you must set clear objectives. What do you define as success? What kind of accuracy rate are you looking at? What kind of deviations are acceptable, and which are a reason for a major concern?
Not all deviations are equally as bad. If you wanted a product with 98% accuracy and your product scored 97.8% in almost all scenarios, this would be considered a resounding success.
Another point is that this way, you have a good chance of staying at least somewhat objective.
Now, some guides would put setting objectives before the KPIs, but the truth is that this is not the right way to go. You must first know what you’re measuring. Then, you need to state where you want to be.
4. Prototype vs. live product testing- decode user behavior
We even have to discuss this because there has been a major shift in how products are developed, launched, and used over the past several years. For instance, in the past, with the waterfall development method, you made a full product and launched it. In this scenario, all you could do is make a prototype of a product and distribute it to get the general sentiment of the user base toward it.
Today, things are different. In 2023, it’s nothing unusual for a user to launch a v0.01 of the product and gather live feedback. The benefits of this are numerous. First, you get a massive audience instead of just having a select few users. Since you’re open about it being a very early version of the product/platform, people will be more understanding toward bugs, lack of content, or lack of features. Just keep in mind that with all the AI UX tools, the UI expectations are high from the very start.
The thing is, however, that you need to make a roadmap and actively work on the issues that are pointed out by your audience. Just because you get a larger number of participants and some leeway doesn’t mean you get a free card to do as you want.
Also Read: Understanding Remote Usability Testing and Why It Is Effective
5. Capturing user behavior- Decoding User Behavior
Now, we come to the most important part – capturing user behavior. This consists of two major aspects:
- Taking verbal feedback: First, you want to get verbal feedback from your users. Sometimes, you need to know what they’re thinking about your product. You want the more abstract thought that can’t be observed. Sometimes, someone will abandon your platform without experiencing any bugs or problems. You want to know why.
- Observing user action: Sometimes, you must look past the self-reported feedback. You need to look at what people are doing, not just listen to what they’re saying. A person will lose a game on Steam and go to leave a vitriolic review… but then you look at their profile and see that they have 1,444 hours in the game, which is why you should probably take this review with a grain of salt.
Either way, before you start analyzing data, you must first gather it.
Data entry is another huge issue, especially since, depending on the analytical tools you’re using, you might have to work with structured data. Fortunately, this might become a lot easier with the latest breakthroughs in AI and NLP (natural language processing) technologies.
6. Following through- Decoding User Behavior
Feedback is a two-way street. In other words, when you ask your users a question, you expect an answer in return. When your users offer this answer, they expect you to do something about it.
- When they complain about the UI, they expect you to improve it.
- If they report a bug, they expect you to fix it.
- When they praise a feature, they expect more of it in the future.
The bottom line is that you need to implement some of these changes and, at least, promise to fix others before asking a new set of questions.
The bottom line is that you do not do usability testing just once. You do it continuously with every change you introduce. This is why A/B testing in usability testing is so important.
If you show your audience that you’ll do nothing about their feedback, you’ll soon start receiving feedback. Since people don’t like being ignored, you risk turning some users into anti-fans (actively posting against you on review sites and rating you negatively). In other words, don’t ask for constructive criticism if you:
- Can’t handle it!
- Don’t intend to do anything about it!
Just see this as a part of a larger process, not a one-off.
Your customers have different experiences to that of your own; try your best to understand it
In the end, you might see the best possible outcome – learn that your product is perfect and that there’s nothing you can improve on. In reality, this won’t happen. Even if the feedback is good, there are always tweaks and features to polish. If it’s not broken, don’t fix it; it really doesn’t apply to product development.
By Srdjan Gombar
Veteran content writer, published author, and amateur boxer. Srdjan is a Bachelor of Arts in English Language & Literature and is passionate about technology, pop culture, and self-improvement. His free time he spends reading, watching movies, and playing Super Mario Bros. with his son.
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