UX metrics are a set of quantitative data points used to measure, compare, and track the user experience of a website or app over time. They are vitally important for ensuring UX design decisions are made and evaluated using fair evidence rather than opinions.
KPIs (key performance indicators) reflect the overall goals of your business – such as revenue growth, retention, or increased user numbers. Metrics are all the measurements that go towards quantifying these higher goals.
So when you’re running any kind of usability research, such as UX Benchmarking, it’s important to choose metrics that reflect your objectives and the overall KPIs of your business.
But which metrics are the most valuable? What should you be measuring? NPS? AOV? TPI? SUS? CUS? Come see how many of these abbreviations I invented myself in this investigation into how the experts measure UX.
My huge thanks to Kuldeep Kelkar, our VP of Consulting and Professional Services, who provided valuable insight for many of these metrics.
It’s all well and good us sitting around in our ivory tower yelling how great UX research is out of the window to passers-by, but this can only get us so far.
Occasionally some of those people look up and yell back, “yeah I know! It just makes common sense to make design decisions based on actual human behavior” but then they often make the following point…
“But how can we measure that? If we run usability testing and make a change to a website that presumably improves the user experience based on our observations, how do we really know the change has worked? What UX metrics can we use to measure success? How do we prove to our bosses that the investment is worth it?”
It’s normally around the ‘metrics’ mark where we start to close the window and mumble something about “having to keep it shut because of the air-con, sorry I can’t hear you.”
Metrics were traditionally a difficult discussion when it comes to measuring the success, failure of shrugging indifference of your UX. Every other discipline has it made!
As we already know, data only shows part of the story. Google Analytics can tell you what’s happening but not why it’s happening. If you’re only going by analytics, you’re essentially guessing. Sure, it can be an educated, highly informed guess – but you won’t know exactly why things are happening on your site until you see real people using it.
But UX measurement doesn’t have to be an intangible mystery. As you’ll see below, there are many ways to prove the value of UX research.
We work with various companies across all industries and have noticed certain metrics that are most commonly used for benchmarking (either over a period of time or compared against competitors). We broadly divide them into two categories:
In the user research world, it’s critical to understand what people are doing, and how they are using your products. Task-based usability testing is a standard method to gather this information across the industry. We don’t mean just ‘in-lab’ think-out-loud studies, but also remote moderated studies, which will help you access larger sample sizes in an efficient way.
Typical metrics you could capture include these task-level behavioral measurements:
How users feel, what they say before, during or after using a product, and how this affects brand perception.
To measure this, you might want to capture these attitudinal metrics:
But how do you quantify opinion? How do you take these “oooooh pretty” or “OW MY EYES!!!” hot takes and turn them into a simple score that any busy executive can understand?
Let’s take a deeper dive into these individual metrics, and we’ll see how they can help to form a bigger picture.
For an in-depth guide to measuring UX and proving the value of research, download our free ebook on running both longitudinal and competitive benchmarking.
Quite simply, how many people have come to your online retail store, put a bunch of products in their basket, and then just left without checking out. Behaviour that would make a real world IKEA visit a treacherous assault course. The abandonment rate is the ratio of the number of abandoned shopping carts to the number of initiated transactions.
AOV means average order value, and this is simply your total revenue / number of checkouts. According to VWO this is a “direct indicator of what’s happening on the profits front.” If your UX efforts directly tie into increasing cross-selling or upselling, then AOV can be an indicator of whether you’ve improved things or not.
Helpful if there’s a specific thing triggered by a UX improvement. Say for instance a web-form completion, newsletter sign-up or some other task completion. If the site change directly impacts how many people are converting in that specific task, and you can measure that accurately, then you can be *fairly* confident you made an impact.
Just remember that having a higher conversion count may also be a result of marketing efforts, so be sure to measure the conversion rate (typically Number of Sales / Number of Visits).
As NN/g suggests:
“The conversion rate measures what happens once people are on your website. Thus it’s greatly impacted by the design and it’s a key parameter to track for assessing whether your UX strategy is working.”
And because we like to argue both sides of the… uh… argument… here’s ecommerce whiz kid Dan Barker on why you shouldn’t necessarily trust conversion rate as the solution to all your problems. Remember that not all visitors to your webpage have the potential to convert, or that conversion rates vary wildly based on visitor type.
Website page views and clicks are a common metric. For mobile apps, or even web applications or even single-page web apps, some combination of clicks, taps, number of screens or steps can be measured.
If you are running an in-lab study, counting these can be extremely tedious. But, if you are using a user research platform like ours, most of these metrics are captured automatically and significantly reduce analysis and reporting time. In most cases, combining these, or at least connecting them to analytics data (from the live site or apps) is beneficial.
These can be measured as a Number of unique problems identified and/or Number (or %) of participants that encounter a certain problem. We recommend conducting Think-Out Loud studies to identify problems, and then quantify them via a large-sample study to find the % of problems actually encountered by a large population (with confidence intervals).
Most of these Behavioral KPIs are collected ‘per task’ and then aggregated as an average for a given study, and/or digital product. These are then compared over a period of time (e.g. each quarter) or compared with competitors’ digital products.
Our single UX metric score
If you’re looking for a single UX metric to clearly prove to stakeholders that your product is improving through user research, let us introduce you to the qxScore!
Typically, a group of representative users are given a set of realistic tasks with a clear definition of task success – examples of task success could be: Reached a specific page in a check-out flow, found the right answer on a marketing website or reached a step in mobile app. Having a clear definition of success and/or failure is critical.
If eight out of 10 users completed the task successfully and two failed, then Task Success would be 80%. Because of the small sample size of 10, the Margin of Error at 90% Confidence Level would be about +-25. This means that we are 90% confident that the Task Success rate falls somewhere between 55% to 100%.
But if 80 out of 100 users completed the given task successfully, then the Task Success rate would still be 80%, but with a Margin of Error of about 8%. Generally speaking, this means that we are 90% confident that the Task Success Rate falls somewhere between 72% to 88%. The larger the sample size, the smaller the Margin of Error.
Usually an absolute number. For example: 3 mins. For most task-based studies, where the user goal is to get something done as efficiently as possible, shorter task times are better. There are exceptions, though: if the goal is to keep the user more engaged, such as staying on Facebook’s News Feed, then longer Task Times could be better. It really depends on what the task is. Even on Facebook’s News Feed – if the goal is to find a specific event then shorter task times might be a better outcome.
Organizations can look at either the Average Task Times for only those who were successful or they can look at the Average Task Times for all users.
Attitudinal metrics are where we ‘quantify’ qualitative data, such as appearance, loyalty, trust and usability. There are many different ‘scores’ on the market that will assign a number to attitudinal data, using various methods. Here’s an overview of the main ones…
This measures customer satisfaction, but doesn’t have the strict question limit parameters of NPS as you can ask anything from one single question to a full-length survey. Results are measured as a percentage.
Pro: unlimited customization. Con: the people who actually take the time to fill in a full-length survey are only likely to either love or hate your product.
Net Promoter Score (NPS) is a survey you can include at the end of your UX tests. NPS helps you measure loyalty based on one direct question: How likely is it that you would recommend this company/product/service/experience to a friend or colleague?
Here’s how NPS works:
The final NPS score is then calculated by subtracting the percentage of customers who are detractors from the percentage of customers who are promoters. Promoters – Detractors = NPS.
This is an 8 item questionnaire for measuring the quality of the website user experience, providing measures of usability, credibility, loyalty and appearance. You can read details about SUPR-Q at www.suprq.com
Watch me go this whole section without saying how I’m going to ‘suss this out’. You’ll be so proud of me…
For every website usability test carried out, users complete a short questionnaire and a score is derived from that. It’s on a Likert scale, which helps to ascribe a quantitative value to qualitative opinions.
Example of a Likert scale
These are the types of questions that can be asked, which are responded to by clicking on an option from strongly agree to strongly disagree:
The benefits of this measurement is that it’s very easy to administer, can be used on a small sample size and it can clearly indicate whether a feature has improved or not. However, bear in mind that the scoring system is incredibly complex, and it won’t tell you what’s wrong with your site – it merely classifies its ease of use.
Gerry McGovern gives an extensive breakdown of the method his team developed, “to measure the impact of changes on customer experience.” With TPI you ask 10-12 ‘task questions’ that are created especially for the ‘top tasks’ you want to test (these will need to be repeatable, as they’ll be asked again when running the test again in 6 – 12 months time).
For each task, the user is presented with a task question via live chat. Once they have completed the task, they answer the question. The user is then asked how confident they are in their answer. The theory is that if a task has a TPI score of 40 (out of 100), it has major issues. If you measure again in six months and nothing has been changed, the score should again result in a TPI of 40.
At UserZoom we have our own single UX metric score, called the qxScore. This is a “quality of experience” score that combines various measurements, collecting both behavioural data (such as task success, task time, page views) and attitudinal data (such as ease of use, trust and appearance) – the purpose of this is to create a single benchmarking score for your product.
This single UX score is a simple, clear and persuasive tool for communicating user research results to stakeholders, and should help with getting future buy-in.
I haven’t even remotely covered every possible UX metric here, because frankly that would take all week. What I am discovering however is that UXers have a broad range of measurements to rely on, that blend both user rating systems with qualitative feedback from usability testing.
It also depends on your own company goals, and what results your various stakeholders wish to see. The key is being clear on what is being measured and why.
Now to clear my throat, throw open the window and start bothering the neighbours again.