What metrics do the experts use to measure UX effectiveness?
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 then evaluated, using fair evidence rather than opinions.
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.
It’s all well and good us sitting around in our ivory tower yelling how great UX testing 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 behaviour” but then they often make the following point…
“But how can we measure that? If we run user 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.”
But UX metrics don’t have to be an intangible mystery. As you’ll see below, there are many ways to prove the value of UX research.
The trouble with UX metrics
Metrics are a difficult discussion when it comes to measuring the success, failure of shrugging indifference of your UX. Every other discipline has it made!
- You want to measure how well your blog post did: look at your traffic, see the time on page, notice how many times it has been shared, judge the quantity and quality of comments.
- You want to measure your social channels: look how many followers you have. Is there growth? Are they influential in your niche? Do they comment? Do they share? Are they entirely bots?
- You want to measure the quality of your Bakewell Tart: did I eat the whole damn thing? Probably, but that’s not a testament to how good it is. I’ll often eat an entire Bakewell Tart with the same ease that I take a breath. Did I demand that you make me another one? Yes! Now that’s a quality Bakewell Tart.
- You want to measure the changes made to the order of the categories in your main menu: well it definitely looks better to you! Have you run some more user testing to see if people are still struggling with it? Perhaps traffic from the homepage to those categories has improved, but there’s no guarantee it’s because of the changes.
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.
And this makes creating metrics around UX improvements difficult. There are other issues too, as UX designer Peter Hornsby points out:
“Part of the issue is that UX effectiveness is hard to measure. It’s a good plan to have UX-specific requirements in a project, but often these can be something that doesn’t necessarily correlate with UX effectiveness. For instance, if someone says ‘We want users to use our app more!’ then making the UX less effective will meet that metric: make it more difficult to do stuff, users take longer to do it, and spend longer on the app. UX metrics can therefore be ‘gamed’ like any other metric.”
But there are ways to measure UX. Many of them are better than others, many of them will only be very specific to a certain company or industry – but they are out there.
What UX metrics do the experts use?
To help with my investigation, I took to the streets of social media, to ask our cadre of experts their opinions on measuring UX.
Conversion, AOV, sign ups etc all indicators of #UX success. SUS in user research is useful for measuring complex workflows and tools.
— Georgia Rakusen (@G_Rak) July 17, 2017
The following metrics have been recommended by our lovely UX community, and many of their individual opinions are peppered throughout…
AOV: Average Order Value
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 nngroup suggests:
“The conversion rate measures what happens once people are at 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.
CSAT: Customer Satisfaction Score
This measures customer satisfaction, but doesn’t have the strict question limit parameters of NPS or SUPR-Q as you can ask anything from one single question to a full length survey. Results are measured as a percentage. Pro: unlimited customisation. 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.
NPS: Net Promoter Score
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?
As I previously described in an article for Econsultancy, here’s how NPS works:
- Those who respond with a score of 9 or 10 are called ‘promoters’. Loyal enthusiasts who recommend your services, products or brand to other people and will continue to buy from you in the future.
- Those who respond with a score of 7 or 8 are called ‘passive’. They are happy with your service but have no real loyalty to you therefore will likely stray.
- Finally there are the ‘detractors’, customers who responded with a score of 0 to 6. These are unhappy people who don’t want to see your product ever again.
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.
SUPR-Q: Standardized User Experience Percentile Rank Questionnaire
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
SUS: System Usability Scale
Watch me go this whole section without saying how I’m going to ‘suss this out’. You’ll be so proud of me…
SUS (System Usability Scale) was also mentioned by Chris Compston, a senior consultant at ThoughtWorks:
This is handy as a holistic barometer of usability effectiveness – ‘System Usability Scale (SUS)’
— Chris Compston (@ndxcc) July 17, 2017
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.
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:
- I think that I would like to use this website frequently
- I found the website unnecessarily complex
- I thought the website was easy to use
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 ‘system’ has improved or not.
There are important things to keep in mind though when using SUS. According to Usability.gov:
- The scoring system is complex
- As the scores are on a scale of 0-100, there’s a temptation to interpret them as percentages – they’re not. Don’t do this. It’s wrong. Here’s what actually happens to the scores…
- The participant’s scores for each question are converted to a new number, added together and then multiplied by 2.5 to convert the original scores of 0-40 to 0-100. Though the scores are 0-100, these are not percentages and should be considered only in terms of their percentile ranking. Based on research, a SUS score above a 68 would be considered above average and anything below 68 is below average, however the best way to interpret your results involves “normalizing” the scores to produce a percentile ranking #MathsFun
- SUS won’t tell you what’s wrong with the site – it merely classifies its ease of use, which is fine for the purpose of measuring the improvements of a specific feature or journey. User testing will tell you how to improve.
TPI: Task Performance Indicator
Georgia Rakusen also pointed us towards TPI (Task Performance Indictator) as potential UX metric, but then heavily caveated (read as ranted) that it is NOT a suitable method.
But first: what is TPI?
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).
The number of test participants is between 13 – 18, larger than you would normally find in user testing, as McGovern believes that “reliable and stable patterns aren’t apparent” until you’re testing with this many participants and that results stabilize to leave a “reliable baseline TPI metric.”
For each task, the user is presented with a task question via live chat. Once they have completed the task, they answer to the question. The user is then asked how confident they are in their answer.
TPI takes into account:
- Target Time: how long it should take to complete the task under best practice conditions.
- Time out: the person takes longer than 5 minutes.
- Confidence: At the end of each task, people are asked how confident they are.
- Minor wrong: the person is unsure; their answer is almost correct
- Disaster: the person has high confidence, but the wrong result
- Gives up: the person gives up on the task.
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.
Georgia Rakusen has her reservations about this measurement:
“If your company is bought into qualitative research, you don’t need a number to prove your design. [With TPI] every session needs to be moderated, so often [which is] expensive and time-consuming. If a user doesn’t speak, you don’t understand the why, this leads to doubling up on research efforts to capture quant and qual. This doesn’t work for ‘Top Tasks’, which involve users making creative choices, as time on task becomes skewed. Test with 15 users and get one score. Test with another 15 users and get a different score. See the problem here? Pushing something live to an AB test and measuring success at high volumes will give us much greater certainty. FIN.”
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 user testing.
I asked for responses on our Slack community channel (which you should totally join btw) and here Peter Hornsby revealed his approach:
“It depends on the system, but generally a mix: [I use] feedback that people in contact with users receive (e.g. client support teams), direct interaction with users (large-scale feedback like surveys, with questions informed by qualitative stuff like interviews), and of course direct user journey research.”
It also depends on your own company goals, and what results your various stakeholders wish to see. On that note, let’s end with this important point to remember from Peter:
“The key for me is being clear on what is being measured and why – and challenging this if it’s not meaningful.”
Now to clear my throat, throw open the window and start bothering the neighbours again.
For an in-depth and entertaining guide to getting started with user research, read our brand new comprehensive e-book: User Experience Research 101.
Christopher is the Content Marketing Manager for EMEA, which basically means the skipper of the good ship ‘UserZoom blog’. So far his requests for changing its name to the ‘USS-erzoom Blog’ have been rightfully denied. In his spare time, Christopher is a filmmaker and the editor of wayward pop culture site Methods Unsound. He used to be the deputy editor of Econsultancy, editor of Search Engine Watch, staff writer for ClickZ and features editor of CMO.com.
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