Transmitting user empathy via data

Conference planning, there's still ~3d left on a discord poll on people's preferred weekday for having the online event. If you have an opinion, please hop on to contribute. Otherwise, please join the discord if you'd like to help with various small conference related tasks.

As has most weeks been lately, this one has been another long and exhausting one. One contributing factor was how my main desktop computer has been acting unstable when I push it to do intensive tasks like play a demanding game or edit photos in Lightroom. It had been acting up off and on since we moved and it frustratingly left little evidence as to why it was acting up. Now, after a couple of days of diagnosing the issue, installing drivers, updating BIOs, experimenting with lots of unexplained items in various system settings, I think I've isolated that the RAM sticks I might be the cause of the random freezing or hard crashing. We will find out if I'm right in a few days...

Diagnosing technical issues like defective RAM is a skill that takes a decent amount of stubborn tenacity mixed with a certain amount of technical knowledge. I didn't really expect that these skills applied to my daily work life as a UX researcher, but if you've got the patience to figure out why your computer keeps crashing, you've at least got the patience to hammer away at testing some cloud infrastructure features to figure out how to make a new, hard-to-use feature work while writing a report to explain why things are similarly painful for end users.

In user research work, one of the many goals we have is to have our stakeholders learn some modicum of empathy for the customers they are providing goods and services to. When Engineering and Product teams have a clear picture of the pain and frustration their products are instilling in customers, they are usually much more motivated to fix issues that they initially think are "low priority".

People who do qualitative research of course can learn about customer issues and share things that increase customer empathy within the team. But people are often surprised when quantitative UX researchers want in on the fun too.

Building empathy with quantitative methods

The tough part about getting stakeholders to develop empathy for customers with numbers is the fact that people tend to view charts and numbers dispassionately. If you report that the 95%tile wait time for a UI operation is 2 minutes, it might not register in their mind just how utterly painful it is to our users. They need to contextualize the pain in order to understand that.

To put it another way, waiting "2 minutes" can sound fine on paper until you mention that prior research has shown that users start getting frustrated after just a handful of seconds. Those same frustrated users may leave and never become a long term customer. Some more savvy stakeholders who have seen their share of UI disasters may the wait time may be too long already, but a significant portion of people will not have the context to make that judgement call.

But we can't realistically provide "extra context" to every single data point we talk about. Not everything presented requires that much attention. Sometimes there's multiple context frames you can place around a given metric like for example splitting the wait time statistics by whether the customer is a new customer or a large existing customer may help people care more. End of the day, providing context means that we aren't some neutral figure "just showing the facts". The context we provide can potentially sway huge decisions. This is where having the specific knowledge becomes important. By holding our own viewpoints on what is important enough to share and deliberately build empathy for, we can use our data to make a point.

For example, if teams think that a 2 minute, or even 5 minute wait times are accessible when other signals are saying they're not, it is potentially possible to find a way to encourage those people to go and experience the 2 minute wait themselves. But failing such direct experiences (which can be highly effective) the best we can do is find context to provide that guides the users into processing the data 'correctly'.

That's where my years of PC debugging came in handy at work. I know enough to be dangerous to myself and other machines, so I have an opinion that leads to providing a strong viewpoint. That opinion guides what context I will include to make my point. You could say that my own empathy for the user experience provides the necessary transformations to show a version of the world that highlights those same issues.

When you sit down and really write it out, it turns out that there's a huge amount of subjectivity involved. Even if I can't go so far as to make a given metric say the opposite thing from the most obvious interpretation, there's a lot of ways to establish different degrees of difference that could affect what kind of decision gets made in the end. It's something I find I have to remind folks new to the industry to think about because in their mind, the interpretation of a metric is self-evident when all readers wind up making their own interpretations that actually don't get the desired point across.

And what happens if you're working in a field where you don't have direct empathy knowledge? Well, that's when you go specifically out of your way to do research to figure out what context is needed. Then it'll become your own empathy knowledge.

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About this newsletter

I’m Randy Au, Quantitative UX researcher, former data analyst, and general-purpose data and tech nerd. Counting Stuff is a weekly newsletter about the less-than-sexy aspects of data science, UX research and tech. With some excursions into other fun topics.

All photos/drawings used are taken/created by Randy unless otherwise credited.

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