Two of your most popular questions I'm asked in response to my "Design Decisions" series are:
- Do these design decisions actually matter?
- How do you make your design decisions?
I'm going to teach you my framework for making micro UX decisions that I've been using across Silicon Valley for years.
But first, let's address this:
Do these design decisions actually matter?
Short answer: Yes.
Long answer: Also yes, but nowhere near as much as you think.
When choosing between minutiae like button placement or tooltip delays, most designers overthink it to the point of diminishing returns. Worse even, these become 30-minute debates during valuable meeting time.
Your role as a designer is to make your company more money (if this notion bothers you, please read The Goal by Eliyahu M. Goldratt).
Some of you need to hear that again.
And if design is a process of experimentation and decision making, then identifying "good enough" is the optimal path.
But how do we determine "good enough" in 2022?
My 3-step framework for making "good enough" design decisions
This linear process builds upon itself as more information becomes available.
It asks three questions, starting with 1 and ending at 3.
In the absence of 3, it falls back to 2. And so on.
- What does institutional knowledge say?
- What are my customers familiar with?
- What does our customer research say?
Let me elaborate:
1. What does institutional knowledge say?
This is the foundation of what we know to be true today about HCI. I look at Gestalt psychology, accessibility guidelines, and reputable research thinktanks:
- The W3 WCAG 2.1 Quick Reference
- Laws of UX: Library of known HCI psychology principles
- Nielsen Norman Group
2. What are my customers familiar with?
This relies heavily on the principle of Jakob's Law which says that people expect our product to behave like the products they frequently use. I look at operating system design guidelines, competitor teardowns, and tangential products (tools that my users are using regularly but aren't necessarily competitors - like Salesforce or Google's Chrome).
Note: It's also worth considering your current design library. Are there any patterns - good or bad - that you've already taught your customers to use? Rely on those unless you actively plan to phase them out.
3. What does our customer research say?
This relies entirely on qualitative interviews and quantitative behavioral analytics. If you have a robust research infrastructure, these answers will likely be more valuable than institutional knowledge and customer familiarity. This is your source of truth, assuming your instrumentation is rock-solid.
- Customer interviews
- Analytics (Full Story, Amplitude, GA, data science, etc)
That's how you determine "good enough", and then move on to your next priority.
Don't overthink it and be open to change as new information becomes available.