Q: What is Data-Driven Design?
Dear Reader,
That term "data-driven design" is thrown around a lot. Let's tackle it.
Last week, I asked some design friends what data-driven design meant. Here's what they told me:
It's true. I have seen "data-driven" projects cherry-pick data because the CEO or HiPPO (highest-paid person's opinion) didn't like the outcome.
But I've also worked with some brilliant data scientists who embody the term "data-driven" and have very high standards of rigor for data.
How should designers use data to make decisions?
There are 3 ways.
Note: "data" = both qualitative + quantitative data and focuses on customer data, both declarative and behavioral.
Data-Inspired Design
"Customer data is a source of inspiration, but intuitions make decisions."
Data is "insightful" to you, but it can come from anywhere and be cherry-picked. You prioritize a big-picture, future view. Trends, declarative data, and insights drive your team.
Example: Designers interview users periodically, and this declarative data indirectly lead them to simplify their app's information architecture. Later, the product manager noticed a drop in the numbers simultaneously as the feature launched. They suspect the customers' opinions they talked to don't match the customer base as a whole. In the end, they don't change the feature because they all agree that the change was an improvement. After all, the senior designer says that it's a best practice, and she's an expert in information architecture.
The Designer's Role:
- Gather inputs and stay up-to-date on customer trends
- Design based on intuition using any research or analytics data you can gather
- Measure your work through outputs and company goals
Decision-Making: Experience makes decisions even over data
#Strategy #Insights #Inspiration
Data-Informed Design
"Customer data helps you evaluate your design decisions."
Customer data is "pulled" by experts or gathered through usability tests to measure projects. Big-picture goals, quantitative KPIs, and qualitative feedback drive the team.
Example: You create and launch a feature based on your team's quarterly OKRs that they hope will increase the adoption of their app. After releasing the feature, your team notices a significant drop in adoption on the analytics dashboard the same day as the release. You do some usability tests to figure out where the issue is. You don't find a problem, but you decide to change a few things that users bring up in the interview.
The Designer's Role:
- Use research, testing, and analytics to adapt designs to the customer.
- Design based on past research/intuition but evaluate your work through usability and analytics.
- Measure your work indirectly through a variety of data points on customer outcomes using analytics and team KPIs
Decision-Making: Experience makes decisions unless customer data informs us that we're wrong.
#Analytics #Usability #Evaluation
Data-Driven Design
"Customer data shows you what to design next."
Design methods are seen as a valuable way to generate data. Design data de-risks ideas and enhances decision-making for the whole team. Behavioral data from experiments drive the team. Customer data is the start and end of projects because it's how you determine what to build.
Example: The last experiment you designed was a test of a new feature you want to build. You set up a Landing Page Test to see if users would give you their email address for the new feature. They didn't respond like you guessed...but in the experiment retro, your team looks at the heat map you added on Hotjar. You notice that every user that gave an email address hovered on the newsletter form at the bottom. You decide to design an experiment to remove all other forms from the page. It passes the test! So you design a prototype to test usability now that you tested the desirability.
The Designer's Role:
- Design experiments that will generate customer data and enhance decision-making for the whole team.
- Design based on learning from recent experiments and set up evaluation criteria before designing anything.
- Measure your work directly through behavioral and data and de-risk ideas in lo-fi concept tests and hi-fi A/B tests.
Decision-Making: Experiments make decisions even over experience
#Experimentation #ConceptTesting #Innovation
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Don't dishearten if you don't like where you land in this framework. Your role, team, and budget affect what you can do with data.
Build the skills to work with whatever customer data you can get hold of...qualitative and quantitative.
Until next week, get inspired, informed, or driven about data!
Jeff Humble
Designer & Co-Founder
The Fountain Institute
P.S. This Saturday, I'm giving a FREE webinar on concept tests and data-driven prototyping: Get a masterclass in designing with data→
Huge thanks goes to Adithya Jayan (AJ), Maximilian Schmidt, Maximilian's data scientist colleague, Mahdis Atabaki, Jean-Luc Momprivé, Damian Martone, Gonzalo Sanchidrian, Mohit Kishore, Gabe Ali, and Paolo Gambardella for giving their time to provide feedback on this framework.