As designers, we often rely on intuition and user research to guide our decisions. But in today's data-driven world, understanding basic statistical concepts can transform how we validate our design choices and predict their impact. I'd like to introduce you to correlation and linear regression analysis.
Merging Best Practices with Data-Informed Design
Think about the last time you redesigned a navigation menu or tweaked a checkout flow. You probably had a strong hypothesis about how these changes would affect user behavior. But how do you prove these relationships exist? This is where statistical analysis comes in.
Linear regression and correlation analysis are two of the most common (and crucial) methods for getting insights from data. Both are used as a foundation for predictions and forecasting. Correlation analysis confirms the relationship and connection between two variables. Linear regression analysis shows how much one variable affects another and how to predict, estimate, or explain its behavior.
Correlation and Linear Regression Analysis
Correlation Analysis helps us understand if there's a genuine relationship between two metrics – like whether simplified navigation actually leads to increased engagement. Linear regression takes this further by helping us predict the potential impact of our design changes before we implement them.
Correlation Analysis illustrates the degree of a relationship between two variables, i.e. how closely they are correlated.
- The result is always between -1.0 and 1.0
- 1.0 denotes the highest positive correlation
- 1.0 stands for the highest negative correlation
- 0 means no correlation
Positive correlation—an increase in X is correlated with an increase in Y. If a positive correlation is found, it could be stated as such; "An increase in installs relates to an increase in signups" or "An increase in notifications relates to an increase in daily active users."
Negative correlation (or inverse correlation)—an increase in X is correlated with a decrease in Y. If a negative correlation is found, it could be stated as such; "An increase in price is related to a decrease in the trial-to-paid rate" or "An increase in webpage time load relates to a decrease in page views."
Linear Regression Analysis takes correlation analysis further It shows how much one variable affects another. More importantly, it tells you whether you can use the pattern of one variable to predict and estimate the behavior of another.
Use cases for linear regression:
- How much do page views have to increase to improve signups by 2x?
- Is showing more recommended articles to readers increasing return visits?
- How many exercises do users have to log in to the app to see an improvement in user retention?
- If we send 3x more notifications, how much will this increase DAU?
- How many months will it take for DesignOps initiatives to affect team productivity levels?
Breaking Down the Numbers Barrier
Many designers shy away from statistics, viewing it as the domain of data scientists and analysts. However, modern tools have made these techniques surprisingly accessible. With just a spreadsheet and some basic understanding, you can:
- Validate design decisions with data
- Predict the impact of design changes
- Communicate more effectively with stakeholders using quantifiable metrics
- Identify which design elements have the strongest influence on key business metrics
Start Simple
You don't need to become a statistician to leverage these tools. Starting with basic correlation and regression analysis in spreadsheets can help you understand relationships in your design metrics – whether it's the connection between page load time and bounce rates, or how visual hierarchy affects conversion.
The ability to quantify design impact not only strengthens your decision-making but also helps you speak the language of business stakeholders. In an increasingly data-driven world, this combination of design intuition and statistical literacy is becoming invaluable.
TIPS: When and How to run the analysis
- Run a Correlation Analysis first to determine whether there is a relationship between two variables. It's the first step to understanding whether there's something worth exploring further. It's simple and quick, providing insights into potential connections that may warrant a deeper dive.
- Run a Linear Regression Analysis when you want to understand the nature of the relationship between variables. Use it when you have a specific hypothesis about how one aspect of your design influences an outcome (e.g., how the number of steps to complete a purchase affects the conversion rate).
- To Run a Correlation Analysis, collect data for the two variables you're interested in. Use our Google Sheet to calculate the correlation coefficient, which will tell you the strength and direction of the relationship.
- To Run a Linear Regression Analysis, identify your independent and dependent variables. You'll need more data, as regression can be more sensitive to variations. Using our Google Sheet, you can fit a regression model to your data, which will give you an equation that predicts the dependent variable based on the independent variable.
Cadence Tips: How often should you run this play?
- Quarterly to start, then Monthly
Here's a replay of a recent lesson at CDO School. This beginner-friendly tutorial walks you through the basics of correlation and regression analysis using spreadsheets, with practical examples you can apply to your design work immediately.
Designers: Get comfortable with Data and Statistics
