What is a Correlation Chart and When Should You Use it?

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A correlation chart is one of the best ways to show the correlation between multiple variables or data points without sacrificing considerable space in your dashboard or report. You’ve probably seen it in Microsoft Excel, but it’s also used in modern business intelligence tools to illustrate correlation and causation.

Today, we’ll show you what correlation charts are and when and how (not) to use them for data visualization.

What is a correlation chart?

A correlation chart or a correlation matrix is a chart type used to show the relationship between multiple variables. It shows how strongly or weakly two variables are related, using a number from -1 to 1.

1 indicates that there is a perfect positive correlation (when one variable increases, the other one increases too)

0 indicates that there is no correlation at all

-1 indicates a perfect negative correlation (as one variable increases, the other one decreases)

In a correlation chart, the data is typically displayed in a matrix or grid form and each cell shows the correlation coefficient between two variables. You can also use color gradients or circles to indicate how strong the relationship between the variables is.

The best use cases for correlation charts

Correlation charts are often used in data science, statistics, and analytics, but they can just as easily be used in data visualization tools for dashboards and reports. Here are the most common use cases you can encounter.

Identifying relationships between variables

When you want to quickly determine the relationship between two or more variables in a data set, a correlation chart is an excellent choice. 

For example, you create a marketing analytics dashboard. You want to understand the relationship between variables such as ad spend, website traffic, and sales revenue. If advertising spend and sales revenue have a strong positive correlation, it means that this initiative is worth investing in.

Detecting multicollinearity in regression models

In predictive modeling, correlation charts can show data scientists if two or more predictors are highly correlated.

For example, a data scientist is building a linear regression model, and they use a correlation chart to show multicollinearity between independent variables such as age, income, and credit score in a credit risk model. This ensures that the model is accurate.

Portfolio risk management

In finance, correlation charts are commonly used to understand the movement of assets in relation to each other, which helps balance out portfolios.

For example, an investment manager can visualize the correlations between different stocks or asset classes. Two stocks can have a high positive correlation, and investing in both of them won’t reduce risk. On the other hand, investing in multiple assets with negative correlation can diversify your portfolio and lower your risk.

Exploring customer behavior in SaaS analytics

SaaS businesses can use correlation charts to understand how different aspects of product usage are related to each other, which helps improve product development and leads to a better user experience.

For example, a SaaS product manager can use correlation charts to analyze the correlation between user engagement metrics like log in frequency, feature usage, and churn rate. This can reveal that users who frequently use a certain feature are likely to churn, for example.

Discovering relationships in healthcare data

In healthcare analytics, you can use correlation charts to discover the relationships between health indicators and patient outcomes.

For example, a medical researcher can use correlation charts to visualize the relationship between variables such as cholesterol levels, blood pressure, and heart disease. A strong relation between high cholesterol and heart disease can lead the researcher to spend more time investigating this phenomenon.

When NOT to use correlation charts

These charts are great for correlation analysis and showing the different types of correlations between data. However, they’re not well suited for just about everything, including these cases.

In causal relationships: correlation charts show just that, correlation - but not causality. If you’re trying to determine cause and effect, a correlation chart can lead to misleading conclusions.

Alternatively, use a causal analysis model such as regression analysis or Granger causality tests.

Non-linear relationships: correlation charts are designed for linear relationships and continuous variables only. If the relationship between variables is non-linear, the correlation coefficient can be close to zero, giving the impression that the relationship is not there, even though it may exist.

Alternatively, use a scatter plot with a trend line to show non-linear relationships.

Too many variables: if there are too many variables (e.g., 10-15), the chart becomes too difficult to interpret.

With a large number of variables, consider using PCA (principal component analysis) or heatmaps instead. This helps show weak and strong correlations on one screen without overwhelming the reader.

Time series data: correlation charts do not account for the time component, which can lead you to miss important patterns that come up in data over time.

Instead, use a line chart or a time series plot. For example, if you want to compare revenue and marketing spend over time, a line chart with a time axis is going to be a better fit than a correlation chart.

Outliers and skewed data: if you have many outliers in your data, the correlation chart can end up being skewed, and the correlation coefficient can inflate or deflate the relationship, giving you an inaccurate representation of your data.

Instead, consider using a box plot or a scatter plot with outlier detection methods, as they are better at showing data distribution.

Top tips for using correlation charts

We’ll give you a brief tutorial on data visualization with correlation charts by showing you some of the basics for getting correlation charts right.

Scale your data before correlating: normalize or standardize your data, especially when dealing with different units. You can use Z-score normalization to do this job.

Use color gradients for clarity: color gradients can help you highlight the difference between strong, moderate, and weak correlation. You can use a diverging scale for this: blue for negative, red for positive, and white for near-zero correlations.

Annotate with correlation coefficients: include the correlation values in each cell of the matrix. Colors are helpful, but the numeric value adds more clarity and helps the reader get to the point quicker.

Be mindful of sample size: the sample size should be large enough to produce meaningful correlations. Small sample sizes can skew your data. In these cases, report or include confidence or p-values to show how statistically significant the results are. 

Group or filter variables: do this before creating a correlation chart, because too many variables make it cluttered and hard to read. Start by grouping variables into categories (e.g., demographic data, behavioral data) or filtering them to show only those variables that are highly correlated. 

Check for multicolinearity: if you have high correlations (above 0.9) between the predictor variables in your model, this could be a sign of multicolinearity. If you detect it, remove one of the highly correlated variables or use principal component analysis.

Handle missing data appropriately: remove data with missing values or impute reasonable values. Methods that can help include data removal, mean imputation, and K-nearest neighbours imputation.

Pair correlation charts with other chart types: complement your correlation charts with other visualizations such as scatter plots or heatmaps. Besides correlation, these chart types can show the actual nature of relationships between data points.

Visualize your data with Luzmo

At Luzmo, we help you add data visualizations to your product. Correlation charts are just some of the many chart types you can add to your product’s built-in, interactive dashboard and show your end-users the true value of their data. 

Thanks to a powerful API and a number of data sources, connecting Luzmo to your app is a matter of hours, not weeks or months.

Ready to give your end-users actionable insights? Grab your free trial of Luzmo today!