Ever wondered if higher customer satisfaction leads to more repeat purchases? Or if increased marketing spend really drives more sales? Correlation analysis helps answer these questions by measuring the strength and direction of relationships between two variables.
By providing a clear perspective on which factors impact your business the most, you can allocate resources more effectively, reduce risks, and uncover hidden opportunities for growth.
In this blog, we’ll break down what correlation analysis is, how to calculate it, and—most importantly—how to interpret the results. Let’s dive in!
Correlation analysis in market research is a statistical method used to measure the strength and direction of relationships between two or more variables. In simple terms, it helps uncover patterns within a dataset.
At its core, correlation analysis identifies how variables are related. Using specific formulas, the result is a numerical value between -1 and +1.
For example, let’s examine the relationship between Variable A and Variable B:
While this type of advanced analytic technique typically examines two variables at a time, you can analyze multiple variables simultaneously using a correlation matrix, which presents them in both rows and columns.
The Drive Research team explores more into the definition of correlation analysis in the video below.
The Pearson Correlation Coefficient (r) measures the strength and direction of the linear relationship between two continuous variables.
It assumes that both variables are normally distributed, have a linear relationship, and are measured on an interval or ratio scale.
The coefficient ranges from -1 to +1:
This coefficient is particularly useful for understanding how changes in one variable relate to changes in another.
Spearman’s Rank Correlation Coefficient (ρ or rs) measures the strength and direction of the monotonic relationship between two ranked variables.
It assumes the data can be ranked and that the relationship between the variables is monotonic. A monotonic relationship consistently increases or decreases, but is not necessarily linear.
The coefficient ranges from -1 to +1:
Spearman’s coefficient is useful when data do not meet the assumptions of Pearson correlation, particularly with ordinal data or non-linear relationships.
This table provides a clear, side-by-side comparison of the two correlation methods, making it easier to understand when and why you would use each one.
| Feature | Pearson Coefficient | Spearman’s Coefficient |
|---|---|---|
| Type of Relationship | Linear (assumes a straight-line relationship) | Monotonic (consistent increase or decrease, not necessarily linear) |
| Data Type | Continuous, interval or ratio scale | Ordinal or ranked variables |
| Best For | Measuring linear relationships between continuous variables | Measuring relationships in ordinal data or non-linear relationships |
| Example Use Case | Analyzing the relationship between marketing spend and sales revenue | Studying the correlation between employee satisfaction ranks and employee retention |
Before you can analyze the correlation between two variables, you’ll first need to conduct an online survey. This process includes designing, programming, and fielding the survey. Once completed, the results are used to determine correlation strength scores.
In our experience, we commonly apply correlation analysis in customer satisfaction surveys, employee surveys, customer experience (CX) programs, or market surveys.
These surveys typically contain a variety of questions, making them ideal for use as variables in advanced analysis techniques.
For more context, here is the process our online survey agency follows to measure correlation.
The first step in conducting a correlation analysis in market research is designing your survey. Be sure to plan your questions in advance, keeping in mind the data you need for the analysis.
Focus on questions that yield numerical or ordinal data. These might include:
Think of metrics such as:
Once your survey is finalized, it’s time to program and test it to ensure the questions are functioning correctly. This step is crucial, as mislabeled scales or improper data validation can compromise the data used for correlation analysis.
Use our online survey testing checklist to ensure everything is in order before launching your questionnaire.
After thorough testing, proceed with launching the survey to your targeted respondents.
Once you’ve collected enough responses, clean the survey data to maintain its integrity before running the correlation analysis.
As a reminder, the two most common methods for running a correlation are:
Most data analysis software offers built-in tools to automatically run correlation analysis once the inputs are entered. For example, you can easily run a correlation using spreadsheet software like Microsoft Excel.
Here’s a helpful video that walks you through the process of calculating a correlation coefficient using Excel.
Correlation coefficients range from -1 to +1, where the value indicates the strength and direction of the relationship between two variables.
A value of 0 signifies no correlation, while +1 or -1 represents a perfect positive or negative correlation, respectively. The higher the coefficient (closer to +1 or -1), the stronger the relationship between the two variables.
All correlation strength scores and classifications are outlined below.
A coefficient in this range indicates an almost perfect relationship between the two variables. As one variable changes, the other variable changes in a predictable and consistent way. For example, a strong positive correlation could exist between height and shoe size.
Variables in this range are strongly related, but the relationship is not perfect. There is a significant association, and changes in one variable tend to be associated with changes in the other. An example might be the relationship between advertising spend and sales revenue.
A moderate correlation suggests a noticeable relationship, though other factors might also influence the variables. For instance, there could be a moderate correlation between customer satisfaction and brand loyalty, but it’s not always a direct cause-and-effect relationship.
A weak correlation indicates that there is little to no meaningful relationship between the two variables. While changes in one variable may slightly affect the other, the connection is weak and unreliable. For example, age and preferred color might have a weak correlation in certain consumer studies.
Employee surveys are a great example of how correlation analysis is used to identify relationships between different factors and overall employee satisfaction.
By analyzing how various independent variables (such as salary or benefits) impact a dependent variable (such as employee satisfaction or eNPS), employee survey vendors can gain valuable insights to improve employee engagement and retention.
Let’s consider a common employee net promoter score (eNPS) question: “How likely are you to recommend working at our company to a friend or family member?” This question is typically answered on a 1-10 scale, where 1 reflects “not at all likely” and 10 reflects “very likely.”
The resulting eNPS score serves as the dependent variable in this analysis.
To uncover how different organizational factors impact eNPS, we include additional survey questions to assess employee satisfaction with various factors, such as salary, benefits, training, and diversity & inclusion. These factors will be the independent variables.
Correlation analysis will help determine which of these independent variables have the strongest relationship with eNPS. For instance, if salary and benefits have a correlation coefficient of 0.6, it indicates a strong correlation with eNPS—suggesting that higher satisfaction with salary and benefits is linked to a higher likelihood of recommending the company.
Below is an example of a correlation matrix showing the relationship between eNPS and various organizational factors:
There are several reasons to consider running a correlation analysis in your next market research study.
Get more from your data
For one, planning a correlation analysis motivates market researchers to ask better questions in the survey.
Knowing many variables will be examined during the analysis, researchers will spend more time thinking through all the most important and relevant data that should be collected.
Make more informed decisions
Once you have the data, the correlation analysis helps you identify which variables have the strongest relationships.
Unforeseen negative or positive correlations may help businesses make better-informed decisions.
Even though correlation analysis results are not a great predictor themselves, they can still inform future qualitative or quantitative research.
For instance, you may discover a significant pattern between variables that inspires additional research.
A great counterpart to regression analysis
Correlation analysis also nicely leads to regression analysis. By comparison, regression analysis tells you what Variable A might look like based on a particular value of Variable B.
In other words, correlation tells you there is a relationship, but regression shows you what that relationship looks like.
Correlation analysis is useful to understand how variables interact with one another.
That said, pitfalls exist and have to be looked out for if you choose to run the survey in-house. Drawbacks to measuring correlation include:
Coincidences within the results
One of those biggest pitfalls is you may get a result that shows a strong correlation, either negative or positive.
Take customer satisfaction, for example. Through the analysis, you may find customer experience is highly graded and correlates strongly with overall satisfaction.
To say that one is directly causing the other could be faulty and should be carefully considered.
There are many factors at play that could just be a coincidence when reviewing correlation analysis statistics, and it’s essential to make conclusions within the scope of reason.
Correlation is not causation
Only use correlation analysis if you understand and can explain to a client that correlation is not causation.
It is tempting to jump to the conclusion that two variables have a direct result on each other, but this analysis is meant for identifying connections, not predicting them.
That said, when there is an interest in discovering relationships between two or more variables, correlation analysis is an excellent fit in a market research project.
Correlation analysis in research allows for a deeper look into variables within a business or industry.
To assure 100% confidence, we recommend partnering with a market research company like Drive Research.
Our team of experts has years of experience using correlation analysis to analyze feedback from our client’s employees, customers, and other audiences.
Want to include correlation analysis in your next research study? Contact Drive Research for a quote.
