How to Use Correlation in Business Decision Making

Correlation is an important concept that can be used to analyse data sets and assist business leaders in gaining useful insights into the relationships between business outcomes.

In business, these relationships can be used for decision-making tasks across all functional areas. For example, a business could analyse trends in sales over time to see whether lower pricing has consistently occurred in the same period as higher sales numbers. If a pattern is noticed, the business may have a better indication for the optimal pricing level to achieve their desired sales amounts.

Correlation is about repeated patterns – it takes more than one isolated instance to create a correlation. When conducting a correlation analysis, the more data points that are available, the more accurate the results of the analysis will be. It is important to have sufficient data before relying on correlations to make business decisions

Correlation Strength

The strength of a linear correlation between two variables is determined by the degree to which they move together in a dataset. Using Pearson's Correlation Coefficient, correlation can be any value between -1 and 1, with the weaker correlations near 0 and stronger correlations at the tail ends near 1 or -1. Weaker correlation values (close to 0) have less predictive power, but can still have valuable business insights.

If analysis determines that correlation strength is weak between two variables, it doesn't mean the analysis is useless! There may be an opportunity for a company to make time or cost saving improvements. For example, a company notices that the relationship between high TV ad marketing spend and higher sales is not highly correlated. In this instance, the company may make the decision to reduce TV ad marketing, resulting in cost savings.

Distinguishing positive and negative correlations

Correlation can be divided into two main types: positive and negative correlations. A positive correlation means when one variable increases, the other also increases. This type of relationship is often seen in successful customer buying trends and healthy marketing performance. For example, a business may see a positive correlation between their promotional campaigns and sales, showing that when they increase their promotional efforts, sales go up during the same time period.

Negative correlations indicate that when one variable increases, another tends to decrease. One example of this could be that an increase in customer service employees correlated with a decrease in customer complaints. This relationship may help a business assess the success of hiring the new employees.

Important caveats to keep in mind

As the age-old saying goes: correlation is not causation. Businesses need to understand the difference to avoid falling into pitfalls with decision-making.

Correlation does not necessarily imply causation; while there may be an apparent connection between two variables in a correlation analysis, it does not necessarily mean that one causes the other.

In order for an individual or business to draw meaningful conclusions from their data, they must take into account any other possible factors that could have caused the observed correlation such as external influences or seasonal trends. Additionally, further research should be conducted in order to understand the underlying reasons behind any observed correlations before making decisions based on them.

Businesses must also keep in mind that just because two variables are historically correlated does not ensure that the relationship will be the same in the future. Always consider outside influences that may have an impact on the relationship in the future.

The benefits of correlation in decision-making

Correlation analysis uses historical data to help inform decisions such as those related to marketing campaigns, product offerings, and workplace efficiencies. It is critical for businesses to use both qualitative and quantitative strategies for decision-making, and correlation is an efficient way of quantitatively assessing the performance of current and historical initiatives to see what works well.

Understanding the impact of business initiatives on desired outcomes can help companies identify trends, uncover hidden opportunities, refine strategies, allocate resources more efficiently, and make better decisions overall.

Real Example of Applying Correlation in Business

Let’s say you’re the founder and CEO of a small marketing agency and you’re tasked with increasing website traffic for a client.

The client implemented a new social media strategy three months ago to achieve this, by creating and posting content that directly links to their website. They have also increased their use of hashtags, optimised their website content and ran a 30% off promotional sale of their product.

To measure the impact of this strategy, your marketing agency can use correlation analysis over a 6-month period, for the 3 months before the new strategy was implemented and 3 months into implementation.

Data Collection Method

In this example, your marketing agency would use social media engagement (the independent variable) and website traffic figures (the dependent variable) to get a full picture of your client’s progress.

Metrics such as likes, shares, comments, click-through rates, impressions, and engagement make up your data set for tracking the client’s social media posts.

Your agency would also monitor the number of website visits, page views, and time spent on site for the website data.

Correlation Analysis

To accurately examine the correlation analysis, your marketing agency would create a scatter plot with social media engagement on the x-axis and website traffic on the y-axis. Each data point represents a specific day or week.

Then, you’d need to calculate the correlation coefficient to determine the strength and direction of the relationship between special media engagement and website traffic.

If higher social media engagement leads to increased website traffic than the two variables are positively correlated.

If somehow this new social media strategy leads to lower website traffic (unlikely but still a possibility) then it results in a negative correlation.

Or if there was no clear relationship between the two variables then there is zero correlation.

Interpreting the Data

If a high positive correlation is found, the agency can confidently conclude that the client’s social media strategy is effectively driving website traffic. They can allocate more resources to social media marketing to further boost website visits.

If a weak or no correlation is found, the agency may need to re-evaluate its social media strategy. They might need to experiment with different content types, posting frequencies, or platforms to improve engagement and drive traffic.

There are a few considerations that should also be kept in mind in this example.

Although a strong correlation between social media engagement and website traffic suggests that social media is likely driving traffic, other factors like SEO, paid advertising, and email marketing (if used) may also influence website visits. This is important for the marketing agency to keep in mind so that they can gain a more comprehensive understanding of the factors driving traffic to the client's website.

Examples of When Correlation is Not Causation in Business

Now imagine that you are part of a sales team selling a new pharmaceutical supplement to prevent vitamin deficiency. You operate across the UK market.

You and your team notice that whenever your advertising budget increases, sales figures also tend to rise.

It’s easy to conclude that increased ad spend directly causes higher sales. However, this could be caused by the lurking ‘third variable’.

The Third Variable

In many cases, a third variable could be influencing both ad spend and sales.

For instance, a strong economic upturn could be driving both increased advertising and increased consumer spending.

In this example, let’s say that the reason for the increased demand for the team’s supplement is the increased public health awareness of vitamin deficiency in the UK and how it can lead to poorer health outcomes and a higher risk of disease.   

Although rather than the ad spend itself, the increased public health messaging by the UK government is the primary driver of sales.

The Importance of Critical Thinking

To avoid falling into the trap of correlation-causation fallacy, businesses using correlation in decision-making should:

• Consider Multiple Factors: Look beyond simple correlations and consider other potential influencing variables.
• Conduct Controlled Experiments: If possible, design experiments to isolate the impact of specific variables.
• Use Statistical Techniques: Employ statistical methods to control for confounding variables and assess the strength of causal relationships.
• Consult with Data Analysts: Seek expert advice to analyze data and identify potential causal relationships.

By understanding the limitations of correlation and applying critical thinking, businesses can make more informed decisions and drive better results.