Understanding Data Correlation, Causation, & Coincidence

Correlation, Causation or Coincidence: What Do You See in Your Data?

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Key Takeaways:

Discover a few key benefits from data-informed decision making as a small business.

Gain a better understanding of data correlation, causation, and coincidence.

In this new era of AI and machine learning that we find ourselves in, data analysis continues to be an essential foundation of small business success. Not only do we have access to historical data, but we can now process real-time data to make more informed and accurate decisions as business leaders.

Data Driven Decision Making (DDDM) is becoming the norm for businesses of all sizes. Per research from Econsultancy and Google, approximately 7 in 10 leading marketers claim that data informs their business’s decisions at every level. According to a recent study from McKinsey, data-driven organizations are 23X more likely to acquire new customers and 6X as likely to retain existing ones. Per research from Forrester Consulting, advanced analytics adopters made an average additional profit of $2.0M per year.

But where exactly do you begin with data analysis? When reviewing your business’s data, it’s important that you take your time. A quick glance at the numbers, trends, and patterns isn’t enough to help you. It’s important to invest the time to understand what factors have contributed to the outcomes you’re examining. Join us as we take a minute to define correlation, causation, and coincidence when it comes to your marketing metrics.

“Data-driven organizations are 23X more likely to acquire new customers and 6X as likely to retain existing ones.”


Examining the Data for Correlation and Causality

Let’s say you are a specialty retailer – a vinyl record dealer – who wants to understand a dramatic but short-lived spike in their website and social media traffic, as well as in-store sales. Knowing what caused the spike could allow the retailer the opportunity to recreate those conditions that drove customer demand – obviously a desirable outcome.

It’s important to remember that data paints a picture of what already happened. To understand the activity spike, we need to look at the data captured when it happened, as well as the time period immediately prior.

Perhaps, as the business owner, you’re wondering if the sales spike could be related to the extra time and effort you’ve been putting into social media. Examining engagement rates does indicate that you’re connecting with more customers more effectively – but does this explain the activity spike?

These two data points – the engagement rate and the activity spike – may appear to be related, but we don’t know for sure. We have no idea if the engaged customers are the same people who are coming to your store to buy records. Sometimes people talk on social media because they enjoy talking on social media. There’s no definitive relationship between the two data points, even though both have increased. This is called correlation.

When we see correlation, we know that there may be something going on that’s worth paying attention to. Knowing that increased effort led to increased engagement is encouraging; having understood that data point, it’s time to consider how to capitalize on that engagement effectively.

But for right now, we’re interested in causation – what factors definitively led to the activity spike. Let’s suppose the vinyl record dealer used email marketing to reach their customers. If an email message that invited customers to visit the website & use a specific promo code (which is something you can track) was followed by the activity spike, we have what is called causation – a direct, definitive relationship between your action and your customers’ response.

Once causation has been identified, it can be repeated. In this instance, investing further time and energy into additional similar email campaigns would appear to be a wise decision.

“Nearly 7 in 10 leading marketers say their companies use data to support decision-making at all levels.”


Analyzing Data for Coincidence

But let’s say there was no email marketing campaign going on, nor any other causal element to be discovered in the data. What does the business owner do then?

It’s important to remember that customer behavior happens in context of a much larger world. Though it’s impossible to track all of the factors that could motivate purchasing behavior, it’s a really good exercise to try. Reviewing the activity spike with the vinyl store owner revealed that a popular musician passed away immediately before the activity spike – and delving deeper into the data showed that the majority of sales during the spike were that musician’s work. This is a coincidence – a factor entirely outside of the business owner’s control still had an impact.

As a small business, you can’t control for coincidence, even though you can certainly profit from it when the opportunity arises. And it’s important to understand that businesses operate in the real world, where the boundaries of what does and doesn’t influence customer behavior aren’t as black and white as we’d like to believe. Did the vinyl record dealer discuss the recently departed musician on social media? Perhaps – and that may have contributed to the activity spike. Had there been an email campaign, would some of those customers have used their promo code to save on the music they were going to buy in tribute? Possibly.

Data-driven marketing is not an exact science. We must do our best to remove the emotion from the analysis and avoid rushing to judgement. That being said, every day customers create more data points for us to examine. This allows for ongoing improvement in understanding and growth – if we’re willing to invest the time in understanding what we’re looking at.

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