In mobile marketing, data-driven marketing and sales approaches provide you with in-depth insights on why users keep using apps or uninstall them. You can use this to optimize your campaigns and drive more traffic, conversions, and sales. 

However, you can misinterpret data and make misguided decisions when you don’t understand the difference between causation and correlation. It can lead to resource wastage and failure of your marketing strategies. So, let’s understand the concept of correlation vs causation and how you can implement them in mobile marketing.

What Does Correlation Mean?

Correlation can be defined as a term in statistics that refers to the degree of association between two random variables. In marketing, correlation can be used to measure the connection between two things. For instance, by using correlation, you can compare the number of emails sent to sales revenue. 

It is important to note that the two factors in comparison do not mean that one factor is happening because of the other. In simple terms, the number of emails is not equivalent to the revenue. With correlation, you can compare and identify the impact of one factor on the other. 

Types of Correlation

The three types of correlation are: 

  • Positive Correlation: It demonstrates the relationship between two variables where the value of both variables increases or decreases concurrently. For example, an increase in education positively affects income levels, therefore, both variables increase.
  • Negative Correlation: It depicts a relationship between two variables where the value of one variable increases whereas the other decreases. For instance, the increase in green cars has decreased the rate of accidents. 
  • No Correlation: In this type, two variables are completely unrelated. For example, there is no correlation between scoring high in university and salary. 

What is Causation?

Like correlation, causation is also a relationship between two variables. However, causation occurs when one thing directly influences another, causing a specific effect. Imagine flipping a switch- it directly causes the light to turn on. That direct connection is causation, also known as causality. 

This causes-and-effect link can either be positive or negative. In a positive causal link, if one factor goes up or down, it directly impacts the other. For example, an increase in study time typically leads to scoring high on tests. Therefore, when study time goes up, test scores also rise. 

On the flip side, a negative causal link operates differently. If one factor goes up, it causes the other to decrease or vice versa. For example, less usage of a mobile app can lead to users uninstalling it after a period. 

Why Does Correlation And Causation Difference Matter?

Having a thorough grasp of the difference between causation and correlation can make a huge difference, especially when making marketing decisions. If you are wondering if the monthly active users for your app have increased because of your recent app store optimization efforts, you can test this to identify if the cause is correlated or causation. 

However, correlation and causation are often misunderstood. Just because two things seem related does not mean one causes the other. For instance, consider the link between rainfall and umbrella sales. When it rains, umbrella sales go up but umbrellas don’t cause the rain, even though their sales are linked to rainfall. 

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Similarly, the correlation between a student’s attendance and their grades doesn’t mean attendance directly causes better grades. While students with better attendance often have higher grades, attendance itself does not cause the grades to go up. 

Here, other factors like studying or the interest in the subject also matter. Therefore, it can be identified that correlation does not imply causation which you need to keep in mind while making decisions based on these connections.

Why Does Correlation Not Equal Causation?

Understanding causation vs correlation difference is vital, especially in fields like marketing where assumptions can lead to misguided strategies. It is important to keep in mind that just because two things appear related does not mean one causes the other. 

Consider a scenario where the two variables are social media posts and product sales. An increase in social media activity might coincide with higher sales, but it does not necessarily mean the posts have caused a spike in sales. Other factors like promotional events or seasonal demand, could influence both. 

Therefore, it can be stated that correlation is not causation. Understanding the different reasons that can impact a factor can help you avoid assuming causation when it’s a correlation. 

  • Third Variable (or Confounding Variable)

The misunderstanding in thinking that the variables are dependent often stems from overlooking the third variable. For instance, the connection between ice cream sales and sunscreen purchases increases in the summer. It’s not that one causes the other, but rather both are impacted by a third variable, which is warm weather. 

  • Directionality Issues

Directionality issues also lead to causation. Let’s take an example of two variables- stress and sleep issues. So, is stress causing sleep issues, or are sleep issues leading to more stress? Understanding these complexities is crucial to unraveling the true relationships between variables. This can help in avoiding assumptions of causation that are based solely on correlation. 

How to Identify if Something’s a Coincidence or a Causality?

In order to identify if your data points towards correlation and causality, you can use five methods, also called design of experiments.

  • Randomized and Experimental Study

In experimental design, there is a control group and an experimental group, both with identical conditions but only one independent variable is tested. The advantage of this method is that by randomly assigning people, you avoid experimental bias, where certain outcomes are favored over others. 

Imagine testing a new feature in a social media app by randomly assigning users where to two groups- one uses the updated version whereas the other continues to use the old one. If the group using the new version engages more, it suggests true causal relationships between the feature and increased user engagement. 

  • Quasi-Experimental Study

When randomization is not possible, a quasi-experimental study compares groups without random assignment. For instance, in beta testing for an app, users who volunteer for new feature trials aren’t randomly selected. 

Testing a feature among voluntary beta testers to compare their engagement levels might show potential effects. However, without randomization, establishing causation becomes more challenging as you cannot be sure if the results are because of the variable or nuisance variables brought about by the absence of randomization. To conduct a quasi-experimental study, researchers may use surveys, interviews, or observational notes, which can also complicate the data analysis process. 

  • Correlational Study
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This method examines the relationship between two variables but does not imply causation. For example, analyzing app usage data to find a correlation between the frequency of in-app notifications and user engagement may reveal a relationship. Yet, it does not provide that more notifications directly caused increased user engagement. 

  • Single-Subject Study

This method focuses on individual cases rather than groups. For example, testing a new app feature repeatedly with one user. You can use this method to compare the performance between the old and new versions to gain insights into the particular user experience. However, drawing general conclusions about all users solely based on one user’s experience is not feasible. 

  • Stories

While anecdotes or stories can indicate issues, they do not provide conclusive evidence of causation. For instance, if users report app crashes after an update, these stories raise concerns but alone cannot establish a clear-and-effect relationship between the update and the crashes.

All the experimental design methods offer different levels of evidence to understand potential causative relationships, but each comes with its limitations when it comes to determining true causation. 

How Does Correlation vs Causation Affect Your Business?

Understanding the intricacies of correlation and causation is pivotal in the realm of data-driven business strategies. It particularly impacts modern sales and marketing campaigns. These concepts wield immense power in deciphering patterns within your business data, yet distinguishing the two is crucial for insightful decision-making. 

Correlation vs Causation in Marketing

In the dynamic landscape of digital marketing, agility reigns supreme. Marketers constantly tweak strategies and make adjustments. Yet, unraveling causation from correlation can be like navigating a maze. 

Take email marketing as an example. Here, you can adjust the subject line of the email and it can lead to an increase in the open rates. This raises questions if the boost is solely because of the modified subject line or is influenced by factors like timing, day of the week, or the audience receiving the email. 

The challenge lies in disentangling multiple variables impacting the outcome. To identify true causation, you need to perform meticulous testing- control variables, scrutinize one change at a time, and gather substantial data to derive statistically significant results. 

However, in a rush for progress, marketers often grapple with time constraints, struggling to maintain precision in their experiments. This dilemma poses a pressing question- how do marketers strike a balance between agility and precision in decoding the cause-and-effect dynamics of the campaigns? Let’s delve deeper into understanding this dilemma. 

Causality vs Correlation in Sales

When it comes to sales, the quest for optimization in the era of Big Data has become the norm. Unraveling external influences impacting sales holds the key to effective planning and strategic measures. 

Let’s consider a scenario where a pricing tweak appears to be linked with a surge in sales. Imagine you are selling snow boots and escalating costs require you to increase the price. Surprisingly, sales soar consistently month after month. 

At first glance, it might seem that the price hike triggered the sales surge. But this snapshot view obscures the broader context, factors like seasonal trends or the continuity of your marketing efforts and product availability demand attention, among a variety of other reasons. 

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Similar to the intricate web of factors influencing marketing, sales outcomes are a mesh of multiple elements. While the pursuit of refining the sales process is constant, the intricacies dictate a cautious approach. This proves that causation is elusive and hastily assuming a direct cause-and-effect relationship can lead to misconceptions and flawed decisions. 

How to Implement Correlation and Causation in Mobile Marketing?

Understanding the impact of correlation causation in mobile marketing is necessary for developing strategies. Here’s how you can employ the causality and correlation concepts in your business: 

  • Confirm Results Through Replication

Smart companies conduct controlled experiments to isolate causes. Consider a mobile app developer running ad campaigns on multiple platforms. To assess the impact of each platform, they might systematically pause advertising on one platform while keeping the others active. 

This isolation helps in determining whether a sudden spike in app downloads is due to a specific platform or it’s influenced by other marketing channels. Therefore, correlation and causation can help one to confirm the origins of the traffic boost on a particular channel. 

  • Analyze the Impact Across Different Channels 

Marketers often confuse the influence of different channels on outcomes. It’s essential to review performance across all channels and consider both correlation and causation in statistics to avoid drawing mistaken conclusions. 

For example, a retail app might advertise on social media, and conduct email campaigns, as well as in-app promotions. Monitoring user-engaging metrics like conversions and click-through rates across all these channels can help in identifying the actual impact of each channel on app installs or purchases. Analyzing these correlations also helps in allocating resources effectively.

  • Steer Clear of Biases

Correlation might lead to false assumptions about a channel’s effectiveness. This is why it’s critical to avoid confirmation bias by using quantitative data to confirm hunches. Interpreting data objectively helps in crafting campaigns based on factual insights, avoiding misinterpretation.

Suppose a fitness app targets young adults for a new workout feature. Sending out a survey to users who have tried the feature may yield positive responses due to an assumed bias. However, the app can carefully analyze data to ensure these responses aren’t just about confirming the initial expectations but reflect the genuine user experience, therefore, avoiding confirmation bias in their decision-making process. 

  • Conduct A/B Testing

In mobile marketing, you can also leverage A/B testing to determine correlation from causation. For example, noticing a correlation between push notification frequency and app engagement might lead to the hypothesis that increased notifications caused higher user engagement. 

To confirm causation, A/B testing can be employed with varied notification frequencies to different user segments. Analyzing the resulting engagement rates allows marketers to determine if frequency directly impacts user engagement, providing insights for strategic decisions.   


Thus, causation usually implies correlation, however, the opposite might not always be true. Due to this, knowing the difference between correlation and causation in statistics can help marketers make informed decisions based on evidence-based insights. Focus on balancing causation correlation to develop impacting marketing strategies for your business.