Causal Machine Learning in Marketing

This article provides a case study that demonstrates how we can leverage causal machine learning for better decision-making in marketing. Understanding cause and effect is crucial in the business world. When it comes to pricing, for example, it is important to know how customers change their buying behavior when prices are adjusted. And to figure out if a marketing campaign is worth continuing, we need to understand if it actually has an impact on the KPIs we care about. It may seem obvious that understanding cause and effect is crucial when addressing these kinds of questions. But in reality, people often confuse causation with mere correlation, which can lead to costly mistakes in decision-making. Let’s look at an example that illustrates this point. <h2>The costly mistake of confusing correlation and causation</h2> Imagine an ice cream parlor that decides to advertise in the local newspaper during the summer. After placing the ads, the owner notices an increase in sales and concludes that the ad campaign was a huge success. After all, sales were much higher during the campaign compared to the rest of the year. Thus, the owner plans to spend more money on newspaper ads. However, what the owner does not realize is that ice cream sales are always higher in the summer, regardless of any ads. Besides, hardly anyone reads the local newspaper anymore, so the ads did not really make a difference. In statistical terms, the owner confused correlation and causation. The ad campaign was correlated with higher sales because it took place in summer. But it did not actually cause the increase. This confusion leads the owner to make the expensive decision to keep running the ad campaign. Although this is only a stylized example, it highlights just how crucial it is to understand causal relationships and not confuse them with plain correlation when making business decisions. <a href="https://medium.com/@heinrichkoegel/causal-machine-learning-in-marketing-12dcd91ec24e">Read More</a>