How machine learning enhances customer segmentation


One of the key challenges that marketing teams must solve is allocating their resources in a way that minimizes “cost per acquisition” (CPA) and increases return on investment. This is possible through segmentation, the process of dividing customers into different groups based on their behavior or characteristics.

Customer segmentation can help reduce waste in marketing campaigns. If you know which customers are similar to each other, you’ll be better positioned to target your campaigns at the right people.

Customer segmentation can also help in other marketing tasks such as product recommendations, pricing, and up-selling strategies.

Customer segmentation was previously a challenging and time-consuming task, that demanded hours of manually poring over different tables and querying the data in hopes of finding ways to group customers together. But in recent years, it has become much easier thanks to machine learning, artificial intelligence algorithms that find statistical regularities in data. Machine learning models can process customer data and discover recurring patterns across various features. In many cases, machine learning algorithms can help marketing analysts find customer segments that would be very difficult to spot through intuition and manual examination of data.

Customer segmentation is a perfect example of how the combination of artificial intelligence and human intuition can create something that is greater than the sum of its parts.

The k-means clustering algorithm