Customer Segmentation using Clustering

April 9, 2024

Customer clustering involves segmenting customers into groups based on common characteristics, such as their behavioral similarities and habits. This practice is highly relevant as it allows companies to make personalized offers to customers and develop marketing strategies based on these groups.

Personalization is considered the gold standard of customer engagement. Organizations that successfully personalize their digital experiences are reported to generate 5% to 15% higher revenues, as well as 10% to 30% higher returns on their marketing spend.

Customer Segmentation Benefits

Some benefits of customer segmentation include:

• Prioritizing the offer of certain products to specific groups.
• Identifying customer's needs to offer more attractive products.
• Developing a more assertive product distribution strategy.
• The possibility of creating personalized marketing campaigns.

How Does Customer Segmentation Work? 

Some commonly used parameters for segmenting customers into groups are behavioral data such as spending and consumption habits, demographic information (age, income, and education), geographic information, and others. However, defining variables becomes particularly challenging in this type of analysis when there is no clear response variable and an ambiguous standard for selection. Therefore, the variables depend on the business objective, and the data analysis specialist must have the ability to translate this objective into variables for the model.

Cluster Analysis and K-means Algorithm

A cluster refers to a collection of data points aggregated together because of certain similarities. Cluster analysis aims to group elements in a way that within the group they are homogeneous with each other, and between different groups they are heterogeneous. In other words, within the group, the variability should be minimal, and between groups, maximum.

After defining the model parameters, a measure is needed to quantify the similarity between elements, such as Euclidean Distance, which is a dissimilarity criterion (the smaller the value, the more similar). It is the distance between two points that can be proven by repeatedly applying the Pythagorean Theorem.

K-means clustering is one of the simplest and most popular unsupervised machine learning algorithms. Typically, unsupervised algorithms make inferences from datasets using only input vectors without referring to known, or labeled, outcomes. 

The K-means algorithm uses Euclidean Distance (D) to form the groups. It creates a number k of random centroids and calculates the value of D for each point to each centroid, classifying that point according to the nearest centroid. This process is iterative until the best position for the centroids is found. All of these steps can be seen in this video and result in a clustering similar to the figure below.

If the iterative process is not executed properly, it may result in the formation of clusters that do not comply with the similarity criteria mentioned earlier, leading to incorrect clustering, as shown in the graph below.

By accurately applying the algorithm, it becomes possible to identify the unique characteristics of each group and make crucial decisions for the business based on them. This results in a more confident and personalized decision-making process, leading to increased returns for the business.

Looking to enhance your customer clustering and segmentation strategies? Reach out to us at Elint! Our team of experts and cutting-edge data analysis solutions can help you unlock valuable insights from your customer data, leading to more effective marketing strategies and personalized customer experiences.