Why k-means clustering is used for customer segmentation?

Why k-means clustering is used for customer segmentation?

The goal of K means is to group data points into distinct non-overlapping subgroups. One of the major application of K means clustering is segmentation of customers to get a better understanding of them which in turn could be used to increase the revenue of the company.

How can cluster analysis be used for customer segmentation?

In the context of customer segmentation, cluster analysis is the use of a mathematical model to discover groups of similar customers based on finding the smallest variations among customers within each group. These homogeneous groups are known as “customer archetypes” or “personas”.

Is k-means used for clustering?

K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. In other words, the K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible.

What is K-means clustering explain with example?

K-means clustering algorithm computes the centroids and iterates until we it finds optimal centroid. In this algorithm, the data points are assigned to a cluster in such a manner that the sum of the squared distance between the data points and centroid would be minimum.

How does the k-means clustering model work?

After finding the optimal number of clusters, fit the K-Means clustering model to the dataset defined in the second step and then predict clusters for each of the data elements. It means it will predict which of the 5 clusters the data item will belong to. 5.

How to think of clusters in customer segmentation?

Think of clusters as groups in the customer-base. Prior to starting we will need to choose the number of customer groups, k k, that are to be detected. The best way to do this is to think about the customer-base and our hypothesis.

How to visualize the result of clustering?

When the algorithm predicts a cluster for each of the data items, we need to visualize the result through the plot. For better representation, we need to give each of the clusters a unique colour and name. The name of clusters is given based on their income and spending.

How to find the optimal number of clusters?

The majority of the customers have annual income in the range 60000 and 90000. Next I plotted Within Cluster Sum Of Squares (WCSS) against the the number of clusters (K Value) to figure out the optimal number of clusters value. WCSS measures sum of distances of observations from their cluster centroids which is given by the below formula.