Customer segmentation refers to the practice of dividing a company's customer base into distinct groups or segments based on certain characteristics or criteria. The purpose of customer segmentation is to gain a deeper understanding of customers and their needs, behaviors, and preferences in order to develop more effective marketing strategies and improve overall business performance.
The dataset is worked with is based on the types of people visit at the mall and according to that profiling is done.
The different types of customers have been segmented using K-MEANS CLUSTERING and the clusters are divided into 5 types:
a. High Income, High Spending Score
b. High Income, Low Spending Score
c. Average Income, Average Spending Score
d. Low Income, High Spending Score
e. Low Income, Low Spending Score
The position of the clusters and their respective types are mentioned in the code file.
a. High Income, High Spending Score (Cluster 5) - Target these customers by sending new product alerts which would lead to increase in the revenue collected by the mall as they are loyal customers.
b. High Income, Low Spending Score (Cluster 3) - Target these customers by asking the feedback and advertising the product in a better way to convert them into Cluster 5 customers.
c. Average Income, Average Spending Score (Cluster 2) - Can target these set of customers by providing them with Low cost EMI's etc.
d. Low Income, High Spending Score (Cluster 1) - May or may not target these group of customers based on the policy of the mall.
e. Low Income, Low Spending Score (Cluster 4) - Don't target these customers since they have less income and need to save money.