CUSTOMER SEGMENTATION:

These days, you can personalize everything. There’s no one-size-fits-all approach. But, for business, this is actually a great thing. It creates a lot of space for healthy competition and opportunities for companies to get creative about how they acquire and retain customers.

One of the fundamental steps towards better personalization is customer segmentation. This is where personalization starts, and proper segmentation will help you make decisions regarding new features, new products, pricing, marketing strategies, even things like in-app recommendations.

But, doing segmentation manually can be exhausting. Why not employ machine learning to do it for us? In this article, I’ll tell you how to do just that.

MACHINE LEARNING

Machine learning methodologies are a great tool for analyzing customer data and finding insights and patterns. Artificially intelligent models are powerful tools for decision-makers. They can precisely identify customer segments, which is much harder to do manually or with conventional analytical methods.

There are many machine learning algorithms, each suitable for a specific type of problem. One very common machine learning algorithm that’s suitable for customer segmentation problems is the k-means clustering algorithm. 

IMPLEMENTATION:-

# Import required libraries

import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
import plotly.express as px
import plotly.graph_objects as go
import matplotlib.pyplot as plt

DATA SET:-


CONCLUSION:-
It’s not wise to serve all customers with the same product model, email, text message campaign, or ad. Customers have different needs. A one-size-for-all approach to business will generally result in less engagement, lower-click through rates, and ultimately fewer sales. Customer segmentation is the cure for this problem.