The document outlines customer segmentation, explaining its importance in marketing and the efficiency it brings in understanding diverse customer needs. It details various types of segmentation, including demographic, psychographic, behavioral, and geographic categories. Additionally, it introduces k-means clustering as a method for organizing unlabeled data into distinct groups based on shared properties.

1. Segmentation
3514 – SHIVPRAKASH VISWAKARMA
3516 – SOWMEN PARUI
Customer

2. Table Of Contetnts
What is Customer Segmentation?
1
Approch for Customer
Segmentation Process
2
Types of Segmentation
3
K- Means Clustering
4

3. What is Customer Segmentation?

4. Approach For Customer Segmentation
01
05
04 03
02
Understanding
Business Problem
Develop Solution
Approach
Collect and Manage
Data
Run Analysis On
Your Data
Deliver Solution

5. Types of Segmentation
Demographic
 Age
 Gender
 Income
 Location
 Education.
Psychographic
 Interests
 Lifestyles
 Psychological
Influences
 Motivates
 Priorities.
Behavioral
 Purchasing Habits
 Spending Habits
 User Status
 Brand Interactions
Geographic
 City
 ZIP Code
 Country
 Climate
 Urban
 Rural.

6. Why Customer Segmentation?
 Segmentation allows marketers to better tailor their
marketing efforts to various audience subsets.
 Segmenting the customers helps to be more efficient
in terms of Saving Time, Money and other resources.
 Helps better to understand the Customers about their
Daily Change in needs and preferences in market.

7. K – Means Clustering
• K-Means Clustering is an Unsupervised Learning algorithm,
which groups the unlabeled dataset into different clusters.

8. K – Means Clustering
• The algorithm takes the unlabeled dataset as input, divides the
dataset into k-number of clusters in such a way that each
dataset belongs to only one group that has similar properties.

9. Environments and Tools