Python-Customer-segmentation-and-consumer-behavior-analysis-using-machine-learning

KO | EN

gitlite — search

#python #java #python3 #arduino #golang #machine-learning #rust #html #flask #javascript #seismology #nodejs

Python-Customer-segmentation-and-consumer-behavior-analysis-using-machine-learning

★ 13 Open GitHub ↗

Segmenting consumer based buying behavior and applying 80/20 rule to identify top customers/products/geographic locations driving 80% of total $ sales

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Customer-segmentation-and-consumer-behavior-analysis

(Please click on the Consumer buying behavior - Paul.ipynb above to see the detailed application of analytics and its interpretation)

Brief Snapshot:

Topic # 1:

Segmenting consumer based buying behavior and applying 80/20 rule to identify top customers/products/geographic locations driving 80% of total $ sales

This is a real sales data set of a UK based retailer

Objective: Segment customers based on buying behavior by applying k-means clustering algorithm to calculate the optimal number of customer segments with similar buying habits (features).

Data Source: https://archive.ics.uci.edu/ml/datasets/Wholesale+customers

Topic 2:

Apply 80/20 rule to identify the top 20%

1) Customer segments

2) Products and

3) Geographic locations

resulting in 80% of $ sales revenue.

Data Source used: https://archive.ics.uci.edu/ml/datasets/online+retail

## Customer segments based on buying behavior by applying k means clustering (unsupervised learning) algorithm :

### Elbow method to choose the optimal number of customer segments (clusters):

![image](https://user-images.githubusercontent.com/38769913/51401473-da786880-1b18-11e9-9f8e-3a79651f25f3.png)

### Customer segments:

![image](https://user-images.githubusercontent.com/38769913/51401411-aa30ca00-1b18-11e9-957d-5bd9342b9093.png)

## Distribution of customers in the 3 (optimal # of) clusters:

![image](https://user-images.githubusercontent.com/38769913/51401510-ef54fc00-1b18-11e9-996e-e06ce24b873d.png)

### Analysis:
There are some overlap between segment No. 3 with each of Segment No. 1 & 2 at the boundaries which we need to keep in mind while analyzing customers.

The k-means clustering algorithm being a un-supervised learning algorithm, we can perform a quick visual check on the model's performance based on the visualization chart.

Business Strategy:
Customer segment # 1 & 2 have opportunities for growth and future expansion. As the retails industry is a saturated industry, hence customer segment # 3 may already be dominated by other retailers so our client can try to increaese sales in the 2 other cutomer segments (# 1 & 2) through suitable competitive positioning, pricing stratgey, cohesive sales & marketing efforts, promotions, bundling etc.

### Buying behavior of customers within each cluster:

![image](https://user-images.githubusercontent.com/38769913/51401375-91c0af80-1b18-11e9-9eb9-be9fcc102d66.png)

![image](https://user-images.githubusercontent.com/38769913/51401293-60e07a80-1b18-11e9-8f6d-0f910b8e6d74.png)

### Analysis:

For all 3 customer segments: majority of customers are from Region 3. So Region is not a key factor to segment customers.

Fresh: Segment # 2 makes significant purchases compared to other segments folloewed by Segment # 3

Milk: Segment # 1 makes significant purchases compared to other segments

Grocery: Segnment # 1 makes significant purchases compared to other segments

Frozen: Segment # 2 makes significant purchases followed by Segment # 3

Detergent_Paper: Segment # 1 is a major purchaser

Delicassen: Segnment # 2 on average makes most purchases, followed by Segmnet # 1

## Snapshots of few Analysis: 
(Detailed analysis shown in Consumer buying behavior - Paul.ipynb file)

### Customers:

1084 out of total 3877, top 25% of customer segments result in 79% of total $ sales amount.

![image](https://user-images.githubusercontent.com/38769913/51401094-f92a2f80-1b17-11e9-9772-da123b92844f.png)

### Products:

775 out of total 3877, top 20% of products result in 79% of total $ sales amount.

![image](https://user-images.githubusercontent.com/38769913/51401016-cc761800-1b17-11e9-9069-29b34fd5bbd1.png)

### Geographic Locations: Top 10 countries by revenue

![image](https://user-images.githubusercontent.com/38769913/51400833-5d98bf00-1b17-11e9-8f8a-2fed1fcee2e1.png)

### Geographic Locations: Bottom 20 countries by revenue

![image](https://user-images.githubusercontent.com/38769913/51400800-4954c200-1b17-11e9-8bdc-7f5d0cd58b89.png)

### Analysis:
UK alone results in 82% of total $ revenue which is expected for a UK based retailer.

### Business Strategy:
The senior management for the retail company should consider expanding to the other countries (after UK) where they have significant sales such as Neatherland, EIRE, Germany & France for future geographic expansion.

Python-Customer-segmentation-and-consumer-behavior-analysis-using-machine-learning

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README