https://github.com/stephen-adwini-badu/04.-quantium-project

This project investigates customer spending patterns and sales trends for Quantium. This notebook is designed to provide a comprehensive analysis of customer purchasing trends and behaviors, specifically focusing on the chips category in a supermarket's portfolio. The objective is to derive actionable insights to support strategic recommendations
https://github.com/stephen-adwini-badu/04.-quantium-project

customer-segmentation data-science data-visualization jupyter-notebook

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This project investigates customer spending patterns and sales trends for Quantium. This notebook is designed to provide a comprehensive analysis of customer purchasing trends and behaviors, specifically focusing on the chips category in a supermarket's portfolio. The objective is to derive actionable insights to support strategic recommendations

• Host: GitHub
• URL: https://github.com/stephen-adwini-badu/04.-quantium-project
• Owner: Stephen-Adwini-Badu
• Created: 2025-01-13T16:13:02.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-12T01:11:31.000Z (over 1 year ago)
• Last Synced: 2025-02-12T02:26:23.961Z (over 1 year ago)
• Topics: customer-segmentation, data-science, data-visualization, jupyter-notebook
• Language: Jupyter Notebook
• Homepage:
• Size: 4.87 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Quantium Project

## Overview

This project investigates customer spending patterns and sales trends for Quantium. This notebook is designed to provide a comprehensive analysis of customer purchasing trends and behaviors, specifically focusing on the chips category in a supermarket's portfolio. The objective is to derive actionable insights to support strategic recommendations , in preparation for the upcoming category review.

## Objectives

1. **Analyze Current Purchasing Trends**: Understand how customers currently engage with the chips category in terms of frequency, volume, and spend.

2. **Identify Customer Segments**: Classify customers into meaningful segments based on their purchasing behaviors to uncover unique preferences and trends.

3. **Support Strategic Planning**: Provide data-backed insights that inform the strategic direction for the chips category.

## Methodology

### 1. **Data Preparation**

   - Load and clean the dataset to ensure accuracy and consistency.

   - Address missing values, outliers, or data anomalies.

### 2. **Exploratory Data Analysis (EDA)**

   - Explore the data to understand general purchasing patterns.

   - Investigate key metrics such as total sales, average transaction size, and purchase frequency.

   - Visualize data trends to identify standout behaviors or anomalies.

   - Analyze pre-Christmas sales spikes and their contributing factors.

### 3. **Customer Segmentation**

   - Profile segments to describe their characteristics, purchasing preferences, and overall contribution to the category.

### 4. **Chip Purchasing Behavior Analysis**

   - Examine metrics specific to chips, such as:

     - Sales trends over time.

     - Popular products or brands.

     - Average spend per transaction and per customer.

   - Identify patterns in chip purchasing across different customer segments.

### 5. **Insights**

   - *110, 134, 150, 165, 170 and 175* products sizes appear to be popular sizes as the **6** alone as they account for **~73%** of all purchases and the other **15** available sizes account for only **~27%**

   ![1 2](https://github.com/user-attachments/assets/a307c75d-e55e-4970-a808-560ab3fec724)

     

   - Sales trends show a significant increase leading up to Christmas.

   ![Image](https://github.com/user-attachments/assets/435e99a9-c3a1-4500-8369-c18d7e6b2048)

     

   - Sales are coming mainly from **(older families)** in general, **(mainstream young singles/couples)** and **(mainstream retirees)**  with these *6 (out of 21)* in total accountingfor **~40%** of total sales.

     

     ![1 3](https://github.com/user-attachments/assets/8cc881e8-cdd4-44cb-9919-4456c5a1304e)

   - Customers are coming mainly from **(older families)** in general, **(mainstream young singles/couples)** and **(mainstream retirees)** with these *6 (out of 21)* in total accounting for **~40%** of customers, *same as total sales*   with **(mainstream young singles/couples)** and **(mainstream retirees)** accounting for **11% and 9%** respectively.

   - **(New families)**, **(older families)** and **(young families)** in general buy more chips per customer and accounting for **56%** of product quantity with **(new families)** alone making up **21%**.

     

     ![1 4](https://github.com/user-attachments/assets/0423872c-7388-44a8-8ae3-374d6a0c1f4a)

   - In total **(midage singles and couples)** are more willing to pay more per packet of chips compared to other lifestage groups. Accounting for **16%** of sales per unit price per lifestage.

   - Also Premium customers tend to pay more for and in total account for **43%** of sales per unit price per customer type. This may be due to premium shoppers being more likely to buy healthy snacks and when they buy chips, this is mainly for entertainment purposes rather than their own consumption.