Summit Outfitters Customer Value Analysis

Project Overview

This project analyzes customer data for Summit Outfitters, a premium outdoor gear company, to identify the most valuable customers and explore cross-selling and up-selling opportunities. To read more about this project, check out my blog post Unlocking Customer Value: A Data-Driven Approach for Summit Outfitters.

Table of Contents

1. Installation
2. Project Motivation
3. File Descriptions
4. Results
5. Licensing, Authors, and Acknowledgements

Installation

This project requires Python 3.x and the following Python libraries:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

You can install these packages using pip:

pip install pandas numpy matplotlib seaborn scikit-learn

Project Motivation

Summit Outfitters wants to leverage its customer data to:

1. Identify the most valuable customer segments
2. Explore cross-selling and up-selling opportunities
3. Develop targeted marketing strategies

File Descriptions

• most-valuable-customers.ipynb: Jupyter notebook containing the main analysis
• data/online_retail_customer_churn.csv: Dataset used for the analysis (not included in repo)

Results

Key findings from the analysis include:

1. Characteristics of most valuable customers:

   • Age: 30-55
   • Annual income: $158k-$186k
   • Total spend: $7,552-$9,226
   • Purchase frequency: 3-8 times per year

2. Identified several customer segments prime for cross-selling and up-selling

3. Developed actionable strategies including:

   • Targeted marketing campaigns
   • Premium product development
   • Loyalty program implementation
   • Concierge customer service for high-value customers

Licensing, Authors, and Acknowledgements

• Data source: Online Retail Customer Churn Dataset on Kaggle (simulated for this project)
• This project is licensed under the MIT License - see the LICENSE file for details
• Connect with me on LinkedIn!