Customer Segmentation and marketing campaigns analysis

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Project Overview 🕮

What motivated me to carryout an analysis on marketing campaigns and customer segmentation is that they are an essential component of how businesses promote their interests. It is therefore critical for companies to measure customer engagement with marketing campaigns, evaluate the effectiveness of previous efforts, and suggest data-driven strategies to boost engagement with upcoming campaigns. The data used in the project is taken from kaggle. I could not find a private company data, open for scraping and usage. Hence, my choice. 📰🗞️

Libraries 🐱‍💻

• numpy for mathematical operations on arrays.
• datetime for date manipulation.
• pandas to perform data manipulation and analysis.
• seaborn for data visualization and exploratory data analysis.
• plotly to create beautiful interactive web-based visualizations.
• plotly express easy-to-use, high-level interface to Plotly.

Dataset Clustering and Segmentation

Languages and Tools 👨‍💻

  Languages

Languages	Usage
Python 3.11.0	Programming Language For data cleaning, manipulation and visualization

  Tools

Tools & Environment	Usage
Jupyter NoteBook	An open-source IDE used to create the Jupyter document.
Power BI (Power Query, DAX)	Data visualization tool.
Kaggle	For downloading training data.
Git	A version control system to manage and keep track source code history.

Problem Statement

• Which products are performing best?
• Which channels are underperforming?
• What does the average customer look like for the Company?
• Which marketing campaign is most successful?
• Which Regions perform best?
• Which costumer segment purchase more?
• What does the costumer segment which accepted the last marketing campaign look like?

Methodology

Data Collection Getting data from Kaggle.

Data Cleaning and Preparation Removing irrelevant and restructuring the dataset for easy analysis.

Visualization and Reporting visually presenting data in form of charts and graphs.

Insights presenting observations from the analysis.

Project Link

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Running the project

To run the (.ipynb) project use Notebook or Google Colab, while Power BI for the (.PBIX) file.

Support

For support, email njimonda.co@gmail.com.

Author

• @njimonda

Badges

Contributing to this project

Contributions are always welcome!

Please adhere to this project's code of conduct.

Acknowledgements

• Awesome Readme Templates
• Awesome README
• How to write a Good readme