Challenge

A medical technology company needed to enhance its customer relationship management by leveraging data to drive marketing effectiveness. The existing approaches to user engagement were not sufficiently targeted, resulting in missed opportunities for customer retention and suboptimal marketing efficiency.

Solution

  • Conducted data preparation and analysis of user behavior to create a reliable foundation for targeted marketing strategies

  • Developed and implemented machine learning models in Python for B2C customer segmentation, enabling more personalized and effective campaigns

  • Collaborated with cross-functional teams (marketing, product management, IT) to ensure that data-driven insights were integrated into business processes and decision-making

  • Delivered reporting and insights in multiple formats (PDF, Word, Excel) to support transparency and communication across teams

Impact

  • Enabled a 15% increase in marketing efficiency through precise targeting

  • Improved customer retention and reduced churn rates

  • Strengthened collaboration between marketing and technical teams by embedding data-driven practices into daily workflows

Technical Tools & Reporting

  • Python (Pandas, NumPy, scikit-learn)

  • Google Colab & Jupyter Notebook for collaborative development

  • SQL (SQLAlchemy) for data extraction and integration

  • Automated reporting in PDF, Word, and Excel formats

Next Steps

A link to the GitHub repository will be added soon, including:

  • Problem description

  • Data and preprocessing steps

  • Full machine learning implementation

🔗 Download both the notebook and dataset from our GitHub repository: Here.

A special thank you to Edmond Tefong for preparing the solution and Python code for this case study.