Product Recommendation System
Project Overview:
For this project we built a regression model to recommend products to customers based on their purchase history, browsing behavior
Objective:
To equip the board of directors with real-time data-driven insights via Power BI, facilitating informed decision-making processes and decisions.
Steps taken to complete the project:
Initial Consultation and Requirements Gathering
Meeting with Stakeholders: Conducted detailed discussions with the board of directors, marketing managers and key stakeholders to understand their specific needs and goals
Requirement Analysis: Identified critical business metrics and KPIs that needed to be tracked
Data Source Integration
Identified and integrated data from various sources (iQvia, OSFE, internal data sources) including sales figures, marketing campaigns, and production data. This ensured a holistic view of the company's performance
Data Cleaning and Transformation
Cleaned and transformed raw data to ensure accuracy and consistency. This involved handling missing values, formatting inconsistencies, and creating calculated fields for deeper analysis
Dashboard Design and Development
Designed user-friendly and interactive dashboards within Power BI. These dashboards visualized key performance indicators (KPIs) such as sales trends, product performance per code, per category and per brick, an overview of the competition and marketing effectiveness
Customization: Created customized dashboards to meet the specific needs of the board, allowing them to drill down into detailed data as required
User Training and Support
Pilot Testing: Conducted pilot testing with a small group of users (product managers and sales managers) to gather feedback and make necessary adjustments
Provided training sessions for the board of directors and all key stakeholders (product managers, sales managers, marketing managers) on navigating and interpreting the dashboards. This included ongoing support to ensure they could readily access and utilize the valuable insights.
Outcomes:
Technologies Used:
Project Results and Impact:
What we learned from the project:
Conclusion: