https://github.com/relostar-devil/analytical-decision-modelling

Optimizing advertising performance and production planning using data-driven decision modeling and leveraging mathematical optimization (Gurobi) to improve ROI by effectively allocating ad spend and managing production costs.
https://github.com/relostar-devil/analytical-decision-modelling

Last synced: about 1 year ago
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Optimizing advertising performance and production planning using data-driven decision modeling and leveraging mathematical optimization (Gurobi) to improve ROI by effectively allocating ad spend and managing production costs.

• Host: GitHub
• URL: https://github.com/relostar-devil/analytical-decision-modelling
• Owner: Relostar-Devil
• Created: 2025-01-26T00:17:45.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-10T19:02:30.000Z (over 1 year ago)
• Last Synced: 2025-02-10T20:20:27.893Z (over 1 year ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 2.06 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Analytical Decision Modeling  

This repository contains analytical decision modeling projects focused on optimizing advertising performance and production planning. The analysis is based on real-world data and employs data-driven decision-making techniques, including mathematical modeling and visualization.  

## Repository Contents  

- **Python Code (`Analytical Decision Modelling Python Code.ipynb`)**  

  - Implements an optimization model using Gurobi to maximize profitability by optimizing advertising spend and production costs.  

  - Includes constraints for ad spend, revenue relationships, and production costs.  

  - Uses a profit-maximization objective function to enhance business efficiency.  

- **Presentation (`KCC Analysis.pptx`)**  

  - Summarizes key findings of advertising performance analysis.  

  - Visualizes insights on sales, ad efficiency, and optimization strategies.  

  - Discusses future considerations for improving ad spend efficiency and scalability.  

- **Report (`REPORT.docx`)**  

  - Provides an in-depth analysis of KCC Development Inc.'s advertising strategy.  

  - Highlights challenges such as high Advertising Cost of Sales (ACoS) and conversion rate optimization.  

  - Proposes data-driven recommendations for improving advertising ROI and inventory management.  

## Key Insights  

- **Optimization Model:**  

  - Helps allocate advertising budget effectively to maximize return on investment.  

  - Suggests an optimal production strategy to reduce costs and improve efficiency.  

- **Advertising Performance Analysis:**  

  - Identifies trends in ad spend, revenue, and key performance indicators (KPIs).  

  - Proposes improvements in cost-per-click (CPC) and conversion rates (CVR).  

- **Strategic Recommendations:**  

  - Implement an optimized advertising budget allocation.  

  - Leverage data-driven decision-making for production and inventory planning.  

  - Improve forecasting models for better resource allocation.  

## Usage  

1. Open the Jupyter Notebook (`Analytical Decision Modelling Python Code.ipynb`) to run the optimization model.  

2. Review the PowerPoint presentation (`KCC Analysis.pptx`) for a high-level summary.  

3. Refer to the detailed report (`REPORT.docx`) for a comprehensive understanding of the findings and recommendations.  

This repository showcases a structured approach to analytical decision modeling, combining optimization techniques with real-world business insights.