Description
In this project, we collaborated with Henkel, a global leader in the DIY tools and consumer goods market. The objective was to optimize budget allocation strategies across different stores to maximize returns and operational efficiency. Together with Jonathan Bouniol, we conducted advanced data-driven analyses and provided actionable recommendations tailored to Henkel’s diverse retail network. The project integrated machine learning models and geospatial analyses to ensure a precise and impactful distribution of resources.
Key Features
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Machine Learning Implementation: We utilized machine learning algorithms, such as linear regression and K-means clustering, to build predictive models for sales and identify patterns in budget allocations. Multiple scenarios were developed to demonstrate the impact of reallocating budgets on key performance indicators.
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Data Analysis: Through comprehensive data exploration, we analyzed correlations between variables like budget size, sales performance, and regional factors. This allowed us to identify discrimination factors, enabling effective store clustering and informed recommendations for resource reallocation.
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Marketing and Finance: Our strategy included A/B testing frameworks to evaluate the effectiveness of budget allocation changes, financial projections grounded in ROI metrics, and detailed analysis of KPIs such as customer acquisition cost (CAC) and average revenue per user (ARPU).
Technologies Used
- Excel: Conducted financial ratio analysis to evaluate current budget allocation and ROI.
- Python (ScikitLearn): Implemented machine learning models such as linear regression and K-means clustering for predictive analytics.
- Pandas and NumPy: Used for data cleaning, transformation, and in-depth analysis.
- Visualization Tools: Matplotlib and Seaborn for creating clear and actionable visualizations of trends and results.
Role and Contributions
As a key contributor to the project, I was responsible for integrating machine learning techniques into the analytical framework. I designed the predictive models, including sales forecasting and clustering approaches, and provided insights into how budget reallocation could be optimized. Additionally, I played a significant role in data preparation and feature engineering, ensuring high-quality inputs for analysis. Together with Jonathan Bouniol, I co-developed the strategy for financial projections and conducted scenario testing to validate our recommendations.
Outcome
The project successfully provided Henkel with a set of actionable strategies to optimize its budget allocation across stores. By implementing our recommendations, Henkel gained deeper insights into which stores and regions required additional funding and which could operate efficiently with reduced budgets. Our analysis highlighted an average improvement of 15% in ROI for test cases, and the clustering approach revealed untapped market potential in specific geolocations. The tools and models developed in this project are scalable and can be adapted for future financial planning and strategy development across Henkel’s global operations.