Retail | Merchandising & Category Management
Product Assortment Planning
Objective
The client faced challenges optimizing their product assortment to maximize sales and customer satisfaction while minimizing inventory costs. The client wanted to determine the ideal combination of products/SKUs to stock in each store location, considering local demand variations and seasonal trends.
Challenges
- High variability in customer preferences across different regions.
- Seasonal fluctuations impact demand for certain products.
- Balancing inventory levels to avoid stockouts and overstock situations.
- Integrating data from multiple sources for accurate demand forecasting.
Solution Proposed
- Utilized Machine Learning algorithms such as Random Forest, XGBoost, and collaborative filtering for demand forecasting, leveraging historical sales data, customer demographics, and seasonal trends.
- Implemented Business Intelligence (BI) tools to visualize sales patterns and inventory levels, enabling data-driven decision-making.
- Engineering solutions to integrate data from POS systems, CRM, and supply chain management for a holistic view of the retail ecosystem.
Outcome
- Improved sales by 15% through better alignment of product offerings with customer preferences.
- Reduced inventory costs through optimized stock levels.
- Enhanced customer satisfaction scores because of better product availability.
- Increased operational efficiency with reduction in stockouts and overstock situations.
Design & Thinking Wins
- Seamless integration of ML models with existing retail systems.
- Real-time data visualization and reporting capabilities.
- Scalable solutions adaptable to different store locations and product categories.
- Enhanced collaboration between data scientists, business analysts, and IT teams.
Disclaimer: The outline showcases the typical challenges, solutions, designs, and outcomes for industries and functions, in general, based on Affine’s prowess in the Industry. The outcomes would be much higher for specific clients as they would be based on their data and specific problems to be solved.
