Luxury Boutique Layout Optimization
Overview
This repository hosts the final report for the MGSC 662 project, focusing on optimizing product placement in a luxury boutique. The project employs advanced analytics to maximize store revenue and enhance the shopping experience, catering to both new and VIP customers.
Project Summary
Objective: To enhance customer experience and maximize revenue through strategic product placement in a luxury boutique.
Approach: Utilizes decision analytics to optimize the arrangement of products, focusing on new customer acquisition and VIP client engagement.
Dataset: Analysis of Fiscal Year 2019 sales data from a global luxury brand, concentrating on top-selling products.
Key Strategies:
Highlighting new and exclusive products in window displays and throughout the store.
Balancing product placement for men and women, reflecting market demand.
Implementing layout constraints to maintain exclusivity and enhance customer experience.
Methodology:
Data Preprocessing: Encoding categorical attributes and formulating the framework using Python.
Optimization Model: A strategic model with objectives focusing on attracting new customers, boosting revenue, and optimizing VIP purchases.
Constraints: Implementing unique SKU appearances and one SKU per shelf to maintain exclusivity.
Problem Extension: Discount Strategies
Introduction of Discount Strategies: Adds a binary variable to manage discounts, aligning with the boutique's luxury positioning.
Constraints on Discounts: Limits discounts to less than 15% of total non-seasonal products, with no discounts on new seasonal products and shop window displays.
Impact Analysis: Shows increased new customer acquisition, constant VIP customer numbers, and a decrease in total revenue, underscoring the need for a balanced discount approach.
Strategic Implications: Demonstrates that judicious discounts can attract diverse customer segments while maintaining the boutique's luxury image.
Results and Recommendations
Model output indicates a selection bias towards monochromatic schemes.
Recommends incorporating comprehensive historical data and expanding the model's scope.
Suggests adapting the model for different retail sectors for broader applicability.
Conclusion
The project demonstrates the potential of data-driven strategies in optimizing luxury retail layout design. It offers valuable insights into customer behavior and store performance, paving the way for sophisticated and profitable retail operations.
This project was created by Tony Xu, Yichen Yu, Yifan Lu, and myself.