Retail | Store Operations
New Store Planning
Objective
The client faced challenges in identifying optimal locations for new stores to maximize profitability and minimize risks. The goal was to maximize foot traffic, sales, and overall profitability while minimizing operational costs.
Challenges
- High variability in customer demographics and preferences across different regions.
- Limited availability of real-time data for accurate decision-making.
- Complexity in integrating multiple data sources such as market trends, competitor locations, and socio-economic factors.
- High costs associated with incorrect site selection.
Solution Proposed
Integration of ML, BI, and GIS created a comprehensive, data-driven approach to site selection that utilized the following:
- Clustering and regression to analyze customer demographics and predict foot traffic.
- Developing a PBI Dashboard to integrate and visualize data from various sources, including market trends and competitor analysis.
- Geographic Information Systems (GIS) for spatial analysis and mapping potential store locations.
- Predictive analytics to forecast sales for each location.
Outcome
- Increased accuracy in site selection by 30%.
- Significant reduction in operational costs due to better location choices.
- Improved sales performance in newly opened stores.
- Expedited site selection process time, enhancing decision-making speed.
Design & Thinking Wins
- Successful integration of ML, BI, and GIS technologies.
- Development of a scalable and adaptable site selection model.
- Creation of a user-friendly dashboard for real-time data visualization and decision-making.
- Implementation of a feedback loop for continuous improvement of the model.
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.
