AI-Driven Geofencing-Based Intelligent

Problem Statement

Traditional retail systems provide generic offers that do not align with individual customer preferences, resulting in low customer engagement and reduced sales effectiveness.Proposed Solution

This project proposes an AI-driven retail system that integrates geofencing and machine learning to deliver real-time personalized offers to customers when they are near or inside a store.

System Workflow

• Customer location is captured using GPS through a mobile application

• Geofencing is implemented using GeoPy to detect entry into the store area

• Customer purchase history is collected from POS systems

• The Apriori algorithm is applied to identify frequent itemsets and buying patterns

• Personalized offers are generated based on these patterns

• Notifications are sent to the user in real-time using Firebase Cloud Messaging

Algorithms Used

• Apriori Algorithm (Association Rule Mining):
  Used to identify frequently co-purchased products and generate relevant promotional offers
  
  Technology Stack

• Frontend: React Native / Web Application

• Backend: FastAPI (Python)

• Database: MongoDB

• Geofencing: GeoPy

• Machine Learning: Scikit-learn, mlxtend

• Notifications: Firebase Cloud Messaging

Key Features

• Real-time geofence-based offer triggering

• Personalised recommendations using AI

• Time-based promotional offers

• Customer loyalty scoring

• Inventory-aware discount generation

• Gamified engagement features

Innovation

• Integration of artificial intelligence with location-based services

• Real-time, context-aware marketing system

• Continuous learning from customer purchase behaviour

Expected Outcomes

• Improved customer engagement

• Increased sales and conversion rates

• Data-driven marketing strategies

• Optimised inventory management