A Data-Driven Approach to SMART SHOPPING: Optimizing Grocery Trips Using Geolocation and Store Inventory Data

Author

Kien T. Giang, University of MississippiFollow

Date of Award

Spring 5-22-2025

Document Type

Undergraduate Thesis

Department

Computer and Information Science

First Advisor

Yixin Chen

Second Advisor

Hong Xiao

Third Advisor

Sumali Conlon

Relational Format

Dissertation/Thesis

Abstract

This project presents a data-driven web-based application, Smart Shopping, designed to help customers optimize their grocery purchases based on location and store inventory information. The application allows users to add items to a shopping list and either enter an address or use their browser’s location services to identify nearby stores. It then retrieves product availability and prices from a mock database representing stores at user’s selected locations. The program compares prices across stores to provide users with two optimized options: the cheapest shopping bill from a single store, and the lowest individual item prices across multiple stores. Additionally, the app integrates Google Maps to provide distance, estimated travel time, estimated gas cost, and driving directions. This combines to show users the actual total cost of a shopping trip, which includes item costs and travel cost, thus, helping users to make informed decisions. Users can sign up/log in to use the save and manage shopping lists function for future references. Built with Vercel using React, Next.js, and APIs, the project showcases how location-based services and inventory data can enhance cost-efficiency and decision-making for everyday shopping.

Recommended Citation

Giang, Kien T., "A Data-Driven Approach to SMART SHOPPING: Optimizing Grocery Trips Using Geolocation and Store Inventory Data" (2025). Honors Theses. 3317.
https://egrove.olemiss.edu/hon_thesis/3317

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.