Nowadays, Quick Commerce (Q-Commerce) has rapidly transformed retail business by enabling ultra-fast delivery of essentials through platforms like Blinkit, Zepto, Swiggy Instamart, and BigBasket. However, inconsistent pricing and also lack of transparency across platforms creates confusion for customers while making decisions on purchasing. This paper addresses this challenge by designing and implementing a scalable centralized architecture that will aggregates and compares real-time product prices from multiple vendors or platforms. Also, the proposed architecture allows customers to view delivery times, offers, and vendor reliability. Finally, the proposed architecture enables customers to effortlessly find the best value, save money, and make better purchasing choices, while simultaneously driving healthier competition in the market.
Introduction
The paper presents a centralized price comparison system designed to enhance transparency in Quick Commerce (Q-Commerce) platforms like Blinkit, Zepto, Swiggy Instamart, and BigBasket. These platforms offer ultra-fast delivery of essentials but often lack price consistency, leading to customer confusion and missed savings opportunities.
Problem Addressed
Inconsistent pricing for the same products across different Q-Commerce apps.
Lack of transparency and difficulty in comparing prices manually.
Need for a digital tool to empower consumers to make informed decisions.
Proposed Solution
A modular, layered architecture is proposed to:
Aggregate and compare prices across platforms.
Provide real-time updates using web scraping and APIs.
Offer user-friendly filtering (e.g., price, delivery time, ratings, offers).
Architecture Components
Frontend (React.js)
Product search, filters, and comparison interface.
Communicates via RESTful APIs with the backend.
Backend (Flask or Node.js)
Handles session management, ranking logic, and business operations.
Connects to scraping modules and databases.
Data Aggregation Layer
Uses web scraping (Beautiful Soup, Selenium, Puppeteer) and APIs.
Gathers data even from platforms without public APIs.
Database (MongoDB/PostgreSQL)
Stores current and historical price data for trend analysis.
Supports both structured and unstructured data formats.
Deployment
Hosted on cloud platforms (AWS, Google Cloud).
Uses Agile methodology, version-controlled via GitHub.
Key Features
Real-time product price comparison across platforms.
Shows best deals when multiple items are selected.
Allows direct payment and order tracking through the system.
Literature Review Insights
Web scraping and APIs are effective for real-time comparison tools.
Smart user interfaces increase customer engagement and satisfaction.
Platforms influence user behavior (e.g., E-switching based on price, UX).
Hybrid systems combining ML and scraping offer predictive insights.
Application Snapshots
Homepage: Product search and personalized recommendations.
Comparison View: Shows product prices across Q-Commerce sites.
Best Deal View: Optimized bundle deals across platforms.
Payment Window: Enables checkout and order tracking.
Conclusion
This paper addresses the significant problem of lack of price transparency and inconsistent pricing across popular Quick Commerce platforms such as Blinkit, Zepto, Swiggy Instacart, and BigBasket by proposing a centralized, web-based architecture that aggregates and compares real-time product prices from these multiple vendors. Also, the proposed system allows users to view not only prices but also compare factors like delivery times, offers, and vendor reliability, enabling them to make more informed and smarter purchasing decisions. Future plans involve enhancements such as AI-driven recommendations and price trend analytics.
References
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