Dynamic Price Monitoring in E-Commerce: Leveraging Web Scraping, Data Analysis, and Email Automation for Dynamic Pricing Insights

Abstract

This paper focuses on the development of a web application that leverages web scraping, data storage, and email automation technologies to track real-time price changes for specific products on e-commerce platforms. Built using Next.js for the frontend, MongoDB as the database, and Nodemailer for email notifications, the system aims to provide users with a comprehensive and user-friendly interface to monitor price fluctuations. By scraping data from product pages, the application gathers key pricing metrics, including the average, lowest, highest, and current prices, and presents this information visually to help users make informed purchasing decisions. By utilizing the combination of web scraping and real-time data analysis, this project provides a dynamic solution for both consumers and businesses, offering valuable insights into price changes and trends in the competitive e-commerce landscape.

Introduction

I. INTRODUCTION

Web scraping, a crucial component of this project, involves extracting structured data from websites, enabling the system to gather information on product pricing directly from e-commerce platforms. This method, combined with the efficiency of MongoDB for managing large datasets and the reliability of Nodemailer for communication, results in a robust system capable of handling real-time data updates and notifications.

The significance of this platform extends beyond simple price tracking. By analyzing and storing historical price data, the system offers insights into long-term trends, providing users with more than just a snapshot of current prices. This information can be invaluable for businesses, enabling them to identify patterns in price movements and understand market dynamics. Ultimately, this project aims to enhance the user experience by providing a valuable tool for monitoring price fluctuations in the e-commerce environment, empowering consumers to make informed decisions while also opening avenues for businesses to optimize their pricing strategies.

Furthermore, the project introduces a method for real-time product tracking, where users can monitor changes in pricing trends over time. The system employs regular scraping cycles, updating the product data and reflecting the latest price trends within the user dashboard. To enhance user engagement, an email notification system is integrated using Nodemailer, which sends alerts to users when significant price changes occur, helping them seize potential discounts or avoid price hikes.

By analyzing historical price data and sales trends, the underlying framework can be extended to identify patterns in customer purchasing behaviors. This creates an opportunity for businesses to gain insights into consumer needs by examining the correlation between pricing and customer interest. As the e-commerce landscape continues to evolve, this project aims to provide a valuable tool for consumers and businesses alike, enabling them to navigate the complexities of pricing strategies and make informed decisions in the digital marketplace.

II. LITERATURE SURVEY

A. A Review on Web Scrapping and its Applications (2019)

This paper explores web scraping techniques and their diverse applications in gathering data from websites. The authors aim to provide a comprehensive review of web scraping tools, methods, and software, addressing its role in automating data collection. By extracting structured data from unstructured web content, businesses and researchers can analyze and leverage information effectively. The paper highlights how web scraping is used in areas like price monitoring, competitive analysis, and data integration. Through a detailed examination of various scraping frameworks and tools, the authors underscore both the strengths and limitations of web scraping for different industries and research fields.

B. Web Scraping Approaches and their Performance on Modern Websites (2022)

The paper "A Review on Web Scraping and its Applications" explores the concept of web scraping as an automated process to extract unstructured data from websites, transforming it into structured data for various applications. The authors discuss different techniques, tools, and frameworks for web scraping, providing insights into the strengths and limitations of each method. They emphasize the importance of web scraping in data-driven industries, highlighting its applications in price comparison, market research, and business intelligence. The authors aim to demonstrate how businesses and researchers can leverage web scraping to gain actionable insights from large volumes of web data.

C. Context-Aware Customer Needs Identification by Linguistic Pattern Mining Based on Online Product Reviews (2023)

This paper presents a novel approach to customer needs identification by integrating context information and product functions derived from online product reviews. The authors propose a linguistic pattern mining method to extract context-aware customer needs, which are then clustered using word embedding techniques. By analyzing both the context in which products are used and their functions, the study offers a more comprehensive understanding of customer requirements. The authors' intention is to provide product developers and marketers with deeper insights into user experiences and expectations. Notably, this approach bridges the gap between traditional product feature-based analyses and the nuanced, contextual nature of customer interactions with products, potentially leading to more targeted and effective product improvements.

D. Scraping the Web for use in Text and Data Mining (2024)               

This paper presents an innovative web scraping project that leverages Next.js, MongoDB, and Nodemailer to create a dynamic price tracking platform for e-commerce products. The authors aim to provide users with real-time insights into price fluctuations, offering average, lowest, highest, and current price data. By employing web scraping techniques, the system extracts structured data from e-commerce sites, storing it efficiently in MongoDB. The use of Next.js enables a responsive front-end, while Nodemailer facilitates timely email alerts for significant price changes. Notably, the authors' approach goes beyond simple price tracking, offering potential for trend analysis and market insights. This technical stack demonstrates a sophisticated understanding of modern web technologies, combining data extraction, storage, and communication to create a robust, user-centric solution in the e-commerce space.

III. SYSTEM ARCHITECTURE

A. Identify the Target Website

Identifying the e-commerce website from which data needs to be extracted. The target website should provide valuable information, such as product prices, and allow scraping without violating its terms of service.Which includes checking for any legal restrictions, such as policies in the robots.txt file or API guidelines, which could impact the scraping operation. Selecting the appropriate website ensures that the scraped data is accurate, relevant, and useful for tracking real-time pricing changes or other metrics.

B. Sending HTTP Request

HTTP request is sent to the website’s server using appropriate methods like GET or POST. This request asks for the HTML content of the specific product page where the pricing data is located. The server responds to the request by sending back the page’s source code.

C. Receive the Response

It receives a response from the website’s server. This response contains the raw HTML content of the requested webpage.The scraper can proceed with analyzing the content. If the response indicates an error the scraper must handle it appropriately by retrying the request or terminating the process if access is denied.

D. Data Analysis

This process , it involves analyzing the response data to locate specific product price metrics, such as the current, lowest, and highest prices. The data is examined for patterns or anomalies, ensuring that only meaningful, valid information is extracted. This step sets the foundation for presenting the user with accurate, real-time insights into price changes.

E. Parse HTML

By,breaking down the raw webpage content into a structured format to locate specific elements, such as price tags, product names, or descriptions.

Enabling easy navigation through the HTML’s structure. By selecting the appropriate tags and attributes looking for the necessary pricing for further analysis and presentation.

F. Conversion of Scraped Data

This conversion makes the data easy to query, manipulate, and present in a user-friendly format on the dashboard. It also ensures that the data can be used for further analysis, reporting, or real-time tracking efficiently.

Conclusion

By automating data extraction and transforming raw HTML into actionable insights, the platform provides users with a clear understanding of a product’s pricing trends, including average, lowest, highest, and current prices. The integration of a real-time tracking system and notification features enhances the user experience by offering timely updates and valuable price insights.

References

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Copyright

Copyright © 2024 A. A. Patel, A. R Bhandare, A. S. Chauhan, S. V. Ghume , T. V. Rajarshi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.