This project focuses on building an automated ranking monitoring system for Airbnb listings, designed to track and monitor the rankings of specific hotels over a 30-day period. The system captures essential data such as rankings, pricing, and competitors' average prices, providing actionable insights for optimizing pricing strategies and improving market positioning.
Key Features:
- Automated tracking of hotel rankings on Airbnb based on selected filters.
- Daily data capture over a 30-day period for consistent trend monitoring.
- Scraping of competitor pricing data to benchmark and assess market positioning.
- Spreadsheet integration for enhanced data visualization and analysis.
- Customizable filters and parameters for flexible tracking based on client needs.
Project Challenges:
- Dynamic Content and Anti-Scraping Mechanisms: Airbnb’s dynamically loaded content required sophisticated scraping methods to handle JavaScript-rendered elements and avoid anti-scraping blocks.
- Data Volume and Consistency: Gathering daily data for multiple hotels over 30 days presented challenges in managing large datasets, ensuring consistent data quality and completeness.
- Competitor Pricing and Average Price Analysis: Developing a system to accurately extract and average competitors’ pricing information involved additional parsing and processing to generate meaningful insights.
- Spreadsheet Integration for Analysis: Automating the process of inserting data into spreadsheets in an organized format required careful planning to maintain a clean, analyzable structure, suitable for price comparison and ranking trend visualization.
Technologies Used:
- Python: Core language for web scraping data processing and automation
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Selenium: Used for handling dynamic and JavaScript-rendered content on Airbnb
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Pandas: For data manipulation and organization before exporting to spreadsheets
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Google Sheets API: Integrated for seamless data insertion and updating in Google Sheets enabling easy access and visualization
