Customer Profile Scraping for Real Estate Industry

Azati Labs developed a progressive web scraping platform for a US-based real estate firm. The solution scrapes customer data from various websites and compiles it into a single, interactive dashboard. The project aimed to help real estate agents gain deeper insights into potential customers before they sign contracts, while adhering to strict data privacy laws in Northern California.

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All Technologies Used

Golang
Golang
Webloop
Webloop
Vue.js
Vue.js

Motivation

The goal was to create a system that could automatically scrape information about potential clients from websites like Yelp, TripAdvisor, Facebook, and Airbnb. The solution needed to bypass privacy protections and provide a reliable way to aggregate this information into actionable customer profiles.

Main Challenges

Challenge 1
Website Restrictions and Privacy Policies

The main challenge was the restrictions imposed by websites like Yelp, TripAdvisor, and Facebook, which had strict privacy policies. To avoid detection and bans, the team researched how these sites track user behavior and developed algorithms to bypass these restrictions.

Challenge 2
JavaScript Rendering and Performance Issues

Modern websites heavily rely on JavaScript frameworks like React, Angular, and Vue, which posed a challenge for traditional web scrapers. To address this, the team used Golang and WebLoop to implement JavaScript rendering. However, the resource-intensive nature of this task required a dedicated server with multiple cores and threads.

Challenge 3
Data Matching and Aggregation

The collected data often lacked the required precision, especially when dealing with common names. To address this, the team worked on intelligent data matching algorithms and manual verification, ensuring that the correct customer profile was built despite the complexity of the data.

Key Features

  • Web Scraping: The platform scrapes data from various websites like Yelp, TripAdvisor, and Facebook, collecting customer information in real-time.
  • Data Matching Algorithm: The solution includes an algorithm that matches customer data across different websites, helping real estate agents build detailed profiles.
  • Interactive Dashboard: An interactive dashboard displays all the collected and matched data in a clear, accessible format for the real estate agents to analyze.
  • Privacy Protection Bypass: The platform uses advanced algorithms and proxies to avoid detection from websites with strict privacy policies.

Our Approach

Overcoming Restrictions
We developed algorithms that mimicked regular user behavior, allowing us to bypass restrictions and avoid detection from websites with strict privacy policies.
Leveraging Golang and WebLoop
The team utilized Golang for its multithreading capabilities and used the WebLoop library to efficiently handle JavaScript rendering, ensuring that modern websites could be scraped effectively.
Building a Matching System
We implemented an intelligent data-matching algorithm that partially automated the process of associating records, using variables like usernames, email addresses, and education details. This helped the customer manually verify the data while keeping costs down.
Phased Development
The project was developed in phases, starting with the creation of basic scraping scripts, then integrating these into a full system with a UI built in Vue.js. The first MVP included data matching features, which were improved based on customer feedback.

Project Impact

The prototype was successfully developed within three weeks and received positive feedback from the customer. The platform provides real estate agents with a powerful tool to better understand their clients, while adhering to strict data privacy laws. The solution enabled the customer to aggregate valuable information from multiple sources, ultimately improving their ability to make informed decisions.

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