Case study • Real Estate
Maximizing Operational Efficiency: Automated Real-Estate Data Scraping to Cut Gathering Time by 2 Hours & Increase Revenue by 190%
Our client is a leading real estate solutions provider with a platform designed to help agents grow their rental businesses and convert renters into buyers. As their user base grew, manual data collection became too slow and inconsistent to support real-time insights.
Success Highlights
Key Details
Industry: Real Estate Solutions Geography: United States
Platform: Cloud + Automated Scraping & Analytics
Business Challenge
The client relied on scraping to collect market data, but the process was slow, brittle, and difficult to scale. Manual steps created gaps in accuracy and delayed updates, limiting visibility for end users.
Our Solution Approach
We automated their data scraping and analytics processes, delivering faster, cleaner data that now powers better decisions and stronger customer engagement.
1 · Build
Scalable Scraping Framework
We created a modular framework to standardize how scraping logic, data sources, and validations were defined. This removed fragile one-off scripts and gave the team a foundation that could support new markets and listing types with minimal effort.
2 · Automate
End-to-End Automated Scraping
We automated the scraping process using AWS, Selenium, and custom handlers to manage long-running URLs and dynamic pages. Retries, failures, and timeouts were handled programmatically, eliminating manual involvement and improving consistency across market updates.
3 · Overcome
Security Bypass + Centralized Dashboards
We used custom code and libraries to bypass Cloudflare, Captcha, and similar blockers, ensuring uninterrupted extraction. We also streamlined Tableau and Shiny dashboards, removing stale views and improving access to current, accurate insights.
4 · Align
Ad-hoc Analysis & Intelligence Layer
We delivered custom reports and on-demand analysis that revealed new usage patterns, emerging markets, and opportunities for customer-service improvement. These insights helped the team make faster, more informed decisions backed by reliable data.
Technical Highlights
Automated AWS-driven scraping pipeline
Cloudflare and Captcha bypassing logic
Selenium orchestration for dynamic pages
Modular, scalable scraping framework
Real-time dashboards in Tableau and Shiny
Insights generation for product and marketing teams
// Pseudocode: — Data Scraping Flow
targets = loadSites()
for site in targets:
html = fetch(site, bypass=True)
data = parse(html)
cleaned = validate(data)
save(cleaned)
Business Outcomes
2 hours saved
Per cycle: Automated flows replaced manual collection, cutting the time required for updates and improving operational efficiency.
190%
Increase in revenue: Faster updates and better data quality improved customer experience and drove higher usage, which directly contributed to revenue gains.
147%
Rise in Product Usage: Streamlined flows and clearer insights encouraged more agents to rely on the platform daily.
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