Canada
Puerto Rico
USA
Belgium
France
UK
India
Client Background
The client is a leading real estate advisory firm, delivering strategic insights and actionable solutions for property investment, planning, and development.
Client Need
The client faced challenges in effectively analyzing end-customer data, resulting in low conversion rates across their diverse real estate business lines (buy, sell, rent, lease, break-lease, renew-lease). Their existing analytics application lacked the accuracy and predictive power of machine learning, leading to unreliable results and a disconnect between onsite and marketing teams.
Solution
Predictive Insights: Designed and developed self-service predictive analytics workflows, blending third-party and proprietary datasets to enhance customer understanding and improve conversion rates
ML-Driven Segmentation: Implemented customer segmentation using machine learning models, allowing realtors to identify target customer groups
Personalized Reporting: Implemented self-service ML models, enabling realtors to generate personalized reports
Customer Propensity Modeling: Developed machine learning models to predict customer propensity to buy or sell property, providing realtors with valuable leads
Data Accuracy and Agility: Developed a workflow to refresh predictive algorithms on demand without manual intervention
Realized Benefits
Improved customer experience by providing more control through self-service predictive analytics
Increased prediction accuracy
Enabled realtors to engage effectively with each persona type, thus aiding a superior customer experience
Helped realtors in redirecting marketing efforts and campaigns in the right direction (focusing on the top 2% of prospects) instead of reaching out to all the prospects
Empower new realtors with swift predictive insights by leveraging the automated machine learning pipeline
Tools & Technologies
Python
Scikit learn
AWS
MySQL
Java
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