Client Background
The client is a real estate advisory firm that provides strategic and tactical advice regarding property investment, planning, and development.
Client Need
- The client wanted to create an easy-to-use tool for realtors to help analyze their end-customers and increase conversion ratio in the various lines of businesses – Buy, Sell, Rent, Lease, Break-Lease, Renew-lease. Â
- They wanted to bring the power of machine learning into their analytics application to improve the accuracy of the results and reduce the gaps between onsite and marketing people.
Our Solution
- Designed and developed predictive analytics and self-service predictive analytics workflows blending third party and own datasets.Â
- Implemented customer segmentation using ML models which helps realtors in identifying target customers group for a better conversion ratio.Â
- Implemented self-serviced ML models which allowed realtors to have personalized reporting.Â
- Developed machine learning models to get the propensity of the customer to buy/sell a property.Â
- Developed workflow to refresh predictive algorithms on an on-demand basis without any manual efforts.Â
Tools & Technologies
Python, scikit learn, AWS, MySQL, Java
Key Benefits
- Improved customer experience by providing more control through self-service predictive analytics.Â
- Increased prediction accuracy.Â
- Enabled realtors to engage effectively with each persona type, thus aiding a superior customer experience.Â
- Helped realtors in redirecting marketing efforts and campaigns in the right direction (focusing on the top 2% of prospects who are most likely to buy in a particular month) instead of reaching out to all the prospects.Â
- The machine learning pipeline helped them to build predictive models for new realtors quickly.Â
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