Implemented self-serviced ML models for a real estate advisory firm which allowed personalized reporting
- 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.
- 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.
- Designed and developed predictive analytics and self-service predictive analytics workflows blending third party and own datasets.
- Implemented customer segmentation using ML models which helps realtors in identifying target customers group for a better conversion ratio.
- Implemented self-serviced ML models which allowed realtors to have personalized reporting.
- Developed machine learning models to get the propensity of the customer to buy/sell a property.
- Developed workflow to refresh predictive algorithms on an on-demand basis without any manual efforts.
Tools & Technologies
- 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 who are most likely to buy in a particular month) instead of reaching out to all the prospects.
- The machine learning pipeline helped them to build predictive models for new realtors quickly.