AI-Driven Recommendation System Boosts Lead Conversion by 5x for India’s Largest Property Portal
Before
One of India’s largest property search portals was facing challenges with low lead closure rates. The telecalling unit struggled to provide effective recommendations, leading to missed opportunities and lower-than-expected conversions. The Company needed a solution to optimize the recommendations made to potential buyers, thereby improving lead closure rates.
Solution
Our data scientists developed a powerful recommendation system using an ensemble model that combined the strengths of XGBoost and Random Forest algorithms. Key features of the solution included:
- The ensemble model analyzed the nature of interactions between telecallers and potential leads, optimizing the recommendations to ensure they were more likely to result in conversions.
- Hierarchical clustering was implemented to further personalize recommendations based on specific user segments, allowing the telecalling unit to target their efforts more effectively.
- The result was a highly efficient recommendation system that significantly increased lead conversion rates and supported rapid scaling of the sales team, contributing to the Company’s strong growth in the competitive property market.
Outcome
With the implementation of an AI-driven recommendation system, lead conversion rates improved fivefolds. The enhanced system was also scalable, supporting larger telecalling units without significant increases in technology overheads.
