Lead Analytics
For a a Real Estate Advisor
- The client is a Real Estate Advisor promoted by one of India’ leading Real Estate Advisory firm
- They provide potential property buyers with appropriate Properties & Developers
- Client wanted to be the first in the space to use smart analytics to identify appropriate customers for the desirable properties
- Identifying the customers from their large Digital marketing lead base who are high potential buyers
- Based on project properties, different groups of lookalike projects were made through a popular clustering technique and on each cluster a data science model was created.
- The data science models created on this data reclassify future projects into clusters that have similar properties and assign a propensity of buying to customers, after months of rigorous updating of various Data Science models, testing them and validating them by prioritizing a set of customers .
- This way recognizing patterns in customer data helped direct resources like time and manpower to customers that had a higher propensity of buying.
- These results were validated when the customers that the model suggested should be contacted showed a higher tendency of conversion.
- G-Square’s productified solution Clientrator is now implemented and is being used to track the ‘high propensity to convert ’ customers.
- Customers are prioritized or de-prioritized based on the Data Science models at various sales process stages.
- A higher conversion rate was observed from the past, which led to a marked increase in revenue and at the same time efficiency. .
