Real estate traditionally has been an industry strongly dependent on networking, reputation and broad economic trends. However, with the advent on analytics models around pricing and customer engagement, leading real estate professionals have started leveraging the power of machine learning to gain competitive advantage in the market place.
Our analytics solutions in the real estate space brings together data from diverse sources such as in-house sales and enquiry data, referral data from existing customers, data from aggregator websites and e-commerce marketplaces, marketing databases, social media data, real estate inventory information, data obtained from expos and other marketing initiatives, to create reusable analytics models which offer the following benefits:
By matching the right inventory to the right prospect, our solutions have helped clients in significantly reduce the sales cycle and improve conversion rates.
Our analytics solutions have helped clients in the real estate sector to gain deeper insights about customers leading to better customer engagement through personalized and customized communication and product offerings.
Our predictive fraud analytics models assist clients in flagging off potential fraudulent clients and transactions before they result in a financial loss. Our proprietary analytics driven credit scoring models help evaluate risks at every stage of the credit lifecycle, and suggest effective intervention strategies, thus minimizing risks of default.
With analytics enabling focused targeting and relevant messaging, real estate professionals are able to generate significantly higher Return on Investments for their marketing dollars.
Some of our analytics based accelerators for our real estate clients include:
The Lead Qualifier algorithm uses advanced analytics and machine learning techniques to qualify leads with probability of conversion. This allows sales people to focus their efforts on those leads with the highest probability of conversion.
Sales agents often rely on third party databases for direct marketing initiatives. The Prospector uses look alike modeling techniques to identify prospects who are similar to past customers and who have a high propensity to purchase.
The ‘Repeat Modeler’ evaluates existing customer databases and identifies the candidates who are most likely to be repeat purchasers by applying machine learning models on customer data and inventory data using parameters such as demographics, life-stage, income levels and location preferences.
Our ‘Persona Builder’ analyzes transaction and demographic data to identify typical characteristics of different customer personas of the client. Our domain experts provide recommendations on how to engage effectively with each persona type, thus enabling a superior customer experience.
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