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Real Estate Sales Prediction & Lead Optimization Platform Enhancement

edvantis.com

Real estate

Challenge

Edvantis's client, a real estate data analytics company, faces challenges in efficiently identifying high-potential properties and leads to improve sales performance. Existing systems require optimization for handling large datasets, and there's a need for advanced analytics, including machine learning, to predict sales and optimize lead engagement. The legacy system is hindering agility and scalability.

About the Client

Provides data aggregation and analytics solutions for the real estate industry, focusing on lead generation and sales forecasting.

Main Goals

Develop and deploy a machine learning-powered sales prediction model to improve forecast accuracy.
Optimize database queries and data processing pipelines for faster and more efficient data retrieval.
Enhance lead management capabilities with AI-driven prioritization and engagement recommendations.
Create a scalable and maintainable platform architecture leveraging cloud infrastructure (AWS).
Automate data acquisition and augmentation from diverse sources, including public websites.
Implement natural language processing (NLP) to analyze agent notes and identify key insights.

Functional Requirements

Automated web crawling and data extraction.
Scalable database querying and data processing.
Machine learning models for sales prediction and lead scoring.
Lead management portal with filtering, search, and prioritization features.
Integration with third-party dialing services.
Natural language processing of agent notes.
Reporting and analytics dashboards.

Preferred Technologies

AWS (Amazon Sagemaker, Redshift, Elastic Beanstalk, VPC, Elasticsearch)

Java

Spring

JavaScript

MySQL

BERT (for NLP)

Gradient Boosting Machines (XGBoost, LightGBM, AdaBoost)

Random Forest

K-Nearest Neighbors (kNN)

Integrations Required

Third-party dialing service

Key Non-Functional Requirements

High scalability to handle large datasets and concurrent users.
Fast query execution times (under 5 seconds for 99% of queries).
High availability and reliability.
Robust security measures to protect sensitive data.
Automated CI/CD pipeline for continuous deployment.
Data integrity and quality.

Estimated Impact

This project is expected to significantly improve sales efficiency by providing data-driven insights and automated lead prioritization. The optimized platform will enable faster decision-making, increased lead conversion rates, and enhanced agent productivity. The use of machine learning will lead to more accurate sales forecasts, allowing for better resource allocation and strategic planning.