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Real Estate CRM and Mobile App Development for Lead Management and Analytics

rubyroidlabs.com

Real estate

Lead Management and Data Processing Inefficiencies

Big Purple Dot's clients face challenges in effectively managing leads from multiple sources and leveraging data for performance optimization. Existing systems likely lacked real-time analytics and efficient data processing, hindering informed decision-making and overall business growth. The need for a mobile application to allow for on-the-go management of leads and data was also a key concern.

About the Client

Big Purple Dot is a real estate software company providing CRM and lead management solutions for lenders and realtors.

Project Goals

Develop a robust CRM system for real estate lenders and realtors.
Create a native mobile application for iOS and Android platforms.
Optimize data analytics and processing performance using Golang.
Provide real-time lead management capabilities.
Enhance data filtering and aggregation functionality.
Deliver offline mode functionality for data access.

System Functionality

Lead Management (capture, assignment, tracking)
Data Analytics and Reporting
Mobile Application (iOS & Android)
Data Filtering and Aggregation
Offline Mode
Loan Manager
Statistics Dashboard

Technology Stack

Go (Golang)

Mobile Development (Native iOS & Android)

Data Analytics Tools

External System Integrations

Various lead sources (e.g., website forms, third-party platforms)
Potential integration with existing financial systems (for loan management)

Performance and Scalability

High performance and speed (achieved through Go's concurrency)
Scalability to handle a growing volume of leads and data
Data Security
Reliable offline data access

Expected Business Impact

This project will empower Big Purple Dot's clients to improve lead conversion rates, make data-driven decisions, and enhance operational efficiency. The enhanced analytics and mobile accessibility will lead to better customer service and increased revenue. The optimized performance will reduce operational costs associated with data processing.