Sales Tracker
An AI-Powered Analytics Platform
A sophisticated sales analytics dashboard that provides real-time insights, predictive forecasting, and AI-driven recommendations to help sales teams optimize performance and enhance decision-making.
Back to ProjectsRole
Full-Stack Developer - Responsible for architecture design, UI implementation, and integration of AI capabilities
Timeline
6-month development cycle with iterative releases
Client
Enterprise SaaS company (confidential) in the B2B technology sector
Problem & Approach
The Challenge
Sales teams struggled with fragmented data sources, lack of real-time insights, and manual forecasting processes that were time-consuming and error-prone. They needed a unified platform that could provide actionable insights, automate reporting, and offer predictive analytics to optimize sales strategies.
My Approach
- •Conducted extensive user research and requirements gathering
- •Created a modular architecture allowing for component reusability and maintainability
- •Implemented an iterative development process with regular client feedback
- •Utilized Supabase for secure data storage and authentication
- •Integrated Python-based ML models for predictive analytics
- •Designed a responsive UI that works seamlessly across devices
Technical Challenges
Real-time Data Synchronization: Implemented WebSocket connections between React frontend and Python backend to ensure data consistency across multiple users
Complex Data Visualization: Created custom, interactive charts that could handle large datasets while maintaining performance
Supabase Integration: Developed a robust data schema and authentication system using Supabase, including row-level security for proper data access control
AI Model Integration: Built a bridge between frontend and Python ML models to deliver predictive insights while maintaining responsive UI
Performance Optimization: Implemented virtualization and efficient state management to handle large datasets without compromising UX
Drag-and-Drop Functionality: Created a custom widget system allowing users to personalize their dashboard layout
Technologies Used
Frontend: Next.js, React, TypeScript, Tailwind CSS, shadcn/ui components
Backend: Python (FastAPI/Flask), Supabase for database and authentication
Data Visualization: Custom charting components with dynamic data handling
AI/ML: Machine learning models for sales forecasting and insights
Real-time: WebSockets for live updates and notifications
State Management: React Context API for global state
Deployment: Vercel (frontend), Render.com (Python backend)
Key Features
Customizable dashboard with drag-and-drop widget system
Interactive data visualization and graphing capabilities
AI-powered chatbots (Quickfix and Lifecycle Insights) for instant assistance
Performance analysis tools with historical data comparison
Predictive sales forecasting using ML algorithms
Real-time notifications and activity tracking
User profile and preference management
Comprehensive data export capabilities
Sales playbook generation based on successful patterns
Theme customization with light/dark mode support
Results & Impact
Reduced sales reporting time by 65%
Improved forecast accuracy by 37% compared to previous methods
Increased sales team productivity by 28% through actionable insights
Enabled data-driven decision making with 92% user adoption within the first month
Decreased onboarding time for new sales representatives by 45%
Platform successfully handles over 10,000 daily transactions with sub-second response times
Positive user feedback highlighting intuitive interface and valuable insights
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