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AI-Powered Automated Lead Acquisition and Classification Platform
  1. case
  2. AI-Powered Automated Lead Acquisition and Classification Platform

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AI-Powered Automated Lead Acquisition and Classification Platform

emerline.com
Advertising & marketing

Challenges in Manual Lead Processing and Classification

The client's existing manual lead acquisition process suffered from severe inefficiencies: 1) Time-consuming multi-channel lead collection and classification requiring hours per lead 2) High error rates from manual data processing leading to misclassified leads and revenue loss 3) Scalability limitations causing system performance degradation with increasing lead volumes 4) Inflexible pay-as-you-go pricing model failing to retain frequent users

About the Client

B2B marketing technology startup providing digital lead generation solutions for enterprise clients

Project Goals for Automated Lead Acquisition Platform

  • Automate end-to-end lead processing across multiple communication channels
  • Implement ML-driven lead classification with 90%+ accuracy
  • Ensure horizontal scalability for 10x lead volume growth
  • Develop hybrid pricing model combining subscription tiers with usage-based billing

Core System Functionalities

  • Multi-channel integration (Meta platforms, WhatsApp, SMS, email)
  • Machine learning classification engine using historical interaction patterns
  • Automated outreach campaign management tools
  • Real-time CRM synchronization
  • Lead scoring dashboard with analytics

Technology Stack Requirements

AWS cloud infrastructure
Python-based ML models
Node.js backend
React frontend
MongoDB NoSQL database

External System Integrations

  • Meta Graph API
  • Gmail API
  • Twilio SMS gateway
  • HubSpot CRM
  • Stripe payment processing

System Performance Requirements

  • Handle 10,000 concurrent users with <2s response time
  • 99.99% system availability SLA
  • GDPR-compliant data encryption
  • Auto-scaling architecture for traffic spikes

Expected Business Impact of Automation

Implementation of the AI-powered platform is projected to reduce lead processing time by 80%, decrease classification errors by 75%, and enable 5x faster campaign execution. The hybrid pricing model is expected to increase customer retention by 40% while maintaining 30% gross margins. Scalability improvements will support growth to 1 million monthly leads without infrastructure cost increases.

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