Zakaria H.

The Problem (Context): The team was wasting precious time manually qualifying each prospect and entering data into the CRM. Result: long response times and missed opportunities ( 'cold' leads).

My Solution (Action): Design and deployment of a complete automated ecosystem:

Lead Centralization: Connecting sources (SeLoger, Leboncoin, Web Form) to a single database via n8n.

AI Qualification: Integration of an OpenAI agent to analyze the urgency and budget of email requests.

Notifications & Tracking: Creation of instant alerts for agents on 'hot' leads and automatic sending of a discovery form to the prospect.

The Results (Proof):

⚡ Immediate Responsiveness: Average response time changed from 4h to < 5 minutes (personalized acknowledgment of receipt).

📉 Time Savings: Approximately 8 administrative hours saved per week for the sales team.

✅ Reliability: 100% of leads correctly entered into the CRM without human error.

Optimization of queries and data model, leading to a 25% reduction in processing time. Analysis and processing of several thousand network records using Hadoop, Spark, and Hive, contributing to an estimated ~15% improvement in the performance of targeted BTS stations. Application of machine learning algorithms for classification, with better identification of high-traffic areas (+10–15%). Development of a REST API in Node.js for user and data management.