Develop a machine learning model to calculate lead scores based on demographics, behavior, engagement, and communication data. Implement labeling and tagging mechanisms to classify and organize leads based on interaction types, content, and sentiment. Build a natural language processing (NLP) module to analyze incoming messages, emails, and communication history for intent and tone. Create a persona identification engine for precise lead segmentation. Integrate email and message analytics (open rates, response patterns, sentiment trends) into lead scoring. Implement a Hot/Cold lead alert system to detect significant status or engagement changes. Develop an early pipeline prediction model to identify high-value or at-risk leads. Deliver a secure, modular AI pipeline with retraining logic and full API integration into the CRM as part of the system’s core architecture.
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