Automatically update a lead's score in the CRM
Posted: Wed May 21, 2025 5:42 am
NLP Models: For sophisticated sentiment analysis, intent recognition, and contextual understanding of messages.
Deep Learning (Most Advanced): For highly complex conversational analysis and predictive capabilities, often using Recurrent Neural Networks (RNNs) or Transformer models.
Model Training & Validation:
Feed your engineered features and historical outcome data (converted/not converted) to the AI model.
Split data into training and validation sets to ensure the model generalizes well to new data.
Continuously refine the model by adding new data, adjusting features, and re-training.
Integration & Automation:
Scoring Pipeline: Integrate the AI model into your Telegram bot's backend or your CRM.
Real-time Scoring: As a user interacts, data is fed to the AI model, and a score is generated.
Automated Actions:
Sales Alerts: Notify sales team via CRM or email if a lead's score crosses a "hot" threshold.
Nurturing Triggers: Automatically enroll high-score leads into specific Telegram drip campaigns.
Support Escalation: If sentiment turns philippines telegram mobile phone number list negative and score drops, automatically escalate to human support.
Practical Implementation Steps for AI-Powered Telegram Lead Scoring
Start Simple (Rule-Based): Don't jump straight to complex AI. Begin with a few key engagement actions that clearly indicate interest and assign points.
e.g., 5 points for /start, 10 points for a specific category click, 25 points for watching a full video, 50 points for requesting a demo.
Define Score Thresholds: What constitutes cold, warm, hot?
Choose a Compatible Platform: Select a Telegram bot platform that allows for custom fields, tags, and ideally, integrations with external APIs or webhooks for advanced scoring.
Ensure Data Privacy & Consent:
Clearly inform users that their interactions will be analyzed for personalization and service improvement. This falls under the "informed" aspect of consent.
Deep Learning (Most Advanced): For highly complex conversational analysis and predictive capabilities, often using Recurrent Neural Networks (RNNs) or Transformer models.
Model Training & Validation:
Feed your engineered features and historical outcome data (converted/not converted) to the AI model.
Split data into training and validation sets to ensure the model generalizes well to new data.
Continuously refine the model by adding new data, adjusting features, and re-training.
Integration & Automation:
Scoring Pipeline: Integrate the AI model into your Telegram bot's backend or your CRM.
Real-time Scoring: As a user interacts, data is fed to the AI model, and a score is generated.
Automated Actions:
Sales Alerts: Notify sales team via CRM or email if a lead's score crosses a "hot" threshold.
Nurturing Triggers: Automatically enroll high-score leads into specific Telegram drip campaigns.
Support Escalation: If sentiment turns philippines telegram mobile phone number list negative and score drops, automatically escalate to human support.
Practical Implementation Steps for AI-Powered Telegram Lead Scoring
Start Simple (Rule-Based): Don't jump straight to complex AI. Begin with a few key engagement actions that clearly indicate interest and assign points.
e.g., 5 points for /start, 10 points for a specific category click, 25 points for watching a full video, 50 points for requesting a demo.
Define Score Thresholds: What constitutes cold, warm, hot?
Choose a Compatible Platform: Select a Telegram bot platform that allows for custom fields, tags, and ideally, integrations with external APIs or webhooks for advanced scoring.
Ensure Data Privacy & Consent:
Clearly inform users that their interactions will be analyzed for personalization and service improvement. This falls under the "informed" aspect of consent.