Telegram Bot Platform: Ensure your bot platform can capture all relevant interaction data (user IDs, timestamps, message content, button clicks, flow paths).
CRM: Sync this data to your CRM. Use custom fields in your CRM to store Telegram-specific engagement metrics.
Data Lake/Warehouse (for advanced users): For large-scale operations, centralize all interaction data for AI model training.
Feature Engineering:
This is where raw data is transformed into features that an AI model can understand.
Text Data:
Embeddings: Representing words/phrases new zealand telegram mobile phone number list numerically (e.g., Word2Vec, BERT) to capture semantic meaning.
Keyword Extraction: Identifying specific terms.
Sentiment Scores: Using pre-trained sentiment analysis models.
Behavioral Data:
Frequency: Messages per day/week.
Recency: Days since last interaction.
Engagement Scores: Number of clicks, flow completions.
Time-in-flow: How long a user spent in a specific bot flow.
Choose Your AI Model:
Rule-Based (Basic AI): Start here for simplicity. Define explicit rules.
Example: IF message contains "pricing" THEN add 10 points. IF user completes "demo request" flow THEN add 50 points.
Machine Learning (More Advanced):
Classification Models: (e.g., Logistic Regression, Support Vector Machines, Random Forests, Gradient Boosting)
Training Data: Requires historical data of Telegram users labeled as "converted" or "not converted." The AI learns patterns of engagement that led to conversion.
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