Rich User Profiles for Smart Replies

[asikurrahmanshuvo]

The "intelligence" of a Smart Reply Bot is directly proportional to the richness and accuracy of the user profiles it accesses. These profiles are built from comprehensive data collected ethically within your Telegram ecosystem.

Zero-Party Data (Explicitly Provided by User): This is the most valuable and trustworthy data source for personalization.

Collection Methods: Bot onboarding quizzes, in-bot surveys/polls, preference centers, direct questions (e.g., "What's your primary goal today?").
Profile Attributes:
Interests/Preferences: Specific product categories, content types, topics of interest (e.g., interest:software_dev, prefers:video_tutorials).
Goals/Pain Points: User-stated objectives or challenges (e.g., goal:increase_sales, pain_point:slow_internet).
Demographics/Firmographics: Role, industry, company size, location (if volunteered).
Communication Preferences: Preferred language, frequency of updates.
Behavioral Data (Observed User Actions within Telegram): This infers intent and context from user interactions.

Collection Methods: Tracking bot interaction paths, content israel telegram data consumption (views, clicks, reactions), engagement metrics (activity frequency, last seen), search queries within the bot/channel.
Profile Attributes:
Product/Service Interaction History: Viewed products, clicked demo links, completed trial sign-ups, abandoned cart status (e.g., viewed:product_X, flow_completed:trial_sign_up).
Content Consumption History: Which articles, guides, or videos they've accessed (e.g., read:AI_guide, watched:onboarding_video).
Support History: Previous support queries, resolution status (e.g., support_ticket:open_billing, support_ticket:closed_technical).
Engagement Level: Active, semi-active, dormant (e.g., status:high_engagement, status:churn_risk).
Contextual Data.