Intent Classification

Overview

Intent Classification is a core feature of the SQ3 platform that uses Natural Language Processing (NLP) to automatically categorize customer messages by their intent. This enables contextual automation, relevant responses, and forms the foundation for analytics and personalized communication.

How It Works

The Intent Classification system analyzes incoming messages across multiple channels (Facebook, Instagram, WhatsApp) and:

1. Extracts Meaning: Uses advanced NLP to understand the context and meaning of customer messages
2. Classifies Intent: Categorizes messages into primary and secondary intents (e.g., inquiry, payment, feedback, complaint)
3. Calculates Confidence: Provides confidence scores for each classification
4. Suggests Actions: Recommends appropriate actions based on the identified intent
5. Assesses Urgency: Determines the urgency level to prioritize responses

Key Features

Multi-Intent Recognition

The system identifies both primary and secondary intents, allowing for more nuanced understanding of customer needs. For example, a message might have a primary intent of "Product Inquiry" with secondary intents of "Availability Check" and "Purchase Intent".

Confidence Scoring

Each classification includes a confidence score (0-100%) that indicates how certain the AI is about the classification. Higher confidence scores typically result in automated responses, while lower scores may trigger human review.

Reasoning Explanation

The system provides explainable AI reasoning for each classification, showing why a particular intent was identified. This transparency helps build trust and allows for continuous improvement.

Urgency Detection

Messages are automatically tagged with urgency levels (low, medium, high) based on language patterns, sentiment, and context. High-urgency messages are prioritized in the queue.

Intent Categories

The system classifies messages into four main categories:

• Sales: Product inquiries, pricing questions, purchase intent
• Support: Technical issues, order problems, complaints
• Service: Appointment booking, consultations, service requests
• Information: General questions, FAQ-like queries

Suggested Actions

Based on the classified intent, the system suggests appropriate actions such as:

• Checking inventory availability
• Providing product details and pricing
• Escalating to customer service
• Processing refunds or returns
• Scheduling appointments
• Sending follow-up messages

Expected Outcomes

• 70% reduction in manual message handling through automated classification
• Accurate classification of customer intents enabling faster response cycles
• Improved response times by prioritizing urgent messages
• Better customer satisfaction through relevant, context-aware responses

Technical Details

The Intent Classification system uses:

• Natural Language Processing (NLP) models trained on customer service interactions
• Multilingual support for Sinhala and English
• Continuous learning from interaction logs to improve accuracy
• Real-time processing with sub-second response times

Integration

Intent Classification integrates seamlessly with:

• Unified Inbox: Classifies messages from all channels
• Knowledge Base: Uses FAQ data to improve classification accuracy
• Automation Engine: Triggers automated responses based on intent
• Analytics Dashboard: Provides insights into intent patterns and trends