Conversational Commerce And Marketing

Use AI-driven chat and voice to personalize and automate customer commerce interactions

Business impact

• Conversion rate — Improved by guiding customers through personalized, seamless purchase conversations
• Customer engagement — Increased via real-time, natural language interactions across messaging platforms
• Customer satisfaction — Raised by providing instant, relevant support and personalized recommendations
• Operational efficiency — Enhanced by automating routine inquiries and sales processes with AI agents
• Average transaction value — Boosted through personalized upselling and cross-selling during conversations

Data requirements

• Customer interaction logs (Text) — Used to train AI on typical queries and improve response relevance
• Product catalog data (Structured) — Provides detailed product information for accurate recommendations
• Customer purchase history (Structured) — Enables personalization by understanding preferences and buying patterns
• Voice recordings (Audio) — Supports voice AI for natural spoken interactions and tone control
• Chat transcripts (Text) — Help refine conversational flows and AI understanding of customer intent

AI methods and techniques

• Generative AI — Generates natural language responses and personalized product recommendations
• Predictive AI — Predicts customer intent and next best actions to guide conversations
• Agentic AI — Autonomously manages multi-turn conversations and transaction processing

AI models and model families

GPT-4, Claude, Llama 2, OpenAI Codex

Industries

Real-world evidence

15 documented case studies on record.

Companies using this: Bluecore, Botmaker, Chalhoub Group, Cognigy, Domino Pizza, Expertise AI, Go Kwik, MEDILASE, Maxim Group, Merx, Pay Pal, Pneu Store, Popcorn AI, Rezolve AI, Starbucks Corp.