[Webinar] From Search to Chat: Unlocking Product Discovery in the Age of AI & Conversational Commerce
Commerce is shifting from single-site browsing to commerce anywhere: buying at the moment of inspiration via TV, voice assistants, influencers, or image snapping. At the same time, generative AI has brought search back to the center: shoppers now ask conversational questions across engines and assistants (e.g., ChatGPT), expecting context-aware results.
This creates two mandates for retailers:
- Modernize on-site search to understand intent, not just keywords.
- Make products discoverable off-site where shoppers initiate AI-driven research.
The Evolution: From Keywords to Understanding
Search has moved far beyond keyword matching and backlink games. With semantic models and LLMs, engines understand content and shopper intent. For merchants, this means:
- Optimize on-site search for conversational, intent-rich queries.
- Ensure product data and content are structured and complete so AI services can parse and surface it.
Vector Search: Meeting Shoppers’ Intent (Not Just Their Keywords)
Vector search recognizes concepts, not just exact terms. If a shopper types “party dress,” vector search can retrieve relevant products even if “party dress” isn’t a literal tag, positioning results between related ideas like cocktail and elegant dress.
Why it matters:
- Handles conceptual and long-tail queries.
- Works alongside traditional keyword search; results can be re-ranked for relevance.
- Reduces manual synonym management thanks to semantic understanding.
Prefixbox highlights rolling out AI vector search and measuring real impact, citing a retailer case with >45% revenue increase and >28% AOV uplift.
If you’re on Salesforce Commerce Cloud, Prefixbox is available via AppExchange, enabling this capability on your store.
Be Discoverable Where Shoppers Start (GPT & Friends)
Shoppers increasingly research and buy through conversational assistants. Example from the webinar: a user asks for hiking pants and receives specific product models plus direct links to brand sites—all within one chat.
How to surface your products in conversational engines:
- Complete, rich product data: size, color, brand, category, attributes, plus correctly tagged images.
- Structured content: clear schema and consistent information architecture.
- Natural-language product descriptions: write like you’re answering a question; think FAQs, buying guides, product comparisons, and reviews.
- Classic SEO still counts: titles, headings, meta descriptions, internal linking, performance, and overall brand/domain reputation.
Reality check: GPT isn’t brand-exclusive; it can (and will) recommend competitors. Your content quality, structure, and authority determine whether you’re included.
Conversational Commerce On Your Site: Agents That Do the Work
Shoppers crave conversational experiences. If you don’t provide them, they’ll have them elsewhere. The transcript introduces Salesforce Agentforce as a way to bring this experience into your channels.
What Agentforce enables:
- Starts with a conversation (text/voice), forms a plan, and performs actions.
- Uses retrieval augmented generation (RAG) to safely access your data (catalog, order status, customer info).
- Personal Shopper Agent (announced at Dreamforce; GA in February per transcript) to:
- Answer questions, make product recommendations,
- Add to cart, assist checkout, and handle order tracking (capabilities expand over time).
- Works across your website and channels like WhatsApp/iMessage.
Practical Roadmap: Test, Measure, Operationalize
- Test & Learn: Try multiple AI use cases and UI patterns (chat UI vs. rich grid after a long query).
- Measure: Track what boosts engagement and conversion for your audience.
- Operationalize:
- Get the right product data in the right structure.
- Feed search/agents with real behavioral data to improve recommendations.
- Keep content conversational and comprehensive (FAQs, comparisons, guides, reviews).
FAQ
Q: Does vector search replace keyword search?
A: No. They work together. Vector search handles conceptual queries; keyword handles exact matches. Re-ranking brings the best results up first.
Q: How do I get featured in GPT-style recommendations?
A: Provide complete, structured product data, conversational descriptions, and authoritative content (reviews, guides, FAQs). Maintain strong technical SEO and brand trust signals.
Q: What does an AI agent actually do on my site?
A: It converses with shoppers, retrieves data, recommends products, and (as capabilities expand) helps with cart, checkout, and support—all within a trusted, guardrailed system.
Conclusion
Product discovery now spans on-site semantic search and off-site conversational engines. Retailers who pair vector + keyword search, invest in structured, conversational content, and deploy on-site AI agents will win the next era of discovery—from search to chat.
For more details, watch the full recording of the webinar:
