Hello, I am creating specialized AI agents for e-commerce stores to improve customer experience and increase conversions. Those agents will understand your brand’s identity and will accurately answer questions about your products and catalog.
I am not selling anything and would just like to understand online merchants’ needs to ensure we address real issues.
What are the main reasons why you use chatbots in your online store? What do you expect from them? Are those expectations met? What current solutions do you use? Are you happy with them? How could it be better? If you had a magic wand, how would you improve your customer’s experience?
If you would be open for a quick 30 min chat, please feel free to connect with me on:
Linkedin: https://www.linkedin.com/in/francknijimbere/
Twitter: https://twitter.com/nijfranck
Great question, Franck. I work in this space daily, so happy to share what I’ve seen work well across different e-commerce stores.
When it comes to chatbots, most merchants I speak with expect three core things:
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Real product understanding, not just repeating site content, but being able to answer specific questions like “What’s the difference between X and Y?” or “What’s available in size M under $50?”
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Conversational tone, ideally, the bot should reflect the brand voice and not feel robotic or scripted.
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Conversion support, meaning the ability to guide users toward the right product, surface relevant bundles or upsells, and help move them closer to checkout.
More advanced use cases also include remembering past interactions or tailoring offers based on user behavior, but that’s still an area evolving for many tools.
Platforms like Tidio, Zendesk, Manychat, and Qualified each approach this differently. Some focus more on support workflows and AI automation like Tidio, others on conversational marketing like Manychat. The best results usually come from bots that are tightly integrated with your product catalog and require minimal effort to keep up to date.
Let me know if you want to dig into any specific use case — happy to chat!
One thing I’d add is that the best chatbot doesn’t just answer questions it knows when to hand the conversation off to a real person. Fast, accurate answers for routine questions, plus a smooth human handoff for more complex cases, usually creates the best customer experience.
HI @nijfranck
I think the biggest reason merchants use chatbots is to provide instant answers without increasing support workload. Questions about shipping, order tracking, returns, product availability, sizing, and product recommendations account for a large share of customer inquiries.
Where many chatbots still fall short is handling conversations naturally. Customers expect the bot to understand context, provide accurate product information, and know when to hand the conversation over to a human instead of giving generic or repetitive responses.
If I had a wish list, it would include:
- Personalized recommendations based on the customer’s browsing behavior and purchase history.
- Accurate answers grounded in the store’s actual product catalog and policies.
- Seamless escalation to a human with the full conversation history.
- Support for multiple languages.
- Analytics that show which questions customers ask most and where the chatbot fails to answer effectively.
The best chatbot is one that feels genuinely helpful, resolves common questions quickly, and improves the shopping experience without creating friction.
The core issue is grounding, not conversation design. Most chatbots fail because they’re trained once on scraped content instead of querying live data. The fix: connect the bot to Shopify’s Admin API and Storefront API directly, not a static knowledge base you upload and forget. Every product query should trigger a real time lookup against variants, inventory, and price, so “what’s available in size M under $50” is answered by a live filtered query, not a language model guessing from stale text.
For tone and accuracy together, use retrieval augmented generation: the model retrieves the exact product records and policy text relevant to the question, then generates a response constrained to that retrieved data. This is what stops hallucinated specs or made up return windows. If nothing relevant is retrieved, the bot should say it doesn’t know rather than fabricate an answer.
For escalation, the practical implementation is intent classification running alongside the RAG pipeline. Frustration signals, repeated failed queries, or explicit “talk to a person” requests should trigger handoff, and the full message thread plus any structured context (cart contents, order ID if mentioned) needs to pass to the human agent’s interface automatically. Losing that context is the single biggest complaint in every merchant thread I’ve read on this.
Personalization and multilingual support are lower priority engineering wise, they’re mostly configuration once the retrieval and API layer is solid. Analytics on failed queries matter more than people think: log every query that returns low retrieval confidence, review weekly, and you’ll find catalog metadata gaps faster than any other method.
Bottom line: the bot is only as good as its data pipeline. Fix the retrieval and live inventory connection first, everything else is secondary.