eCommerce sites are usually designed to support different visitor segments, but each individual comes with something specific in mind, whether it’s a holiday shopper, someone looking for a new winter jacket, or a visitor searching for a gift.
But most shoppers don’t always navigate the site the way it was designed. They start shopping halfway through a decision, with a few questions in their head and a bit of uncertainty. They’re trying to figure out what size to get, which color works with something they already own, whether it will arrive in time, or if returning it will be a hassle.
Questions like:
• “Will this fit over my base layer?”
• “Are these shoes good for standing all day?”
• “Can I get this delivered by Friday?”
The answers often exist somewhere on the site, maybe in a product description, FAQ, blog post, or return policy page, but they’re buried. And if the shopper has to go looking for it, there’s a good chance that they just won’t and they’ll abandon your site.
What these shoppers need is the kind of support they’d get from a store associate. Someone who understands their context, asks a follow-up question or two to narrow down the options, and points them towards the right product. Online, brands can deliver that experience with an AI agent.
What makes an AI agent different is that it doesn’t rely on fixed paths or assumptions about the ‘type’ of shopper. It responds to the individual person, not a pre-defined persona. When a shopper has a question, the agent responds based on what they’re really asking, not just based on the keywords they type in. And when connected to third-party systems like your CRM, product catalog, or order management tools, it can personalize the experience even further by factoring in a shopper’s purchase history and preferences.
Instead of pointing the shopper to a list of links and a page where the information might live, like traditional search does, the agent brings the answer directly into the conversation. It can:
- Compare products side-by-side when visitors are unsure of which to purchase
- Produce personalized answers about delivery timelines, sizing, or return policies instead of generic FAQs
- Find relevant alternatives when something is out of stock
- Compile complementary products that increase order value
- Deliver localized, conversational help in over 50 languages
- Generate dynamic product images and details that bring clarity in real time
When people get the answers they need, they buy with more confidence. And when they feel confident in what they’re buying, they’re less likely to return it later because it didn’t fit, didn’t arrive on time, or wasn’t what they expected.
It also helps brands get more value out of the content they’ve already created, things like older blog posts, fit guides, product details and FAQs, or other services. Instead of sitting a few clicks away, that information becomes part of how the agent supports shoppers in real time. It’s easier to act on, stays relevant longer, and shows up when it has the best chance of influencing a decision.
If you want to see what this actually looks like, we put together a quick demo using Atlas, a ski and snowboard retailer.
It shows how the agent handles common shopping questions, like “what’s the difference between these two helmets?” or “will this arrive before my trip?” You’ll see how it brings in information from product pages, policies, and guides without sending the shopper off to dig for it, and helps the shopper move from “I’m not sure” to a clear next step.