Q&A: How brands should measure AI’s real impact after checkout

As AI becomes a bigger part of how consumers discover products and make purchase decisions, how should brands actually measure whether it’s delivering value? Is success really about driving more traffic and conversions, or should brands pay closer attention to what happens after checkout?

This hits on an interesting intersection of ecommerce, AI, customer experience, and timely topics with economics and consumer spending such a focus right now. Many consumers, for example, are using AI to make purchasing decisions before they buy, or are increasingly looking for options to exchange items instead of requesting refunds as they try to make every purchase count

This Q&A is with Andrew Schmid, Chief Product Officer at Route. A Schmid previously led digital consumer experience and post-purchase strategy at Nike before joining Route, where he now oversees product, design, marketing, analytics and customer experience for a platform that works with more than 13,000 ecommerce brands. 

Digital Journal AI is rapidly changing how consumers discover products and how brands engage with their customers. But agentic shopping takes this to a new level. What is your realistic outlook for consumer adoption of agentic shopping tools, and what factors will determine how quickly these move from experimentation to mainstream use?

Andrew Schmid: Consumers are adopting AI quickly when it removes friction, but they’re still cautious about letting AI make purchasing decisions. That confidence will grow only if AI consistently delivers accurate recommendations and reliable shopping experiences.

For brands, it’s more important than ever to have up-to-date, accurate product information throughout their online presence, including places like their product discovery pages. Success in shopper discovery depends on having reliable product data, properly optimized for AI search so it offers the right recommendations. Additionally, having a much more integrated payments ecosystem in place before shoppers can transact on AI interfaces will continue to be key to consumer adoption. Until those fundamentals are in place, I expect most shoppers will continue using AI as a discovery tool, rather than using it to transact and check out.

DJ: Where are you already seeing AI influence consumer shopping behavior today, and how does that impact customer expectations once a purchase has been made?

Schmid: We’re already seeing platforms like ChatGPT and Claude help shoppers identify product choices, compare products, and answer questions that used to require digging through reviews or product pages. That creates higher expectations after the purchase because customers assume the recommendation was tailored to their criteria and that the product will arrive exactly as described.

When that expectation isn’t met – for example, because an item looks different in person or delivery communication falls short – trust can erode fast. The post-purchase experience becomes the moment where the promise made before checkout is either reinforced or broken. That’s where brands either reinforce the confidence created before checkout or lose it.

DJ: What are brands’ biggest considerations to ensure AI recommendations lead to a positive post-purchase experience? What kind of challenges or considerations should they prepare for?

Schmid: Brands need to look past whether AI can help close a sale and take a harder look at what happens once the order is placed. As AI plays a bigger role in how people discover products, it raises expectations for everything that follows. If the experience after checkout feels disjointed or slow, that disconnect stands out more.

A common challenge is that tracking, customer support, returns, and exchanges often live in separate systems that aren’t connected. That creates friction for both the customer and the brand. When those pieces are connected, it’s easier to deliver a smooth experience from delivery updates to resolving issues or handling returns, which ultimately makes AI-informed purchases feel more reliable and worth repeating.

DJ: Many brands are evaluating AI’s impact on traffic and conversions. Should they also be paying close attention to post-purchase metrics to gauge impact? If so, why?

Schmid: Absolutely. Traffic and conversions tell you whether AI drove them to the brand’s website to complete a purchase. They don’t tell you if AI led the customer to make the right purchase. Metrics like return and exchange rates, customer support inquiries, refund timelines and repeat purchase behavior give a much more complete picture of whether AI is improving loyalty and customer lifetime value (LTV). If AI recommendations consistently lead to lower returns volume and higher customer satisfaction, that’s a good indication the technology is delivering meaningful value. On the other hand, if conversions go up but returns, exchanges, and support requests increase as well, brands should take a closer look at the quality of those recommendations.

DJ: Looking ahead, what role do you believe the post-purchase experience will ultimately play in determining whether AI delivers meaningful value for brands and consumers?

Schmid: I think the post-purchase experience will become one of the clearest measures of whether AI is actually improving outcomes for retail brands. The experience after checkout determines whether a shopper comes back again, and that directly impacts a brand’s need to spend more on customer acquisition. AI will eventually make product discovery feel effortless. Post-purchase is where retailers prove they can deliver on the expectations their customers have. As AI becomes a bigger part of the shopping journey, brands should start to treat post-purchase as an extension of that experience, not the end of it.