A recent survey of 900 online shoppers in the US and UK uncovered a significant gap between what shoppers expect when they search for a product on a website – and what they’re currently experiencing. Four in 10 shoppers graded product discovery experience with a C or less and called for more tailored search results. 

Kevin Laymoun CCO at e-commerce search and delivery platform Constructor IO, has advice on how to bridge the gap between expectation and experience. He talked with Inside Retail’s Amie Larter for an episode of the podcast Retail Untangled, recorded on the sidelines of last month’s Shoptalk Fall in Chicago. 

Laymoun says when consumers go online to shop, they are looking for an experience similar to what they would get in-store. There, you might be met by a greeter who takes you to the aisle with the product you’re looking for and then helpfully recommends a few other products. “That is the dream shopping experience you rarely get online,” says Laymoun.

“There is such a huge gulf between the experiences that you’re used to today,” he contends. “Even on Amazon, which has invested heavily in experiences, the customer experience is far from the one I just described.”

Laymoun urges retailers to invest in technology that gets shoppers closer to that experience. While acknowledging that building a better CX might require significant investment, he argues that retailers must consider the impact of lost sales from not getting the experience right. 

“The product discovery equation – or should I say, the e-commerce equation – is pretty simple. It’s visitors to the site times conversion rate times average order value, which equals revenue. There are things like margins and return rates – all of that good stuff – but at the end of the day, product discovery can improve that conversion rate and the average cart value. 

“You do that by having better customer experiences. And you do that by using user clickstream, letting customers vote for the experience they want to see, not just in aggregate, of course, but also within the session for them, personally.”

Satisfying customer expectations in online customer experience

A lot of the emerging AI-driven machine learning that is coming to the fore in and outside of e-commerce is making a huge difference in precision and context, which Laymoun considers very important. 

“There are two things. How do you promote using that user clickstream within your technology, whether it’s something that you already have or going out and finding technology that you can optimise for that clickstream? And then, how do you marry it with awesome merchandising?”

He says many brands rely on manual product curation via their merchandising team, which uses some form of back-end merchandising dashboard. If they use some machine learning, it’s an either-or proposition: Either the machine learning happens, or the merchandiser is merchandising that page.

The key, he explains, is for e-commerce companies to marry both. “Oftentimes, you see your shopper once a month if you are lucky. Sometimes, once a year or a couple of times a year. So, how can you do more with less? It’s about using that data … and leveraging that clickstream alongside your merchandising. Know how to get much more context-aware experiences that drive that precision.

“The cool thing about product discovery is that it is client-side and customer-facing. We’re all shoppers. That’s the fun thing about what I do: You work on a product that you, your family, and your friends use.”

Three retailers that stand out for excellent CX

Laymoun singles out three different US retailers as examples of delivering a successful product discovery and customer experience, with beauty retailer Sephora the hallmark among them. 

“Sephora’s user experience is fantastic. It’s personalised. It’s context-aware. It changes. It’s dynamic to the context. You love to see that.” 

Another is Kmart Australia, where, unlike the US, Kmart continues to trade successfully. “Kmart Australia is innovative in chatbots and how you engage with the brand in various ways,” he says. The retailer understands the algorithm, recognises a consistent shopper and understands the changing context of shopping experiences.” 

In the Midwest, grocer Meijer is his third example: “Meijer does a fantastic job across its site in replenishment and understanding what products should be shown to a customer based on their previous purchase history. In grocery, previous purchase history is really important in [predicting] future purchasing intent, so they do a great job of that.

“Those are the top three that are easy to go and find and check out and reference.”

Laymoun believes more is to come regarding engagement in driving revenue from AI-powered shopping assistance. Companies that invest in custom experiences will ultimately boost engagement and sales. 

“Learning from what your users click on, add to cart, and purchase drives machine learning.” 

• Listen to the podcast to hear Laymoun explain how technological advancements will change search and discovery during the next few years and the importance of data in driving effective product searching.