National retailer Sur La Table attracts customers to brick-and-mortar locations with cooking classes and a wide selection of premium cookware to purchase. Now the company’s customer experience is also optimized online with a new AI tool that makes it easier to discover products and identify e-commerce opportunities to increase sales.
In 2023, Sur La Table implemented Bloomreach’s Discovery tool, which uses AI to improve site search, organize product pages, and segment customers. It’s designed for businesses, but can also be managed by small teams within those businesses, making Discovery feasible for Sur La Table.
“We wanted to build around growth with a very small team and becoming data-centric,” said Chadwick Radaz, director of Sur La Table. “We had data from all touchpoints, and now we’re moving towards a lean, data-centric approach that takes AI technology and makes it truly scalable and efficient. »
The company has two on-site merchandisers, as well as a data analytics team, all of whom use Discovery insights to improve the customer experience on the Sur La Table website. The retailer’s e-commerce platform offers 7,000 products to choose from and present to customers.
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Use predictive analytics to improve site search
The Discovery integration is managed by the company’s merchandising team. It is their job to show customers the products they are likely to buy, and AI predictive analysis helps them with this task.
“Search queries were a big opportunity for us,” Radaz said. “Merchants can do a great job generating search results for the top 50, but this long tail was really a great opportunity. »
He added: “Integration gives them the tools to work really smart. They really rely on predictive analytics to make the majority of merchandising decisions, but the tool also identifies great opportunities to help improve overall product visibility.
Prior to implementation, the team used a tool on its web platform to identify top product searches on the website. This did not identify the least popular products that customers or customer segments were looking to purchase.
“(The Bloomreach tool) ranks the most important opportunities and lets you know that these are the best actions you can take to really drive incrementality,” Radaz said. “Then you go back and see if that’s actually the case. So it’s about using predictive analytics, but then quantifying them as actions are taken.
Category landing pages and product recommendations
The new tool allowed the team to expand into category landing pages and product recommendations, both ongoing projects.
“We’ve seen great results, so we’ve incorporated product recommendations, which are on our product pages, suggesting similar items (customers) might also like,” Radaz said.
He added: “The learning data we get for surfaced opportunities are real opportunities for us to work with marketers, especially when we see new trending search queries. Are we in this assortment (of products) or can we be in this assortment? So it’s really necessary to take a step back and create new campaign ideas for these new audiences that we identify based on the trends we observe.
Scalable roles for marketers
The AI tool generates a high volume of data, allowing it to find many other actions that can improve the customer experience. Before adopting it, the team simply wasn’t aware of many of these trending products. They can now respond to customer interests by creating new product pages and recommendations.
“My goal for the site merchandising team is really to drive the add-to-cart (metric), and that can come from the right traffic and the right assortment,” Radaz said. “And that can actually free them up from the time they previously spent sorting manually, to where they can now organize content where they know customers are dropping off in their journey.”
Organizationally, merchandisers are the gatekeepers of the data generated by Discovery. However, the data is also distributed to marketing and analytics colleagues, helping them make decisions about campaigns and broader initiatives. Data analysis was previously trapped in customer experience decisions made by merchandisers. Now they can focus on broader metrics like customer lifetime value.
In addition to add to cart, the other KPI tracked by Radaz is revenue per visitor.
“We know it works because we are constantly pushing ourselves, as the machine continually learns and refines what our customers are doing,” Radaz said.
The company recently concluded its first holiday season with this solution and is about to celebrate the anniversary of the AI implementation. Year over year, the marketing team is seeing a 4% increase in add-to-carts for customers clicking through to product category pages.
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