• by Laurie Sullivan  @lauriesullivan, February 23, 2024

Chadwick Radaz, director at Sur La Table, says the company's small marketing team has spent less time manually merchandising search results on the page through AI-powered tools because the technology easily learns consumer patterns, even for long-tail of search terms.

“We are in the process of using AI to revamp our product pages and integrate recommendation tools,” he says. “In the back half of the year, we will have new product pages. Yes, we want consumers to build their basket and convert, but by using AI predictive analytics, we can make the pages more relevant to the customer.”

Sur La Table, a Seattle-based company that sells kitchenware, food, and outdoor products, has been using artificial intelligence (AI) for website search, category merchandising, product recommendations, and to support the high volumes of calls to its call center.

One strategy to get consumers to discover all types of products when they landed on the website through a paid channel based on predictive data shows that although AI support proved the most difficult, it generated the best results.

The Sur La Table team faced the challenge of piecing together where certain recommendations came from, but Bloomreach helped restructure the company's merchandising strategy.

The transition to an AI tool went smoothly, but involved updating Sur La Table’s product discovery and recommendation strategy and tightly integrating it with Salesforce Commerce Cloud. The tool continually learns what customers search, browse and purchase, and that data is used to serve similar recommendations to other consumers.

AI has freed up resources and time, as well as streamlined workflow to connect better with consumers, and Radaz says the company has “seen really great add-to-cart conversions.”

Cloud-based Bloomreach created Loomi, an AI and machine learning (ML) technology and it wove it through the company’s platforms. 

Through this platform, Sur La Table experienced a 7.6% rise in the average order volume (AOV) in search, and 6.6% increase in the add-to-cart (ATC) rate, people who land on a product page and add the products to the cart. The company also saw double-digital increases in conversation rate.

Once it had implemented the recommendation and pathway part of the strategy, the company counted and saw 1.4 million visits to its “Similar Items” widget and another 1.6 million visits to its “You May Also Like” widget.

Radaz, a self-proclaimed “data geek,” grew his career at Sur La Table. “Companies lose insight to data as they grow, so having the predictive data in real time should become the strategy,” he says. “Be proactive, rather than reactive. It lets us figure out where the opportunities from the customer’s perspective.”