Problem: Manual search rules for over 600,000 products kept marketers from doing more meaningful projects.

Solution: Caleres tested Coveo’s fully AI-powered filters against the traditional search rules requiring manual fine-tuning. Coveo’s filters, enhanced by machine learning models that helped them improve over time, resulted in a better search product discovery process and conversion rate than legacy methods.

Caleres’ 140-year legacy is able to meet the changing demands of today’s digital customer by trading in manual indexing and recommendation rules with a self-optimizing technology stack from Coveo. They launched 13 sites in 18 months with enhanced product search and discovery, filtering, and ranking capabilities. Customers wanting to Buy Online, Pick Up In Store (BOPIS) now have the assurance that the results they see are relevant to their preferences and will be in-stock at their preferred location.

St. Louis-based clothing and shoe brand, Caleres, responsible for successful brands like Famous Footwear, Dr. Scholl’s Shoes, Naturalizer, and Zodiac, has 13 distinctly branded online retail sites and over 900 brick-and-mortar store locations. With over 600,000 product SKUs to track and manage, Caleres migrated its ecommerce properties to Coveo’s AI-powered search and experience platform to ensure perfect product discovery that meets customer needs. These innovative solutions, made better by machine learning, allow Caleres to focus on storytelling and the creative upgrades that delight their target audience. Product marketing offers better returns across their web properties.

Problem: Maintaining a relevant facet library for so many products was difficult, and the same facets were shown on every page.

Solution: With Coveo’s custom pipelines, each brand site has the ability to manage its own facets on a category or product page level, which is then fine-tuned with the assistance of machine learning. Businesses can own their own site experience and let ML create dynamic recommendations based on real-time data to match user needs.

The company’s e-commerce initiatives and strategic planning positioned it as a top six retailer in searches during high-demand periods, a significant achievement for a specialty store. The implementation of personalized content and automated flows improved the overall customer experience. New customers received tailored content that introduced them to Home Depot’s offerings and engaged them in long-term interests, fostering loyalty and encouraging repeat purchases. By addressing the challenges of a fragmented market, educating consumers about e-commerce, and implementing advanced personalization strategies, Home Depot strengthened its presence in the Mexican market. The success of these initiatives showcased Home Depot’s ability to adapt to market dynamics and leverage digital transformation to drive growth.

Home Depot’s strategic focus on omnichannel personalization not only addressed key challenges but also propelled the company to new heights in the Mexican market. By offering a consolidated shopping experience, educating consumers, and personalizing customer interactions, Home Depot achieved remarkable improvements in engagement, conversion rates, and overall sales performance, solidifying its position as a leader in the home improvement retail industry.

With the volume of orders and visits to their sites, Caleres could see substantial returns on their use of machine learning to create a more functional and relevant shopping journey. The Coveo engine supports continuous product discovery through the search box, product category pages, recommendations throughout the site, and how products are ranked and sorted. Because the platform runs on AI and ML, marketing tactics update automatically to match current best practices.