Wolseley Uses Bloomreach To Elevate B2B Product Search for Higher RPV

With the end goal of modernizing its digital customer experience and delivering more relevant product discovery across its different trade audiences, Wolseley partnered with Bloomreach to rethink how search and merchandising were handled across its website. The company wanted a solution that could understand the highly specialized needs of all its various audience segments, while eliminating the heavy manual workload previously required to keep search results accurate with a build-it-yourself search engine like Solr.

Using Bloomreach, Wolseley aimed to deliver a smarter, more personalized search experience powered by Loomi AI and relevance by segment. This enabled the company to interpret technical search terms, tailor results by industry, and scale merchandising without the limitations of its legacy rule-based setup. 

Leveraging AWS services, such as Amazon Elastic Compute Cloud (Amazon EC2), Amazon Simple Storage Service (Amazon S3), and Amazon OpenSearch, Wolseley gained the performance, reliability, and scalability needed to support its vast digital catalog and evolving customer expectations.

Here’s more insight into how Bloomreach helped:

• Personalized search experiences through relevance by segment. By leveraging Bloomreach’s relevance by segment feature, Wolseley could finally differentiate search results by audience. A customer searching for “pumps” no longer saw a generic product grid — plumbers and HVAC professionals received entirely different, industry-specific recommendations aligned to their needs. This segment-forward experience made search more intuitive and significantly improved engagement and conversions.
• AI-powered ranking and relevance scoring. Loomi AI’s relevance score algorithm enabled Wolseley to move away from hundreds of manually created Solr rules. Instead, products were ranked based on how well they matched each query, with Loomi AI identifying the correct product type and attributes behind terms like “shower tray” or “Worcester 4000.” This increased clarity around queries allowed Wolseley to scale search quality across its 300,000-product digital catalog without the need for constant intervention.
• Improved tolerance for misspellings and overly specific queries. B2B search often involves complex model numbers, brand variations, and technical descriptors. Loomi AI recognized when queries were too narrow or contained spelling errors and relaxed the parameters accordingly. This new AI-driven approach reduced null results, kept users engaged with product discovery longer, and improved search success rates across all segments.
• Greater transparency and dramatically reduced manual maintenance. Bloomreach helped Wolseley understand why certain results appeared — something that was nearly impossible with its old rule-based system. As the AI now manages relevance and ranking, the merchandising team no longer spends hours troubleshooting inconsistencies or building rules. Now, they can focus on strategic initiatives, catalog expansion, and delivering a more customer-centric experience.

How It Worked

1. Phased implementation of customer segments. Wolseley collaborated with Bloomreach to define core trade segments and map search behavior for plumbers, HVAC engineers, pipe-fitters, and building services professionals. These segments formed the backbone of Wolseley’s new personalized search experience.
2. Deployment of AI-driven relevance through Loomi AI. By transitioning from Solr’s rule-based approach to Loomi AI’s algorithmic recall and ranking capabilities, Wolseley replaced hundreds of rules with automated, behavior-driven optimization. Search results improved immediately, with technical product terms consistently returning accurate matches.
3. Rebuilding search logic with dynamic relevance scoring. Loomi AI analyzed query patterns and assigned relevance scores based on product type, keyword match, and application. This separation of attributes from products allowed Wolseley to show the most appropriate results without needing manual configurations.
4. Ongoing measurement and data-driven refinement. As segmented search went live, Wolseley monitored traffic distribution, product views, add-to-carts, and conversion performance within each segment. These insights helped the team quickly validate improvements and identify opportunities for further optimization.
5. Expansion into additional AI-powered product discovery tools. After search results dramatically improved, Wolseley went live with Bloomreach recommendations to extend personalization beyond the search bar and build a more comprehensive product discovery experience.