Patrick Morin Increases RPV by 8% With Bloomreach

Patrick Morin implemented Bloomreach as the backbone of its product discovery experience, replacing its legacy ElasticSearch setup with a modern, AI-driven platform that fixed the poor performance of its search bar, supported bilingual catalogs, and automated merchandising. 

Here’s how the solution helped: 

• Search bar with synonym handling. Bloomreach improved Patrick Morin’s search bar by applying curated and algorithmic synonym logic. This made it easier for customers to find products in Québec French, mixed French/English, and slang-based queries.
• Category navigation with merchandising controls. The team pinned flyer items, boosted seasonal products, and hid irrelevant SKUs through the Bloomreach dashboard. They also defined which facets appeared for easier filtering, giving shoppers a more intuitive way to browse.
• Autosuggest for faster intent capture. As shoppers typed, autosuggest returned products, SKUs, and categories in real time, helping customers find what they were looking for more quickly.
• Unified catalog management. Patrick Morin maintained a consistent catalog across stores and languages, so customers saw accurate product details regardless of how or where they shopped.
• Pixel-based behavioral optimization. Pixel-tracked shopper interactions, like queries, clicks, and add-to-cart events, were used to refine ranking and merchandising logic over time.
• Analytics and insights dashboard. The team monitored key KPIs such as conversions, null results, bounce rates, and average order value (AOV). The dashboard provided query-level insights and benchmarking, giving them a clear feedback loop for improvements.

How It Worked

1. Catalog ingestion. Product data was ingested into Bloomreach through the Catalog Management API and SFTP. A full feed was refreshed daily, while smaller feeds captured intraday changes, including stock levels and pricing. This ensured search and category queries reflected current availability.
2. Pixel deployment. Once the Bloomreach pixel was implemented, events (e.g., search queries, clicks, and add-to-cart actions) were logged, forming the behavioral data that informed ranking and relevance models.
3. Query interpretation. When customers entered a term in the search bar, Bloomreach parsed the input, applied synonym logic, and matched it against the indexed product attributes. Autosuggest ran in parallel, calling the index in real time to return suggested products, SKUs, and categories before the full results page loaded.
4. Merchandising rule execution. The merchandising rules configured in the Bloomreach dashboard were applied after results were generated. Such rules included weekly flyer items, seasonal products, and filtering based on stock when shoppers had selected a location.
5. Analytics monitoring. The Patrick Morin team reviewed results through the  Bloomreach “top opportunities” dashboard. Reports included all the vital metrics they had defined at the beginning of their continued product discovery efforts. Additionally, the team kept a consistent pulse on pixel health and query-level performance, using these insights to refine catalog data and merchandising rules.