Algolia AI Search improves Plieger Groep's B2B e-commerce search, dropping average click position from over 16 to 5.5
Plieger's expert B2B customers — professionals who often knew exactly what they needed by item number or SKU — still could not find products due to poor filtering and search relevance. Both customers and employees complained, and employees were searching competitor websites to locate items before returning to buy from Plieger. The company also needed a solution that did not require a scarce and expensive dedicated developer to manage.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Professional customer initiates search
Expert B2B customers search for products, often knowing the exact item number or SKU, on Plieger's web store.
Over a 40-week period, Plieger's average click position dropped from more than 16 to close to 5.5, with ACP falling from 12 to that level in just 15 weeks after adding autocomplete and search suggestions. The team became data-driven using Algolia search analytics instead of working from complaints alone.
What failed first
Plieger's previous Solr-based search solution lacked the strong filtering capabilities essential for professional customers to find exact products by specification.
Results
Time saveddrop from more than 16 to close to 5.5
VolumeACP drop from 12 to its current value over only 15 weeks