A shopper lands on your store. They have intent. They have a budget. They're ready to buy. And then - they can't find the product.
Not because you don't sell it. Because they couldn't navigate to it, couldn't search for it the way they naturally think about it, or couldn't filter your catalogue to surface what they needed. So they left.
This is the product discovery problem - and it's one of the most pervasive and underdiagnosed issues in ecommerce today.
Product discovery is the full process by which a shopper moves from intent to finding the right product. It includes search, browse, filtering, and recommendations. When any of these mechanisms fails, product discovery breaks down. The shopper can't find what they need, and you lose the sale - even though you have the inventory.
Your product catalogue uses the language of your category. Your customers use the language of their lives. These are not always the same thing.
You call it a "lightweight down-filled puffer jacket." Your customer searches for "warm coat that packs small." A search engine that only matches on exact keywords in your product titles and descriptions will fail this customer at every step. The product exists. The intent exists. The discovery mechanism is the missing link.
Most ecommerce merchants organise their category trees based on how their products are stored or imported - not based on how customers think about their needs. A customer looking for "office gifts under €50" can't navigate to that outcome if your category tree separates "stationery," "tech accessories," and "desk items" into completely separate branches.
Filtering is where product discovery either accelerates or collapses. If your filters don't reflect what shoppers actually care about - if you offer colour and size but not material, occasion, or compatibility - shoppers can't narrow a large catalogue to a manageable shortlist. Poorly implemented filters that return empty result sets create the illusion of a poor catalogue when the problem is the discovery layer, not the inventory.
Modern search solutions use NLP to interpret what a shopper means, not just what they typed. "Warm coat that packs small" is matched against product attributes - thermal rating, packability, weight - even if none of those exact words appear in the product title.
Beyond keyword frequency, semantic search evaluates meaning. It understands that "trainers" and "sneakers" are the same thing, that "sofa" and "couch" refer to the same product, and that "something for a small bathroom" is a spatial constraint that should filter results accordingly.
Once a shopper's session reveals behavioural signals - what they've clicked, lingered on, filtered by - a smart search layer can re-rank results to surface products more aligned with that individual's preferences.
Quantify your product discovery problem with these metrics:
A high exit rate from search results pages, combined with a reasonable click rate, suggests the results are visible but not compelling - a ranking problem. A low click rate suggests the results themselves are irrelevant - a matching problem.
Site search is the tool shoppers use when they type a query. Product discovery is the broader concept of how shoppers find products - including search, browse, filtering, and recommendations. Good ecommerce product discovery means all of these channels work together to connect intent with inventory.
Most commonly: vocabulary mismatch, poor categorisation, inadequate filtering, or poor search ranking. The right product exists but the discovery mechanism fails to surface it.
Great product discovery starts with search that understands intent. That's exactly what BRADsearch is built to deliver.