July 7, 2025
The Cost of Zero Results
In eCommerce, on-site search is the fastest path from intent to conversion. When customers search, they are declaring what they want in their own words. But what happens when that journey ends in a dead end?
The dreaded “No Results Found” page isn’t just a minor UX flaw. It’s a silent killer of revenue and a symptom of deeper inefficiencies in product discovery. Yet many eCommerce platforms still treat it as an edge case. It’s not. Industry studies suggest that up to 30% of site searches return zero results, and each failed search is a missed opportunity to convert high-intent traffic into sales.
Let’s break down why zero results are so damaging – and how Nibelung.ai eliminates them altogether.
Users who engage with search typically convert 2–3x more than non-searchers. A zero-results page interrupts this high-intent journey. According to Baymard Institute, no-results pages have bounce rates exceeding 80%. Users leave not because they’re not interested – but because your search engine failed to understand their language.
Many zero results are not because the product doesn’t exist, but because of a mismatch between user language and product attributes. For example:
Traditional keyword search engines can’t handle this semantic variation. They rely on brittle rules and exact string matches, failing to adapt to how customers actually talk.
Zero-results pages often expose poor data hygiene: outdated SKUs, inconsistent tagging, missing synonyms, or siloed product categories. Worse, they usually lack a feedback mechanism. That means your system isn’t learning from user behavior or correcting for its failures over time.
Many teams try to patch the problem manually:
This approach is labor-intensive and reactive. It doesn’t scale with a growing catalog or changing user behavior. Merchandising teams spend hours firefighting individual cases instead of improving overall discovery.
At Nibelung.ai, we designed our semantic search engine to eliminate zero-result dead ends entirely – with no manual intervention required.
Our AI agents don’t rely on keywords alone. They interpret the meaning of a query and match it against a dynamically enriched understanding of your product catalog. If a user searches for “portable grill for balcony”, Nibelung understands the implicit intent: compact, smokeless, safe for small spaces – and finds the closest match, even if that exact phrase doesn’t exist in your catalog.
When a query risks returning zero results, our system intervenes in real-time:
All of this happens invisibly to the user, preserving their confidence in the site while steering them toward relevant products.
Nibelung’s self-optimizing architecture means it learns from:
No need for your team to maintain synonym lists or manually configure rules. The system improves itself over time, ensuring better results with less overhead.
Retailers that fix their zero-results problem often see double-digit improvements in conversion rates. In one case, a B2C marketplace using semantic search saw:
This isn’t just about UX. It’s about treating on-site search as a core profit driver – because that’s exactly what it is.
Zero results aren’t just a technical issue. They’re a reflection of how well (or poorly) your site understands your customers. In an era where user expectations are set by Google and Amazon, even one dead-end query can drive shoppers away.
If your search engine is returning “no results” for products you actually sell – or for high-value queries – it’s time to upgrade.
Nibelung.ai eliminates zero results automatically, using AI agents and semantic understanding to guide every customer to a relevant outcome.
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