Why Zero-Result Searches Are Killing Your Conversion Rate and What XGEN Does About It

Let's start with your big problem: someone looked for something and they got the dreaded "0 results."

Here are four of the most common (and easily solvable) reasons eCommerce sites return zero results:

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Search That Understands the Shopper

Most eCommerce search engines rely on rigid keyword matching that takes years of experience and hours to input. If a customer types “party heels” but your product is listed as “evening stilettos,” the result is a blank page, unless your team manually configures synonyms or redirects.

XGEN eliminates this problem with semantic search built on machine learning. Instead of focusing on the literal words in a query, our system interprets intent. It learns from real-time behavior, product relationships, brand-specific terminology, and customer feedback to surface relevant results (even when the wording is off).

The result: No more zero-result queries, faster product discovery, and happier customers.

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Leaving the Hellscape of Synonym Spreadsheets & Redirect Rules

For many merchandising teams, low-performing queries turn into an endless cycle of patchwork fixes: creating synonym libraries, redirecting niche terms to broad categories, and guessing what customers “really meant.”

With XGEN, that work is done for you.

Our system adapts automatically, learning from how customers search, browse, and convert. With something called It identifies patterns in query failures and proactively resolves them by expanding the relevance space without overfitting or introducing noise. That means more automation, less maintenance, and more time spent on creative merchandising instead of backend triage.

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We Build Upon Your Brand Vocabulary

LLMs, even sophisticated ones, can struggle with brand-specific language because it often includes unique product names, collection titles, or internally used terms that aren’t part of general language training data. One of the limitations of generic AI models is their struggle with brand-specific language.

For example, if a customer searches for “The Cloud” on a footwear brand’s website like On Running, a general-purpose LLM might return raincoats or weather-related items, missing the fact that “Cloud” is a flagship sneaker line. XGEN’s AI is trained directly on your catalog and real customer interactions, so it understands your brand’s unique terms and context. That means fewer false positives, and more accurate, revenue-driving results.

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How Exactly Do We Do This?

1. While many modern platforms use embedding and vectorization to interpret search queries, not all systems are created equal. The difference lies in the semantic space; how meaning is captured and represented. At XGEN, our models are trained specifically on commerce data (Fashion, Jewelry, Electronics and more), meaning they don’t just understand language, they understand shopping behavior, product relationships, and brand-specific intent.
2. We prompt users to search like they think. The era of "caveman" speak in search bars across the internet is coming to an end. Customers expect more intuitive experiences that resemble the GPTs and Geminis of the world, or they’ll bounce. Every friction point, from a typo to a mismatched term, is a lost sale.

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Let Your AI Do The Hard Work

Whether you’re launching a new collection, entering a new market, or just trying to reduce bounce rates, XGEN helps your product discovery engine keep up. Intelligent, multilingual, brand-aware, and fully customizable. This is what search should have been all along.

At XGEN, we believe that product discovery should be intelligent, flexible, and forgiving. That’s why we built a search platform that doesn’t just match keywords; it understands meaning.