Extracting Ecommerce Search Intent, from Head to Tail

Ecommerce search queries are more than just a means to find products. They’re behavioral signals — real-time expressions of what customers want, what matters to their purchase decision, and how close they are to buying. 

In designing search intelligence, we can’t just focus on keyword matches. Keywords can have multiple meanings that change based on the context of how the search query is constructed. And keyword-matched results can have various levels of relevance to an individual user depending on their history and behavior intent signals.

Instead, modern ecommerce search must model intent and extract the semantic meaning* of a search term for both broad “head” search terms, which may include only one or two words, and highly descriptive “long-tail” searches. And with that meaning, they must layer in real-time behavior, inventory, and personal preferences to optimize result rankings.

Let’s explore how modern machine learning techniques extract and score behavioral intent from search queries, in addition to how this semantic insight gets applied across the shopping journey through search.

Head Terms, Long-Tail, and the Intent Between

For most ecommerce sites, search queries follow a distribution curve with a short and lumpy “head” of frequently searched terms, a longer “tail” of infrequent but highly specific terms, and some that fall along the curve.

Head terms (also referred to as “short-tail”) tend to be short and high-level. They often mirror site navigation intent, such as looking for a quick path to a category (e.g., “mens jeans,” “office chairs,” “sunscreen”). Customers that use head terms are often early in their purchase journeys, in the exploratory or even casual browsing mode.

Tail terms typically contain attributes and qualifiers that suggest a shopper is closer to purchase (e.g., “Samsung 55 inch 4K smart TV under $500” or “vegan protein powder chocolate flavor”). Mid-tail terms (higher up in the distribution curve of search frequency) often contain a single attribute like brand, color or feature. Long-tail terms are typically longer in length, contain more qualifier keywords and suggest a shopper has done their homework, and they want a tight result set. They may even be looking for one specific product, searching by its full (or close-to-full) name.

The myth of the long tail

You may have heard that the long tail makes up 80% of ecommerce site searches. We have the data to bust this myth. A meta-analysis across a large set of online retailers using Constructor shows that the opposite is true: 75-90% of search queries are shorter than four words.

This old adage is a remnant of general SEO (queries typed into Google and other search engines, many of them informational versus transactional) and dates back to a time before smartphones ruled digital traffic. The majority of traffic to ecommerce sites today comes through mobile, which is one reason shoppers favor shorter searches due to the form factors of smartphones.

Shoppers that describe what they want in more detail tend to be closer to conversion, but many visitors that use short queries have just as strong purchase intent. It’s critical that your system understands what shoppers are looking for every time by unpacking intent intelligently.

Matching Merchandising to Search Intent

From a search and discovery standpoint, head and tail queries play fundamentally different roles.

Head / short-tail terms represent broad interest. They require intelligent disambiguation, personalization, and guided navigation. For example, “couch,” “cereal,” or “blankets.”

Mid-tail terms can swing either way – they may be longer keyword strings that describe product types (and should still be treated as broad intent) or, depending on the qualifying keywords, suggest the user is still open to suggestion within a scoped set of results. For example, “leather couch,” “sugar-free cereal” or “baby blankets.”

Long-tail terms reflect clear purchase intent. The user knows what they want and makes their best effort to describe it, expecting precise results.

It’s the job of an intelligent search engine to gauge what queries mean what to what users.

Handling head terms

Intelligent disambiguation is the process by which a search application interprets ambiguous or vague queries in a smart, context-aware way to surface the most relevant results (even when the query could mean multiple things).

For example:

• “apple” → could mean the fruit or the tech brand
  
  
• “boots” → could mean hiking boots, fashion boots, or rain boots
  
  
• “chair” → could mean an office chair, dining chair, or accent chair
  
  

Rather than blindly returning every possible result, intelligent disambiguation uses available contextual clues to narrow things down. These clues might include:

• User behavior (previous purchases, clicks, or filters applied)
  
  
• Popular associations (e.g., most people who search “Apple” on your site mean the tech brand)
  
  
• Catalog structure (which product types are relevant and available)
  
  
• Session history (what the shopper was doing before the query)
  
  

In short, it's the system’s ability to say, "This term could mean several things, but here’s the one that makes the most sense for this user right now."

Intelligent disambiguation enables Constructor to handle vague queries gracefully — especially for high-volume, highly-ambiguity “head” terms. 

Tackling long-tail terms

While head queries bring visibility, the tail is where intent sharpens. Longer queries provide more information to the search system. However, simply keyword matching is not the best way to serve results for long-tail queries. Again, every individual word may have multiple meanings and represent different forms of intent, and keywords together nearly always change the semantic meaning of the search.

Let’s take an example of a search for “benefit pore minimizing primer.” A keyword-oriented search may return two SKU variants of this exact product (full and travel size) along with a host of other brands’ primers across product categories, including lash primer, hair primer, and non-pore minimizing formulas. It may also return irrelevant Benefit brand products and items that minimize oil, fine lines, or frizz. Behind the scenes, this is the “or” search operator (from Boolean logic) in action: search will find and return products with “benefit” or “pore” or “minimizing” or “primer.” 

Similarly, the “and” operator, which tells search to only return matches that contain all keywords, can return too few matches or miss what the user meant by their query. For example, a search for “eczema cream for face” can miss key products like Ceramidin Skin Barrier Moisturizing Cream or Ultra Repair Face and Body Moisturizer for Skin Repair because they don’t include “eczema” specifically in their titles (if the engine is very tightly tuned). Even worse, the engine will return zero results for “affordable eczema cream for face.”

Real search results from a large online drugstore using Boolean search logic

Instead of straight keyword matching, intelligent search factors both keyword relevance with how attractive products are likely to be to the user. Here’s an example with results for “eczema cream” on a major beauty retail site using Constructor. A few top-ranking products do include “eczema” in their product names, but popular matches that include semantic matches such as “skin repair” or “barrier cream” are also included in the set. The sort order of products is determined by the individual user’s behavioral profile and intent scoring.

Real search results for “eczema cream” using intelligent search

Notice how results change for this user when the attribute qualifier “affordable” is added to the query:

Real search results for “affordable eczema cream for face” using intelligent search

Let’s break down how Constructor identifies intent from long-tail keywords such as this one, and ranks by a mix of semantic relevance and attractiveness.

To truly make sense of customer intent and return results with a mix of semantic relevance and attractiveness**, at Constructor we use several layers of understanding and focus on three key approaches: attribute extraction, semantic vector embeddings, and behavioral intent scoring. 

Attribute extraction 

The first layer of understanding comes from recognizing product attributes embedded in the query. These can include:

• Category: "boots," "laptop stand"
  
  
• Brand: "Nike," "Sony"
  
  
• Color: "black," "beige"
  
  
• Size/Dimensions: "size 10," "2L," "55 inch"
  
  
• Material: "leather," "ceramic"
  
  
• Price cues: “under $100,” “cheap,” “luxury”
  
  
• Usage context: “for travel,” “gift for dad,” “office use”
  
  

By building an attribute extractor model — often using a combination of rule-based patterns and machine learning classifiers — we can enrich the query with structured metadata, enabling better recall and relevance. 

We do this using transformers, also known as the ‘T’ in GPT models. Transformers are a type of deep learning model that can deeply analyze the query and understand the meaning behind the keywords.

But that’s just level one.

Semantic vector embeddings

Long-tail queries are challenging because many of them are unique or nearly unique. We won’t have historical click or conversion data for all of them, and the more keywords within the query, the greater the chance these keywords don’t precisely match what’s in your product data. In older systems, this would mean "no results found."

To address this, we apply semantic search powered by techniques like vector embeddings, which are numerical representations of words or phrases that let machines understand similarity in meaning, even when the exact words don’t match. Effectively, this lets us map words and phrases into a mathematical space where similar meanings are grouped closely. 

For example, semantic search allows the system to understand that:

• “small desk for kids” ≈ “child-sized study table”
  
  
• “affordable” ≈ “under $100” or “budget”
  
  
• “noise-cancelling headphones” ≈ “headphones that block background sound”

By embedding both queries and product content into a shared high-dimensional vector space, we can:

• Retrieve products that are semantically aligned with the user’s intent, even without exact keyword overlap
  
  
• Handle cold-start or zero-result scenarios more gracefully

The problem with vector search

It’s important to point out that vector search alone has limitations, particularly for ecommerce websites. 

To get more technical, traditional vector search systems embed queries and product content into a shared vector space, often using off-the-shelf models like Word2Vec, FastText, or generic BERT embeddings. 

This works fine — until it doesn’t.

Standard vector search models are trained on linguistic patterns — not product catalogs, shopper behavior, or business constraints. They fail in several ecommerce-specific ways:

• They lack catalog and attribute awareness. For example, they may not distinguish between lactose-free and regular milk, or know that “NB” refers to New Balance
  
  
• They’re unable to apply hard filters like price, dietary attributes or inventory availability
  
  
• They lack sensitivity to seasonal changes and product trends, and don’t “retrain” to adjust to summer versus winter products and styles, pre-to-post holiday, etc.
  
  
• They poorly handle multilingual contexts such as English queries on a non-English catalog
  
  
• They don’t understand product taxonomy or catalog constraints
  
  
• They can return overly generic or irrelevant matches due to misunderstanding semantic language

That’s why we built our own solution to bridge the gap between relevance and revenue.

Cognitive Embeddings Search is our proprietary vector-based recall engine specifically enhanced for ecommerce. It’s trained on real-world shopping behavior, query-product interactions, and catalog-specific structure. This lets us embed queries into a “commerce-aware semantic space” — where relationships are shaped by what actually sells.

This provides:

• Precision matching for long-tail queries: Even if a long-tail query has never been seen before, Cognitive Embeddings Search can return high-quality results based on intent similarity, not keyword overlap
  
  
• Zero-result rescue: Instead of fallback logic that returns partial matches, Constructor dynamically matches queries to adjacent products or categories based on a deep understanding of catalog metadata, product attributes and product affinities
  
  
• Commerce-aware semantic space: Our embeddings incorporate real-world shopping behavior, not just linguistic patterns. That means results aren’t just relevant — they’re likely to convert

But even this isn’t the last piece of the intent-sniffing puzzle…

Behavioral intent scoring

This is where intelligent search really shines. With behavior intent scoring, we combine our transformer model with behavioral data (what users click, scroll past, and purchase collectively and individually) and label queries based on downstream behavioral outcomes – a.k.a. which search terms lead to clicks, cart adds, checkout starts, and purchases.

By analyzing large volumes of customer behavior over time, Constructor can:

• Score queries based on likelihood to convert
• Re-rank search results based on collective shopper preferences
• Surface high-intent queries earlier in autocomplete or recommendations

When the semantic meaning of keywords within a query match your product offering, your results move from relevant to attractive, providing a far better (and higher converting) customer experience.

Merchandising for mid-tail terms

Because mid-tail terms may indicate varying degrees of intent, it’s important to get things right using all the tools: intelligent disambiguation, attribute extraction, cognitive embeddings, and behavioral intent scoring.

Again, it’s not about matching keywords specifically, but understanding what the customer means and expects to see from great search results.

The Intent Spectrum as a Design Principle

Head vs. tail isn’t just a distribution — it’s an expression of user certainty. Head queries express exploration. Tail queries (both mid and long) express decision. Site search’s job is not to treat these as problems, but to decode the behavior they represent and retrieve the best results every time.

Vector-based retrieval (with Cognitive Embeddings Search), behavioral intent scoring, and query attribute extraction all converge toward the same goal: to understand what the customer means, even when they don’t say it directly.

And when we succeed at that? We don’t just optimize for conversions, we build search systems that feel like they understand people.

For more insights on intent-based search, check out our original research: Beyond Relevance The Data-Backed Case for Attractiveness as the New Standard for Ecommerce Search Performance

* Semantic meaning refers to the ability of a search engine to understand the intent and context behind a user's search query, rather than just matching keywords.

** Attractiveness represents the likelihood that an item will be clicked on, added to cart, and/or purchased for a specific search query entered by a specific user.