Know Your E-commerce Search Query Types? 41% of E-commerce Sites Are Getting It Wrong

Introduction

It’s no secret that search functionality is the backbone of any successful e-commerce site, as it directly impacts the customer experience and conversion rates. When customers land on your site, they aren’t just browsing—they’re actively searching for products. If they can’t find what they need quickly, they’ll leave, and are unlikely to return. Here’s the kicker: 41% of e-commerce sites are still getting their search functionality wrong.

It’s not just about having a search bar on an e-commerce website. It’s about understanding the types of search queries your users are making and optimizing your e-commerce site to respond accurately to each one.

In this post, we’ll break down the common e-commerce search query types and reveal why most e-commerce sites get it wrong.

Why Search Queries Matter

Research shows that 69% of online shoppers go straight to the search box when visiting an e-commerce site. The way users search it’s revealing their intent and search patterns, and in e-commerce, search intent is the golden ticket to conversion. If you don’t understand customer search patterns and fail to deliver relevant results based on that intent, you’re losing money—plain and simple.

However, why do so many e-commerce sites struggle with search? One major issue is that many e-commerce businesses don’t fully understand the different types of search queries and how they work. Let’s explore search query types so you can begin optimizing your site’s search functionality.

In our next blog, we’ll dive deeper into customer search intent and how it influences search query types in e-commerce.

Search Query Issues Across E-commerce Sites

Breaking Down the Common E-commerce Search Query Types

According to the Baymard Institute’s UX research, there are eight essential e-commerce search query types that customers rely on to find the products they need. Unfortunately, many e-commerce sites perform poorly in supporting these search queries.

Here’s a breakdown of these common search query types and how they function:

• Exact Searches: These are searches where users know exactly what they want (e.g., “iPhone 14 Pro” or a specific SKU like “STH2000DM004”).
• Product Type Searches: Customers often search by product category (e.g., “women’s sneakers” or “laptops”).
• Feature Searches: Some customers search for products based on specific attributes (e.g., “waterproof jacket” or “Bluetooth headphones”).
• Use Case Searches: Users searching for products for a specific purpose (e.g., “birthday gift” or “travel backpack”) rely on this query type.
• Abbreviation/Symbol Searches: Customers may use shortened or symbolic terms (e.g., “26 in trolley bag” instead of “26 inch trolley bag”).
• Compatibility Searches: These are searches for products that are compatible with others (e.g., “charger for MacBook Air”).
• Symptom Searches: Some users search based on problems they want to solve (e.g., “cure for dry skin”).
• Non-Product Searches: These include queries about policies or information (e.g., “return policy”).

Each of these search types is critical for creating a seamless online shopping experience. Failing to support these queries can frustrate users and result in site abandonment.

Why 41% of E-commerce Sites Get It Wrong

Despite the clear importance of search functionality, many e-commerce sites fail to fully support these critical search types. Here are some of the key reasons why this is happening:

• Lack of Synonym Support: Customers may use different terms to search for the same product (e.g., “jacket” vs. “coat”), but many sites don’t provide relevant results for alternate terms.
• Misspellings and Typos: Without typo tolerance, even minor spelling errors can result in no search results, driving customers away.
• Irrelevant Results: Poor search algorithms often return unrelated products, even for simple product type or feature searches.
• Missing Filters: The absence of useful filters (such as size, color, or price) makes it difficult for users to refine their search results.
• Slow Search: If search results take too long to load, users will abandon your site.
• Fuzzy Search: Fuzzy search compensates for typos and misspellings, ensuring relevant results are shown even when users make small errors (e.g., “bluetooh” instead of “Bluetooth”), but many sites still don’t utilize it effectively.
• Search Relevance: A well-tuned relevance engine ensures the most appropriate results appear first, based on user queries and behavior.
• Inflectional Search: Inflectional search recognizes different grammatical forms (e.g., singular/plural) to return accurate results regardless of word variations and is often overlooked by sites.

Poor Search Functionality and Missed Revenue Opportunities

“Poor search functionality leads to 80% of users leaving e-commerce sites, with 39% abandoning them entirely when they can’t find what they need, often switching to competitors. This issue costs retailers up to $300 billion each year.”

Conclusion

Baymard’s research shows that 41% of e-commerce sites still struggle with search functionality, but those that get it right see a notable increase in both sales and customer satisfaction.

In our next blog series, we’ll provide a detailed look at each of the eight essential e-commerce search query types, discuss common usability issues observed across the industry, and explore how mastering these queries can dramatically improve your e-commerce search experience.