The Hidden Problem of E-commerce: When Your Store’s Search is Hurting Sales

How Many Customers Are You Losing Due to Poor Search Functionality?

Imagine you’re looking for a book about skiing, but you can’t remember the exact title. You type "ski" into the search bar of an online bookstore, only to see results that include the word "ski" in the title—but not books that are actually about skiing. Perhaps the title contains the word "skiing," or the topic is only mentioned in the book’s description. After several frustrating attempts, you leave the site—either to buy elsewhere or to abandon your purchase altogether.

The same scenario applies to a customer searching for a "pressure washer" but using the slang term "vapka." If the store’s search engine doesn’t recognize this term and fails to show relevant products, the retailer loses a sale. However, if the search engine understands how people actually speak, the customer effortlessly finds what they need—and is more likely to return in the future.

Search as a Key Factor in Conversions

According to Algolia, customers who use search are two to three times more likely to make a purchase than those who browse categories. A study by Digital Commerce 360 confirms that e-commerce sites with advanced search features achieve an average conversion rate of 5.9% on desktop, while those with basic search functionality see only 2.8%.

The Salesforce Shopping Index, which analyzes the behavior of over 1.5 billion customers from 67 countries, highlights internal search as a key element affecting not only conversion rates but also average order value and cart abandonment rates.

In other words, a customer who uses search is already close to making a purchase. If the search engine delivers the right results, the likelihood of completing the order increases significantly.

The Most Common Search Mistakes That Drive Customers Away

Search needs to be fast, intuitive, and relevant. Users expect the system to handle typos, understand query context, and return appropriate results. When this doesn’t happen, customers hit a dead end.

The Most Critical Search Engine Mistakes:

• Low relevance of results – You search for running shoes but see sandals at the top of the list. Even if the rest of the results are relevant, this initial mismatch erodes trust.
• Lack of filtering options – You enter "blue women's jeans" and receive hundreds of results with no way to filter by size or fit. The outcome? Frustration and exit.

Full-text vs. Faceted Search: What Does Your E-shop Need?

Most e-shops rely on full-text search, which works with keywords. While fast, it can be more problematic than beneficial if not optimized properly. Without advanced features like synonym dictionaries or typo correction, it often returns irrelevant results. A modern search engine must understand synonyms, colloquial expressions, and typos—essentially, it should grasp what the customer wants even if the exact word isn’t in the product description.

To refine results and enhance user experience, e-shops employ faceted search filtering. This allows customers to filter products by attributes such as size, material, color, brand, and more. A well-designed faceted filter is intuitive, dynamically updates based on selections, and provides relevant filtering options. Users should be able to easily remove filters and clearly see which ones are active.

While faceted filtering is now standard for browsing product catalogs, many e-shops still underutilize it for refining search results. However, combining properly optimized full-text search with faceted filtering is the ideal solution. It enables visitors to find what they need quickly while also discovering new products. This approach is especially critical for stores with extensive product ranges, such as clothing retailers with numerous sizes and variations.

The search engine must also be fast. Customers are impatient, and competitors are just a few clicks away. Amazon found that every 100-millisecond delay in page load time decreases conversions by 1%. The same principle applies to search—if it’s slow, inaccurate, or hard to use, customers won’t wait; they’ll leave.

How to Optimize Search and Improve Conversions

A search engine should work with natural language, understand query intent, and adapt to customer behavior. An intelligent autocomplete feature is essential—it should guide users to what they’re looking for as early as possible, ideally leveraging their search history.

Simply setting up search isn’t enough. It’s a living system that needs continuous optimization based on data and user behavior. If customers frequently search for products but don’t find them, it may indicate missing keywords or a system that fails to interpret queries correctly.

Key metrics to track for search performance optimization:

• Search abandonment rate – The percentage of users who search but don’t make a purchase. This metric reveals how effective your search function is.
• Click-through rate on search results – The percentage of users who click on the suggested products.
• Search conversion rate – The percentage of users who complete a purchase after using search. This shows how well your search engine supports sales.

Data insights help identify weak points and refine search functionality to maximize effectiveness.

Search alone isn’t a silver bullet—other factors like design, product descriptions, microcopy, and overall site usability also play a role in conversions.

Search as a Growth Tool

Internal search isn’t just a technical feature—it’s a sales tool. Think of it as an in-store salesperson who understands what customers want and leads them to the right shelf. Poorly configured search leads to lost sales, frustrated customers, and lower revenue. But when optimized, it boosts conversions, average order value, and overall customer satisfaction.

Want to know how well your search engine performs and where there’s room for improvement? Check out our smart search solution, Search Ready.