Businesses that rely solely on the default ecommerce search often miss out on a significant portion of their sales potential.

43% of online shoppers head directly to the search bar upon landing on a retail website. If your ecommerce platform doesn’t provide a well-performing search function—one that delivers the exact products users are looking for—your business could be losing up to half of its potential sales [1].

The Role of Ecommerce Search in Revenue Generation

Search functionality plays a crucial role in customer experience and directly affects revenue.

39% of all purchases are influenced by relevant search results, and customers using search generate 2.6 times more revenue than those who do not use the search feature [2].

Many ecommerce sites fail to capture this potential because they rely on default search engines, which often lack advanced features such as understanding complex queries.

For instance, when a shopper searches for a Samsung fridge with an ice maker, the search function should return precisely that product, rather than a mix of unrelated results: a Samsung phone, a random fridge, and an ice maker.

If the search results are irrelevant, customers are likely to leave, resulting in lost sales opportunities [3].

Evaluating the Efficiency of Your Ecommerce Search Solution

There are several factors you should consider to assess whether your ecommerce search is working effectively:

  1. Bounce Rate: A high bounce rate can signal that users aren’t finding the products they’re searching for. When ecommerce sites deliver irrelevant results, customers tend to exit quickly. Improving site search relevance can reduce bounce rates by up to 15% [4].

  2. Zero-Results Pages: If your search engine frequently returns “no results found” pages, it’s a clear sign that the tool isn’t capable of handling long-tail or highly specific queries. Reducing these instances can increase conversions by up to 30% [[5](#references].

  3. Purchases from Search: Tracking how many sales are directly generated through search is one of the best indicators of search performance. Advanced search tools can offer metrics that highlight trending products and high-performing queries [2].

Why Default Search Engines Fall Short

Many default ecommerce search engines lack the personalization and advanced features required to stay competitive.

As online shopping becomes more sophisticated, relying on a default search engine that doesn’t learn from user behavior or offer customization options can hinder business performance.

Businesses with advanced ecommerce search functions can see up to 50% higher conversion rates compared to those relying on default search engines [2].

Advanced Ecommerce Search: The Solution

To maximize the potential of your online store, adopting an AI-driven search engine like LupaSearch is critical.

LupaSearch processes complex queries with precision and continuously learns from user behavior, delivering highly relevant results. By offering better search results, businesses can increase both conversions and revenue.

LupaSearch integrates seamlessly with popular ecommerce platforms, and its customizable features offer businesses a way to outperform competitors relying on default search tools.

Request a free product demo today, and let’s grow your business.

References

  1. Singh, G., Parikh, N., & Sundaresan, N. (2011). User behavior in zero-recall ecommerce queries. Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. https://doi.org/10.1145/2009916.2009930.

  2. Cezar, A., & Öğüt, H. (2016). Analyzing conversion rates in online hotel booking: The role of customer reviews, recommendations and rank order in search listings. International Journal of Contemporary Hospitality Management, 28, 286-304. https://doi.org/10.1108/IJCHM-05-2014-0249.

  3. Petz, G., & Greiner, A. (2014). First in Search - How to Optimize Search Results in E-Commerce Web Shops. , 566-574. https://doi.org/10.1007/978-3-319-07293-7_55.

  4. Jansen, B., & Resnick, M. (2006). An examination of searcher’s perceptions of nonsponsored and sponsored links during ecommerce Web searching. Journal of the Association for Information Science and Technology. https://doi.org/10.1002/ASI.V57:14.

  5. Singh, G., Parikh, N., & Sundaresan, N. (2011). User behavior in zero-recall ecommerce queries. Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. https://doi.org/10.1145/2009916.2009930.