A zero-results page appears when an ecommerce search engine fails to match the user’s search query with any products. This situation is more than just a small inconvenience—for users and businesses, it can lead to frustration and missed opportunities.

For users, a zero-results page means hitting a dead end. They understand that the store does not have the product they are seeking.

For businesses, it signals a lost opportunity to sell, upsell, or recommend relevant products—ultimately affecting revenue. Poor search experiences can lead to significant customer abandonment and loss of sales.

Improving search relevance using optimized algorithms could enhance customer satisfaction and business performance [1].

So, what are zero-results pages, and how can you minimize their occurrence?

What Are Zero-results Pages?

A zero-results page is a search result page that shows no matching products or content for a user’s query. When a search engine cannot find what the user is searching for, they end up on this page.

While it’s often considered standard practice to guide users to these pages when products are not found, businesses should aim to avoid them as much as possible.

Zero-results pages can be a dead end in the customer’s journey, which negatively impacts user experience and sales.

The Impact of Zero-results Pages on Ecommerce Businesses

Zero-results pages can harm ecommerce businesses in several ways:

  1. Loss of Potential Sales and Customers: If users do not find what they’re searching for, they are more likely to abandon your site and visit a competitor. Poor usability and high bounce rates significantly impact online businesses, with many customers leaving websites that fail to meet their search expectations [2].

  2. Negative Impact on SEO: Zero-results pages can lead to high bounce rates, which can signal to search engines that your ecommerce site is not providing relevant content. High bounce rates can harm your SEO rankings, making it harder for your website to attract organic traffic [3].

  3. Poor User Experience: Repeatedly landing on zero-results pages frustrates users, leading to decreased trust in your site and reduced customer loyalty. Improving search relevance using AI-powered tools can increase such metrics as Gross Merchandise Volume (GMV) by up to 0.26% [4].

Why Do Zero-Results Pages Occur?

Zero-results pages can appear for several reasons. Luckily, many of these are avoidable:

  1. User Errors: Users may misspell words, use different terms (e.g., jargon), or enter overly specific queries, which may not match your site’s search parameters.

  2. Insufficient Content: If your ecommerce store lacks detailed product descriptions or metadata, search engines may struggle to match user queries with relevant products. Including rich, well-optimized content has been shown to increase search result accuracy and customer satisfaction [5].

  3. Outdated or Poor Search Algorithms: Many ecommerce search engines fail to understand multi-language queries or complex user intents. Using natural language processing (NLP) can improve query relevance and boost search satisfaction [4].

  4. Technical Issues: Downtime, server errors, and network issues can all contribute to an unexpected zero-results page.

    Strategies to Minimize Zero-results Pages

Several strategies can help reduce the occurrence of zero-results pages, improving user experience and overall sales performance:

  1. Incorporate Synonyms and Related Terms: Ensure that your search engine recognizes synonyms and related phrases, allowing users to find results even if they use different wording. Synonyms can significantly improve search relevance and enhance customer satisfaction by offering more accurate product matching [6].

  2. Auto-complete and Suggestions: Use auto-complete and suggestion features to guide users to relevant products, even if their initial search query does not match exactly. This feature can reduce the occurrence of zero-results pages and improve overall customer experience. Auto-complete features can also enhance query relevance and business metrics like GMV [4].

  3. Optimize Product Descriptions: Make sure your product descriptions are accurate, detailed, and optimized with relevant keywords. This helps both users and search engines better understand your product offerings. Well-optimized content leads to improved product visibility and better customer engagement [7].

  4. Natural Language Processing (NLP): Implementing NLP algorithms enables your search engine to better understand user intent and deliver more accurate results. A study showed that incorporating NLP into search algorithms significantly improved search relevance and business performance metrics [8].

  5. Product Recommendations: Offer personalized product recommendations with such e-commerce search functionality as SellSence based on user search history and behavior. Product recommendation systems powered by natural language processing and deep learning have been shown to effectively guide users toward relevant products, thereby reducing the chance of zero-results pages [9].

By monitoring search analytics with Search Intelligence, you can identify trends and patterns in user queries that frequently lead to zero-results pages.

For instance, you might notice certain keywords that commonly return no results, which you can address by adding synonyms or new products.

This proactive approach will help you meet customer demand more effectively and enhance the overall search experience.

LupaSearch: Your Solution to Reducing Zero-results Pages

Looking for an advanced ecommerce search solution to minimize zero-results pages?

LupaSearch is an AI-powered search engine optimized for ecommerce. With cutting-edge NLP algorithms, multi-language support, and synonym integration, LupaSearch can help improve search relevance and boost your conversion rates.

Schedule a demo with LupaSearch product consultants today and discover how we can optimize your ecommerce search engine for better results and more satisfied customers.

References

  1. Yin, W., & Xu, B. (2021). Effect of online shopping experience on customer loyalty in apparel business-to-consumer ecommerce. Textile Research Journal, 91, 2882 - 2895. https://doi.org/10.1177/00405175211016559.

  2. Tsagkias, M., King, T., Kallumadi, S., Murdock, V., & Rijke, M. (2020). Challenges and research opportunities in eCommerce search and recommendations. ACM SIGIR Forum, 54, 1 - 23. https://doi.org/10.1145/3451964.3451966.

  3. Roy, G., & Sharma, S. (2021). Measuring the role of factors on website effectiveness using vector autoregressive model. Journal of Retailing and Consumer Services, 62, 102656. https://doi.org/10.1016/J.JRETCONSER.2021.102656.

  4. Singh, S., Farfade, S., & Comar, P. (2023). Multi-Objective Ranking to Boost Navigational Suggestions in eCommerce AutoComplete. Companion Proceedings of the ACM Web Conference 2023. https://doi.org/10.1145/3543873.3584649.

  5. Golebiowski, J., Merra, F., Abedjan, Z., & Biessmann, F. (2022). Search Filter Ranking with Language-Aware Label Embeddings. Companion Proceedings of the Web Conference 2022. https://doi.org/10.1145/3487553.3524218.

  6. Wu, F., Liu, Y., Gazo, R., Bedrich, B., & Qu, X. (2022). Some Practice for Improving the Search Results of E-commerce. ArXiv, abs/2208.00108. https://doi.org/10.48550/arXiv.2208.00108.

  7. Zhou, J., Liu, B., Hong, J., Lee, K., & Wen, M. (2023). Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce. ArXiv, abs/2310.18357. https://doi.org/10.48550/arXiv.2310.18357.

  8. Patidar, R., & Patel, S. (2022). Design & Implementation of Product Recommendation Solution using Sentiment Analysis. 2022 International Conference on Edge Computing and Applications (ICECAA), 233-239. https://doi.org/10.1109/ICECAA55415.2022.9936480.

  9. Marchand, A., & Marx, P. (2020). Automated Product Recommendations with Preference-Based Explanations. Journal of Retailing, 96, 328-343. https://doi.org/10.1016/j.jretai.2020.01.001.