43% of online shoppers head straight for the search bar upon visiting a website, with site search users being 2-3 times more likely to convert than those who do not use search.
One strategy to enhance the accuracy of your ecommerce search engine and your business success is through semantic search.
What is it, how does it work, and why does it matter for your business’s success?
What is semantic search, and how does it work?
Semantic search, different from lexical search, considers the underlying meaning behind a search query. Lexical search, on the other hand, looks for literal matches of the query words without understanding the overall meaning of the query.
In ecommerce search, this technology uses natural language processing (NLP) and machine learning to understand the meaning and context of search queries, providing more accurate and relevant results.
In ecommerce, semantic search plays a critical role in providing user-relevant product suggestions. For instance, if a user searches for “waterproof hiking shoes,” your search engine should offer products that perfectly match the search query.
However, users might also search for “robust footwear” or “military boots.” In this case, your search engine might not find those shoes (even though they are very similar).
Here, the semantic search comes in handy. It understands the similarity between these terms and takes them as synonyms for the same group of products.
Why Is Semantic Search Crucial for Business Success?
The importance of semantic search extends beyond improved search accuracy. It is a pivotal component for enhancing user experience, driving targeted traffic, and facilitating quicker website improvements:
1. Enhanced User Experience:
Semantic search significantly boosts the user experience by providing accurate and relevant search outcomes. It leads to higher customer satisfaction, loyalty, and improved conversion rates.
A comprehensive survey by Bast, Buchhold, and Haussmann (2016) on semantic search highlights its potential to understand user queries more effectively, suggesting a direct link to improved user engagement and satisfaction [1].
2. Increased Targeted Traffic:
The efficacy of on-site semantic search has a strong correlation with better SEO outcomes. By improving site structure and making it more accessible for search engine crawling and indexing, semantic search contributes to higher site visibility, increased traffic, and, consequently, sales.
Research by Formica et al. (2013) underscores the effectiveness of semantic matching in enhancing the relevance and targeting of web traffic, which can lead to improved conversion rates and ROI on marketing efforts [2].
3. Faster Website Improvements:
Semantic search simplifies the process of making website improvements by automating tasks such as synonym listing, typo corrections, and related product identification.
AI-driven site search technologies, like LupaSearch, handle these improvements, allowing businesses to focus on growth while the search engine optimizes the site.
The role of semantic technologies in streamlining website enhancement efforts is supported by Thorleuchter and Van Poel’s (2012) analysis, which discusses the predictive power of semantic analysis for ecommerce success [3].
Semantic search is a cornerstone for modern ecommerce success, offering improved user experience, increased targeted traffic, and streamlined website enhancements.
Semantic search not only meets the immediate needs of users but also drives long-term business growth and success.
Adopting semantic search is not just an option but a necessity for businesses aiming to thrive in the competitive digital marketplace.
If you do not use a semantic search on your website, it is time to start. Contact LupaSearch product consultants, receive a demo, and start noticing promising results for your ecommerce business.
References
Bast, H., Buchhold, B., & Haussmann, E. (2016). Semantic Search on Text and Knowledge Bases. Found. Trends Inf. Retr., 10, 119-271. doi: 10.1561/1500000032. https://www.nowpublishers.com/article/Details/INR-032
Formica, A., Missikoff, M., Pourabbas, E., & Taglino, F. (2013). Semantic search for matching user requests with profiled enterprises. Comput. Ind., 64, 191-202. doi: 10.1016/j.compind.2012.09.007. https://www.sciencedirect.com/science/article/abs/pii/S0166361512001443?via%3Dihub
Thorleuchter, D., & Poel, D. (2012). Predicting e-commerce company success by mining the text of its publicly-accessible website. Expert Syst. Appl., 39, 13026-13034. doi: 10.1016/j.eswa.2012.05.096. https://www.sciencedirect.com/science/article/abs/pii/S0957417412008123?via%3Dihub