{"id":10172,"date":"2025-04-10T10:56:48","date_gmt":"2025-04-10T08:56:48","guid":{"rendered":"https:\/\/www.fact-finder.com\/blog\/?p=10172"},"modified":"2025-08-12T09:45:27","modified_gmt":"2025-08-12T07:45:27","slug":"vector-search","status":"publish","type":"post","link":"\/blog\/vector-search\/","title":{"rendered":"Vector search in eCommerce: when semantic search increases conversion"},"content":{"rendered":"\n<p><em>41% of online shops have problems with their search function. Is vector search the key to higher relevance and fewer drop-offs? Here&#8217;s a crash course from an eCommerce perspective.<\/em><\/p>\n\n\n\n<!--more-->\n<!--noteaser-->\n\n\n\n<p><em>41% of online shops have problems with their search function.<\/em><sup data-fn=\"66abd1be-6c3c-41a9-8717-4c24d470b94d\" class=\"fn\"><a id=\"66abd1be-6c3c-41a9-8717-4c24d470b94d-link\" href=\"#66abd1be-6c3c-41a9-8717-4c24d470b94d\">1<\/a><\/sup><em> Is vector search the key to higher relevance and fewer drop-offs? Here&#8217;s a crash course from an eCommerce perspective.<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Definition: what is vector search?<\/h2>\n\n\n\n<p>Vector search uses AI to translate complex data, like products, texts and images, into numbers (vectors). It stores this data in a vector database so that when someone searches with an image or another type of complex data, it can quickly find and show similar content based on mathematical distances.<\/p>\n\n\n\n<p>In eCommerce, this technology is used to maximize the relevance of product listings for search results, category pages, recommendations, etc. AI models, like machine learning and natural language processing (NLP), understand the semantic connection between products and search queries, allowing them to deliver relevant results even when there&#8217;s no exact match. So, your customers will still see sneakers, whether they search for &#8220;kicks&#8221; or &#8220;trainers&#8221;.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"530\" src=\"https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/04\/vector_search_in_ecommerce_nearest_neighbor-1024x530.png\" alt=\"a simplistic visual representation of &quot;nearest neighbor search&quot;, part of the process of vector search in ecommerce, showing results for the  intent-based search query &quot;furniture for remote job&quot;\" class=\"wp-image-10173\" srcset=\"https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/04\/vector_search_in_ecommerce_nearest_neighbor-1024x530.png 1024w, https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/04\/vector_search_in_ecommerce_nearest_neighbor-300x155.png 300w, https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/04\/vector_search_in_ecommerce_nearest_neighbor-768x397.png 768w, https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/04\/vector_search_in_ecommerce_nearest_neighbor-1536x794.png 1536w, https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/04\/vector_search_in_ecommerce_nearest_neighbor-2048x1059.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Key terms in vector search<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><a href=\"https:\/\/www.fact-finder.com\/blog\/ecommerce-site-search-vector-search\/\">Semantic search<\/a><\/strong> is an umbrella term for technologies that <a href=\"\/blog\/why-your-ecommerce-needs-intelligent-search-and-discovery-solutions\/\" target=\"_blank\" rel=\"noreferrer noopener\">understand the context and meaning of search queries<\/a> to find matching results, often using NLP. Vector search is a type of semantic search, specifically based on calculating mathematical distances between vectors.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><a href=\"https:\/\/www.fact-finder.com\/products\/vector-search.html\">Vector search<\/a><\/strong> is a specific method of semantic search that understands context and intent behind queries, even without any matching keywords. It compares shopper input with product data in a multidimensional space using &#8220;nearest neighbor search.\u201d While the graphic shows three dimensions, AI models typically use hundreds or even thousands.<br><\/li>\n\n\n\n<li><strong><a href=\"https:\/\/www.fact-finder.com\/products\/site-search.html\">Keyword search<\/a><\/strong> in eCommerce looks for similarities between search requests and product data. It\u2019s ideal for query types like brand names, product names and even product IDs, where precision matters. The quality of results depends on the search engine. Basic ones can stumble when a query doesn\u2019t exactly match what\u2019s in the catalog. <a href=\"https:\/\/www.fact-finder.com\/products\/site-search.html\" target=\"_blank\" rel=\"noreferrer noopener\">Smarter systems, like FactFinder\u2019s, use more advanced algorithms<\/a> to detect not only obvious typos like \u201csheos\u201d vs. \u201cshoes\u201d but also phonetic similarities like \u201cfone\u201d vs. \u201cphone\u201d. However, even the best keyword search doesn\u2019t actually understand what the user means.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/www.fact-finder.com\/products\/vector-search.html\">Hybrid search<\/a><\/strong> combines the strengths of keyword search and vector search. The goal is to return relevant hits in every situation, no matter what the customer types into the search bar: SKUs, keywords, mixed language and conversational queries or any mix of these. By intelligently weighting and ranking results, it creates a varied yet highly relevant search experience that meets a wide range of user intents. Resulting in fewer drop-offs, happier customers and a direct boost in revenue per customer.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">How does vector search work?<\/h2>\n\n\n\n<p>We\u2019ll walk through the workflow with a typical example: intent-based search queries. These queries indicate the reason behind the search. For example, if an online shopper types in &#8220;comfortable shoes for a city trip in July,&#8221; we can assume they\u2019re looking for shoes suitable for long walks on hard surfaces during summer temperatures. But how does vector search recognize this purchase intent and deliver fitting results? This process consists of three steps:<\/p>\n\n\n\n<p><strong>1. Vector embedding<\/strong> \u2013 An AI model translates the words into numeric expressions to capture the meaning behind the query. Now, it can understand that &#8220;comfortable&#8221; in the context of shoes refers to ergonomically designed, cushioned, lightweight, etc. And from &#8220;city trip&#8221; and &#8220;July,&#8221; it extracts, among other things, attributes related to the ground surface and season.<\/p>\n\n\n\n<p><strong>2. Nearest neighbor search<\/strong> \u2013 Next, the AI checks these numeric expressions with the product data to semantically similar items based on the mathematical distances in the vector space.<\/p>\n\n\n\n<p><strong>3. Response <\/strong>\u2013 The closest products are then returned to the shop as search results. For this search query, these might be breathable, low-cut hiking shoes with arch support.<\/p>\n\n\n\n<div style=\"height:28px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"521\" src=\"https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/04\/vector_search_in_ecommerce_how_it_works-01-1024x521.png\" alt=\"A visual representation of the 3 step process of vector search in eCommerce\" class=\"wp-image-10176\" srcset=\"https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/04\/vector_search_in_ecommerce_how_it_works-01-1024x521.png 1024w, https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/04\/vector_search_in_ecommerce_how_it_works-01-300x153.png 300w, https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/04\/vector_search_in_ecommerce_how_it_works-01-768x391.png 768w, https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/04\/vector_search_in_ecommerce_how_it_works-01.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Use cases: vector search vs. keyword search<\/h2>\n\n\n\n<p>Let\u2019s explore different types of search queries to compare the hypothetical performance of a vector search with keyword-based search methods.<\/p>\n\n\n\n<p>In some cases, vector search yields better results; in others, keyword search is more efficient. <a href=\"https:\/\/www.fact-finder.com\/landing-page\/video-llms-in-ecommerce\" target=\"_blank\" rel=\"noreferrer noopener\">Our analysis of search queries from over 2,000 shops<\/a> shows that online customers still mostly do not use natural language inputs \u2014 they don\u2019t ask questions like they do in Google, but search with short, precise terms. However, user behavior is changing as we speak, and in the coming years, a reliably functioning vector search could become vital for online retailers.<\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:180px\">\n<h3 class=\"wp-block-heading\">Use case<\/h3>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<h3 class=\"wp-block-heading\">Keyword search<\/h3>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<h3 class=\"wp-block-heading\">Vector search<\/h3>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-text-color has-vivid-purple-color has-alpha-channel-opacity has-vivid-purple-background-color has-background\"\/>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:180px\">\n<p><strong>\ud83d\udca1 Intent-based search queries<\/strong><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p> Processes precise keyword matches.&nbsp;<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Understands the context and intent behind search phrases like &#8220;casual but elegant office shoes.&#8221;<\/p>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:180px\">\n<p><strong>\u2795 Synonyms<\/strong><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Requires synonym entries like &#8220;sofa&#8221; = &#8220;couch.&#8221;<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Automatically recognizes semantic similarities without synonym lists.<\/p>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:180px\">\n<p>\u270d\ufe0f <strong>Spelling variations<\/strong><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Advanced algorithms like <a href=\"https:\/\/www.fact-finder.com\/products\/site-search.html\" target=\"_blank\" rel=\"noreferrer noopener\">FactFinder\u2019s Worldmatch<\/a><a href=\"https:\/\/www.fact-finder.com\/products\/site-search.html\" target=\"_blank\" rel=\"noreferrer noopener\">\u00ae<\/a> tolerate misspellings, typos, different languages and phonetic deviations like &#8220;naikee sneekers.&#8221;<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Understands similar concepts, regardless of spelling.<\/p>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:180px\">\n<p>\ud83d\udd3d <strong>Faceted search<\/strong><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><a href=\"\/blog\/faceted-search\/\" target=\"_blank\" rel=\"noreferrer noopener\">Allows precise filtering<\/a>. Example: &#8220;Adidas shoes&#8221; only shows Adidas shoes.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Recognizes related products and categories.<\/p>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:180px\">\n<p>\ud83c\udff7\ufe0f <strong>Brand-specific queries<\/strong><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Ensures only products from the searched brand are displayed.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Recognizes semantic relationships between brands, which could lead to broader results.<\/p>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:180px\">\n<p>\ud83c\udfaf<strong> Exact search<\/strong><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Supports precise queries like numerical IDs, <a href=\"\/blog\/why-b2b-ecommerce-tech-leads-should-prioritize-product-discovery-asap\/\" target=\"_blank\" rel=\"noreferrer noopener\">key for B2B eCommerce<\/a>, spare parts vendors and specialist stores.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Finds related products without an exact match, which in some cases, might not be desired.<\/p>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:180px\">\n<p>\ud83d\udccf <strong>Sizes and dimensions<\/strong><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Searches for exact product attributes like &#8220;mattress 180&#215;200.&#8221;<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Recognizes implicit meanings, e.g., &#8220;light laptop for travel.&#8221;<\/p>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:180px\">\n<p>\ud83c\udf0d&nbsp;<strong>Mix of languages<\/strong><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Understands queries like &#8220;shoes rojo&#8221; when it&#8217;s a cross-language concept.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Understands the meaning across languages, even with different formulations.<\/p>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:180px\">\n<p>\ud83d\udcac <strong>Natural language<\/strong><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Requires precise inputs. Example: &#8220;Shoes for travel.&#8221;<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Understands complex, natural sentences like &#8216;Which jackets are best for snow?<\/p>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-text-color has-vivid-green-cyan-color has-alpha-channel-opacity has-vivid-green-cyan-background-color has-background\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Hybrid mode keeps the customer experience in focus<\/h2>\n\n\n\n<p>Vector search isn&#8217;t always enough on its own, especially when it comes to exact product names, brands or item numbers. Many online shoppers still expect reliable keyword search. That&#8217;s why FactFinder blends advanced keyword search with vector capabilities \u2014 so you get exact matches when needed, and smart results when keyword search fails.<\/p>\n\n\n\n<p><strong>1. Linguistic matching <\/strong>\u2013 The core of our solution is the <a href=\"https:\/\/www.fact-finder.com\/products\/site-search.html\" target=\"_blank\" rel=\"noreferrer noopener\">Worldmatch\u00ae search algorithm<\/a>, which we\u2019ve been refining for over 20 years. The technology detects phonetic similarities between terms and ensures incorrect or imprecise inputs, like &#8220;naiki&#8221; vs. &#8220;Nike,&#8221; lead to the correct result.<\/p>\n\n\n\n<p><strong>2. Semantic relevance<\/strong> \u2013 Every industry, audience and product range comes with its own unique requirements. That\u2019s why FactFinder&#8217;s <a href=\"https:\/\/www.fact-finder.com\/products\/vector-search.html\">vector search<\/a> offers two modes:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Hybrid mode<\/strong> delivers a smart product list combining keyword-based and vector-based results. You decide which type of result should be weighted higher or lower based on your specific business needs. This mode is ideal for audiences who often browse for inspiration, have limited product knowledge or shop based on specific occasions.<\/li>\n\n\n\n<li><strong>Fallback mode<\/strong> kicks in only when keyword matching would return no results. Instead of landing on a zero-results page and potentially bouncing, customers see a selection of products that best match the intent and context of their search query.<\/li>\n<\/ul>\n\n\n\n<p><strong>3. Human expertise<\/strong> \u2013 Unlike some <a href=\"\/blog\/product-discovery\/\" target=\"_blank\" rel=\"noreferrer noopener\">search and product discovery<\/a> platforms, FactFinder is not a &#8220;black box.&#8221; You can easily track the sorting of your product and recommendation lists in an <a href=\"\/blog\/next-generation-user-interface\/\" target=\"_blank\" rel=\"noreferrer noopener\">intuitive backend editor<\/a>. It\u2019s the perfect blend of automation and control: you can let the AI do the work for you, while having the flexibility to manually adjust the configurations in line with your business goals, e.g., through ranking rules that push selected brands or sale items.<\/p>\n\n\n\n<p>Instead of blindly following the hype around vector search, FactFinder focuses on thoughtful, step-by-step integration. We don\u2019t view new technologies as an end in themselves but concentrate on the experience they bring to users and shoppers. Our solution applies AI to the areas where it creates real value, striking the best balance between keyword precision, semantic context and human expertise.<\/p>\n\n\n\n<div style=\"height:28px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/www.fact-finder.com\/request-demo.html\" target=\"_blank\" rel=\" noreferrer noopener\"><img loading=\"lazy\" decoding=\"async\" width=\"860\" height=\"223\" src=\"https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2024\/01\/Demo_Banner_EN_01_Zeichenflache-1.png\" alt=\"Demo Banner EN 01 Zeichenflache 1\" class=\"wp-image-9273\" srcset=\"https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2024\/01\/Demo_Banner_EN_01_Zeichenflache-1.png 860w, https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2024\/01\/Demo_Banner_EN_01_Zeichenflache-1-300x78.png 300w, https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2024\/01\/Demo_Banner_EN_01_Zeichenflache-1-768x199.png 768w\" sizes=\"auto, (max-width: 860px) 100vw, 860px\" \/><\/a><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion: get the best of both worlds<\/h2>\n\n\n\n<p>Vector search offers huge potential to increase conversion rates and AOVs, especially for intent-based search queries, synonyms and natural language inputs. However, it\u2019s not a cure-all. The future lies in hybrid search, a combined approach that blends semantic understanding, keyword precision and <a href=\"\/blog\/1-to-1-personalization\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI-powered personalization<\/a> to show the best results every time.<\/p>\n\n\n\n<p>We\u2019re here to help you address your unique eCommerce challenges. <a href=\"https:\/\/www.fact-finder.com\/request-demo.html\">Contact us today<\/a> to learn more about how our vector search&#8217;s hybrid mode can drive sustainable revenue growth.<\/p>\n\n\n\n<div class=\"wp-block-media-text is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><a href=\"https:\/\/www.fact-finder.com\/resources\/interactive-demo.html\" target=\"_blank\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/03\/thumbnail_interactive_demo_open_-1024x576.jpg\" alt=\"thumbnail interactive demo open\" class=\"wp-image-9981 size-full\" srcset=\"https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/03\/thumbnail_interactive_demo_open_-1024x576.jpg 1024w, https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/03\/thumbnail_interactive_demo_open_-300x169.jpg 300w, https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/03\/thumbnail_interactive_demo_open_-768x432.jpg 768w, https:\/\/www.fact-finder.com\/blog\/wp-content\/uploads\/2025\/03\/thumbnail_interactive_demo_open_.jpg 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure><div class=\"wp-block-media-text__content\">\n<h5 class=\"wp-block-heading\"><strong>Interactive&nbsp;demo video<\/strong><\/h5>\n\n\n\n<p>Dive into this interactive demo to explore both the front and the back end of the FactFinder platform and our suite of advanced features. <a href=\"https:\/\/www.fact-finder.com\/resources\/interactive-demo.html\" target=\"_blank\" rel=\"noreferrer noopener\">Watch video now<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div style=\"height:69px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n<ol class=\"wp-block-footnotes\"><li id=\"66abd1be-6c3c-41a9-8717-4c24d470b94d\"><a href=\"https:\/\/baymard.com\/blog\/ecommerce-search-query-types\" target=\"_blank\" rel=\"noreferrer noopener\">Deconstructing E-Commerce Search UX: The 8 Most Common Search Query Types (41% of Sites Have Issues) \u2013 Baymard<\/a> <a href=\"#66abd1be-6c3c-41a9-8717-4c24d470b94d-link\" aria-label=\"Jump to footnote reference 1\">\u21a9\ufe0e<\/a><\/li><\/ol>\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>41% of online shops have problems with their search function. Is vector search the key to higher relevance and fewer drop-offs? Here&#8217;s a crash course from an eCommerce perspective.<\/p>\n","protected":false},"author":51,"featured_media":10206,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":"[{\"content\":\"<a href=\\\"https:\/\/baymard.com\/blog\/ecommerce-search-query-types\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">Deconstructing E-Commerce Search UX: The 8 Most Common Search Query Types (41% of Sites Have Issues) \u2013 Baymard<\/a>\",\"id\":\"66abd1be-6c3c-41a9-8717-4c24d470b94d\"}]"},"categories":[225],"tags":[230,199,27],"class_list":["post-10172","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ecommerce-insights","tag-ai","tag-ecommerce","tag-search-2"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Vector search in eCommerce: when semantic search increases conversion - FactFinder blog<\/title>\n<meta name=\"description\" content=\"41% of online shops have problems with their search function. Is vector search the key to higher relevance and fewer drop-offs? Here&#039;s a crash course from an eCommerce perspective.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.fact-finder.com\/blog\/vector-search\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Vector search in eCommerce: when semantic search increases conversion - FactFinder blog\" \/>\n<meta property=\"og:description\" content=\"41% of online shops have problems with their search function. Is vector search the key to higher relevance and fewer drop-offs? 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