{"id":1558080,"date":"2025-11-20T07:21:12","date_gmt":"2025-11-20T12:21:12","guid":{"rendered":"https:\/\/www.practicalecommerce.com\/?p=1558080"},"modified":"2025-11-21T06:09:19","modified_gmt":"2025-11-21T11:09:19","slug":"will-ai-solve-ecommerce-personalization","status":"publish","type":"post","link":"https:\/\/www.practicalecommerce.com\/will-ai-solve-ecommerce-personalization","title":{"rendered":"Will AI Solve Ecommerce Personalization?"},"content":{"rendered":"<p>A nascent firm armed with a fresh $12.3 million investment aims to deliver on the promise of ecommerce personalization.<\/p>\n<p>A personalization engine shows the right product to the right shopper at the right time. In theory, it makes everyone happy. Shoppers see relevant and engaging products. Merchants sell more.<\/p>\n<p>It sounds simple enough. Think of an ecommerce website with products for sale. What item(s) does the site show to a particular user to entice a sale? How does it know what to show?<\/p>\n<h3>Data Right Now<\/h3>\n<p>This question of &#8220;what to show&#8221; is how Matteo Ruffini, chief science officer of the Swiss start-up <a href=\"https:\/\/usealbatross.ai\/\">Albatross AI<\/a>, described the problem his company solves during a February 2025 <a href=\"https:\/\/www.youtube.com\/watch?v=cM0vnOVyNus\">interview<\/a>.<\/p>\n<p>Many ecommerce personalization and recommendation solutions rely on historical shopper behavior. The systems look backward over months or years, at purchases and browses, for instance.<\/p>\n<p>The folks at Albatross also use past behavioral data, but they&#8217;ve added a real-time, right-now <a href=\"https:\/\/www.practicalecommerce.com\/ad-attribution-gets-a-crystal-ball\">predictive element<\/a>.<\/p>\n<p>The Albatross product, <a href=\"https:\/\/www.forbes.com\/sites\/charliefink\/2025\/11\/18\/albatross-raises-1225-million-to-build-the-second-pillar-of-ai\/\">according<\/a> to a Forbes contributor, &#8220;captures every user action in a session and passes it into [an AI] transformer model that behaves like a language model for intent. The inputs are event triplets \u2014 user, action, item \u2014 instead of words. The model analyzes not just the action but the sequence of actions and the context that connects them. It updates continuously and responds in milliseconds without retraining.&#8221;<\/p>\n<p>Essentially, the company claims to have the first AI infrastructure for training models on sequential, live events.<\/p>\n<div id=\"attachment_1558132\" style=\"width: 580px\" class=\"wp-caption alignnone\"><a href=\"https:\/\/www.practicalecommerce.com\/wp-content\/uploads\/2025\/11\/Albatross-diagram-real-time-personalization.jpg\" target=\"_blank\" rel=\"noopener\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-1558132\" class=\"wp-image-1558132 size-large\" title=\"A flow-diagram illustrating a real-time personalization system by Albatross.\" src=\"https:\/\/www.practicalecommerce.com\/wp-content\/uploads\/2025\/11\/Albatross-diagram-real-time-personalization-570x358.jpg\" alt=\"A flow-diagram illustrating a real-time personalization system by Albatross. At the bottom left, several orange-toned blocks represent item embeddings feeding into a \u201cLarge Event Model.\u201d To the right, small orange blocks show a \u201clive sequence of events\u201d coming from a smartphone-shaped icon. These events flow into the model, which outputs a horizontal row of blue blocks labeled \u201cReal-Time User Embedding\u201d at the top left. An arrow carries this embedding to the top right, where gray-toned blocks represent \u201cBest items based on in-session user behaviour.\u201d The overall layout shows events from a user\u2019s device informing embeddings to generate personalized item recommendations.\" width=\"570\" height=\"358\" srcset=\"https:\/\/www.practicalecommerce.com\/wp-content\/uploads\/2025\/11\/Albatross-diagram-real-time-personalization-570x358.jpg 570w, https:\/\/www.practicalecommerce.com\/wp-content\/uploads\/2025\/11\/Albatross-diagram-real-time-personalization-300x189.jpg 300w, https:\/\/www.practicalecommerce.com\/wp-content\/uploads\/2025\/11\/Albatross-diagram-real-time-personalization-768x483.jpg 768w, https:\/\/www.practicalecommerce.com\/wp-content\/uploads\/2025\/11\/Albatross-diagram-real-time-personalization.jpg 800w\" sizes=\"auto, (max-width: 570px) 100vw, 570px\" \/><\/a><p id=\"caption-attachment-1558132\" class=\"wp-caption-text\">Albatross claims to have the first AI infrastructure for training models on sequential, live events.<\/p><\/div>\n<h3>3 Challenges<\/h3>\n<p>Albatross AI addresses at least three long-standing problems with predictive ecommerce recommendations:<\/p>\n<ul>\n<li>Long training periods.<\/li>\n<li>Categorizing new shoppers.<\/li>\n<li>Cold starts for products.<\/li>\n<\/ul>\n<h4>Training<\/h4>\n<p>Personalized and segment-based recommendations depend on machine learning models that need time and data to mature. It can take weeks or months to gather enough data for meaningful recommendations. Moreover, the model must retrain often.<\/p>\n<p>Some recommendation solutions train in cycles, such as daily or weekly, and they require reams of historical shopping activity. The result is recommendations that can lag behind rapidly changing demand signals, seasonal trends, influencer surges, or unpredictable cultural moments (such as the pandemic).<\/p>\n<p>A shopper&#8217;s intent can change today, but if not in the next training cycle, the system cannot react.<\/p>\n<p>Emerging platforms such as Albatross explore continuous or incremental learning, reducing reliance on scheduled retraining and moving toward models that reflect active sessions.<\/p>\n<h4>New shoppers<\/h4>\n<p>A second long-standing challenge is how recommendation systems treat new shoppers.\u00a0Historically, these systems relied on popularity-driven rankings or generic best-sellers while they waited to gather enough signals to personalize.<\/p>\n<p><a href=\"https:\/\/www.practicalecommerce.com\/the-new-era-of-cookie-less-ad-targeting\">Cookie-less personalization<\/a> or probable <a href=\"https:\/\/www.practicalecommerce.com\/the-rebirth-of-marketing-mix-modeling\">identity matching<\/a> offers only limited relief.<\/p>\n<p>The industry is now shifting toward what could be described as &#8220;first-minute personalization,&#8221; meaning that intent signals within a single session \u2014 scroll depth, dwell time, bounce patterns, micro-hovers, theme switches \u2014 become the primary inferences.<\/p>\n<p>The goal is to reduce the number of interactions required to understand a shopper&#8217;s interests and intents.<\/p>\n<h4>Cold start<\/h4>\n<p>The third obstacle is the cold start product problem.<\/p>\n<p>An ecommerce catalog is rarely static. New SKUs arrive every day; marketplaces can add thousands per hour.<\/p>\n<p>Current recommendation algorithms need interaction data before they can confidently suggest an item. Hence new products may remain buried.<\/p>\n<p>Marketers can mark them as new and provide preferential treatment in search and on category pages. But those actions can defeat the purpose of <a href=\"https:\/\/www.practicalecommerce.com\/gen-ai-nears-hyper-personalization-at-scale\">personalized recommendations<\/a>.<\/p>\n<p>AI approaches are beginning to leverage content embedding, multimodal representation, and sequential modeling to infer probable relevance before engagement data is available. Essentially, AI understands much better which shoppers will like the new product.<\/p>\n<p>Research continues to uncover ways to combine item metadata, textual or image-based descriptions, and user-sequence context so that new items are visible on day one.<\/p>\n        <div class=\"backlist-wrapper\">\n            <h3 class=\"recommended\"><div class=\"icon\"><\/div> Recommended<\/h3>\n            <a href=\"https:\/\/www.practicalecommerce.com\/agentic-commerce-has-arrived?utm_source=back\" >\n                <div class=\"backlist-content\" >\n                    <div href=\"\" class=\"thumb-link\">\n                                                    <img decoding=\"async\" src=\"https:\/\/www.practicalecommerce.com\/wp-content\/uploads\/2025\/10\/agentic-commerce-has-arrived.jpg\" alt=\"Agentic Commerce Has Arrived\" loading=\"lazy\" \/>\n                                            <\/div>\n                    <div class=\"backlist-detail\">\n                                                <h4>Agentic Commerce Has Arrived<\/h4>\n                                                    <p class=\"backlist-date\">October 2, 2025<\/p>\n                                            <\/div>\n                <\/div>\n            <\/a>\n        <\/div>\n        \n<h3>AI and Commerce<\/h3>\n<p>The three challenges apply to other trends in ecommerce and the ongoing AI transformation.<\/p>\n<p>LLMs such as ChatGPT, Perplexity, and Gemini are attempting to rank products for individuals through <a href=\"https:\/\/www.practicalecommerce.com\/agentic-commerce-has-arrived\">agentic commerce<\/a>. Yet none of these will deliver unless they can interpret shopping intent.<\/p>\n<p>In short, recommendation engines and AI shopping agents are becoming blurred. Product discovery and purchase decisions are merging.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Albatross AI claims to have the first infrastructure for training personalization models on live events.<\/p>\n","protected":false},"author":391,"featured_media":1558136,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_wp_rev_ctl_limit":""},"categories":[58303],"tags":[],"class_list":["post-1558080","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-customer-retention",""],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.2 (Yoast SEO v25.2) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Will AI Solve Ecommerce Personalization? - Practical Ecommerce<\/title>\n<meta name=\"description\" content=\"Albatross AI claims to have the first infrastructure for training personalization models on live events.\" \/>\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.practicalecommerce.com\/will-ai-solve-ecommerce-personalization\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Will AI Solve Ecommerce Personalization?\" \/>\n<meta property=\"og:description\" content=\"Albatross AI claims to have the first infrastructure for training personalization models on live events.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.practicalecommerce.com\/will-ai-solve-ecommerce-personalization\" \/>\n<meta property=\"og:site_name\" content=\"Practical Ecommerce\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/practicalecommerce\" \/>\n<meta property=\"article:published_time\" content=\"2025-11-20T12:21:12+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-21T11:09:19+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.practicalecommerce.com\/wp-content\/uploads\/2025\/11\/Will-AI-Solve-Ecommerce-Personalization.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"360\" \/>\n\t<meta property=\"og:image:height\" content=\"240\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Armando Roggio\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@ecommerceboy\" \/>\n<meta name=\"twitter:site\" content=\"@practicalecomm\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Armando Roggio\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"NewsArticle\",\"@id\":\"https:\/\/www.practicalecommerce.com\/will-ai-solve-ecommerce-personalization#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.practicalecommerce.com\/will-ai-solve-ecommerce-personalization\"},\"author\":{\"name\":\"Armando Roggio\",\"@id\":\"https:\/\/www.practicalecommerce.com\/#\/schema\/person\/357c476238d0b5e19bd0a0bfacefe722\"},\"headline\":\"Will AI Solve Ecommerce Personalization?\",\"datePublished\":\"2025-11-20T12:21:12+00:00\",\"dateModified\":\"2025-11-21T11:09:19+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.practicalecommerce.com\/will-ai-solve-ecommerce-personalization\"},\"wordCount\":703,\"publisher\":{\"@id\":\"https:\/\/www.practicalecommerce.com\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.practicalecommerce.com\/will-ai-solve-ecommerce-personalization#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.practicalecommerce.com\/wp-content\/uploads\/2025\/11\/Will-AI-Solve-Ecommerce-Personalization.jpg\",\"articleSection\":[\"Customer Retention\"],\"inLanguage\":\"en-US\",\"copyrightYear\":\"2025\",\"copyrightHolder\":{\"@id\":\"https:\/\/www.practicalecommerce.com\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.practicalecommerce.com\/will-ai-solve-ecommerce-personalization\",\"url\":\"https:\/\/www.practicalecommerce.com\/will-ai-solve-ecommerce-personalization\",\"name\":\"Will AI Solve Ecommerce Personalization? 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