{"id":9310,"date":"2024-03-18T10:41:47","date_gmt":"2024-03-18T09:41:47","guid":{"rendered":"https:\/\/www.fact-finder.com\/blog\/?p=9310"},"modified":"2025-04-07T13:11:09","modified_gmt":"2025-04-07T11:11:09","slug":"factfinder-launches-atlas-ai-a-new-era-in-ecommerce-personalization","status":"publish","type":"post","link":"\/blog\/factfinder-launches-atlas-ai-a-new-era-in-ecommerce-personalization\/","title":{"rendered":"FactFinder launches Atlas AI: a new era in eCommerce personalization\u202f"},"content":{"rendered":"\n

We are excited to announce the official launch of Atlas AI – a major step for our product discovery platform. Atlas AI is like going into a local shop that rearranges its shelves for each customer, showcasing the most relevant products for their current visit. The AI creates real-time, 1:1 personalized experiences, elevating customer engagement to unprecedented levels. As a result, businesses benefit from higher conversion rates and tailored interactions, bringing the joy of a perfect find online.

Let\u2019s see how it works. <\/p>\n\n\n\n

What does a relevant search look like?<\/h2>\n\n\n\n

Let’s consider two people both looking for an “overshirt\u201d online. The first shopper is all about fashion, eyeing a chic item from a high-end brand. The second person, however, is hunting for something cozy in wool, keen on finding a good deal. This is where Atlas AI steps in. It catches these subtle differences in preferences \u2013 the first shopper\u2019s lean towards premium brands and the second shopper\u2019s focus on material and cost. By doing so, Atlas AI makes sure that each shopper finds exactly what they\u2019re after, personalizing search results based on unique intent and preferences.<\/p>\n\n\n\n

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The issue lies in the lack of behavioral data. Unlike in physical stores where salespeople can personally observe and ask for individual preferences, online shops often lack enough interactional data to grasp each shopper’s unique tastes. To address this challenge, many eCommerce solutions turn to segmentation based on demographic data. But this approach is error-prone and can lead to inaccurate results. Consider Jeff Bezos and Bruce Springsteen – two people who couldn’t be more different, but surprisingly share a lot of demographic similarities including age, gender, nationality and wealth. Despite this long list of similarities, it’s very unlikely that they’d share the same shopping preferences. <\/p>\n\n\n\n

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With Atlas AI, we approach relevance differently<\/h2>\n\n\n\n

Atlas AI is above and beyond traditional segmentation. When it comes to delivering spot-on relevance, we have a three-step approach that ensures a tailored shopping experience for every customer: <\/p>\n\n\n\n

1. General relevance based on shopper\u2019s behavior<\/strong>  <\/p>\n\n\n\n

What it does: <\/strong>Atlas AI optimizes search results and recommendations based on shoppers\u2019 overall behavior on the site. This includes search queries and interaction patterns.  <\/p>\n\n\n\n

How it benefits you: <\/strong>this significantly reduces the need for manual search optimization. <\/p>\n\n\n\n

Example: <\/strong>if customers mainly click on iPads rather than cases or cables after searching “iPad,” Atlas learns to show iPads first. It also keeps up with trends, like favoring ski helmets over bike helmets in winter, ensuring search results always match what users are currently looking for. <\/p>\n\n\n\n

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2. Relevance based on the current shopping intent of each shopper<\/strong>  <\/p>\n\n\n\n

What it does: <\/strong>this step focuses on the immediate intent of the shopper’s current session, adapting search results based on the latest interactions and signs of buying intent.  <\/p>\n\n\n\n

How it benefits you: <\/strong>this creates a more natural buying experience for the shopper as the model understands what they are looking for and only shows them products that are relevant in this context. <\/p>\n\n\n\n

Example: <\/strong>when a shopper moves from searching for “LG TV” to “Samsung,” Atlas AI swiftly identifies this shift, understanding the shopper’s continued interest in televisions. It then adjusts the search results to prioritize Samsung TVs, effectively aligning with the shopper’s updated search preferences and showcasing its ability to dynamically respond within the shopping session. <\/p>\n\n\n\n

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3. Relevance based on personal preferences<\/strong>  <\/p>\n\n\n\n

What it does:<\/strong> this involves a deeper, more granular understanding of individual preferences, inferred from past purchases, detailed product views and interaction patterns with specific product attributes.  <\/p>\n\n\n\n

How it benefits you: <\/strong>this step is about precision. It uses a nuanced understanding of personal preferences to tailor the shopping experience for every single shopper. This results in more personal relevance for your shoppers and higher conversion rates for you. <\/p>\n\n\n\n

Example: <\/strong>after buying a metallic espresso machine, Atlas AI tailors search results and recommendations, noting a preference for premium, metallic kitchen items. It suggests similar appliances but shifts strategy for unrelated searches like TVs, focusing on quality without the metallic preference. This demonstrates Atlas AI’s skill in applying relevant preferences across different contexts, smartly distinguishing between product categories. <\/p>\n\n\n\n

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Synergy of Atlas AI and Worldmatch<\/h2>\n\n\n\n

To achieve effective product discovery, two key components are essential: retrieval (finding the right products) and ranking (arranging them in the most relevant order). FactFinder’s Atlas AI, in collaboration with Worldmatch, combines these elements seamlessly to elevate the shopping experience. <\/p>\n\n\n\n

Worldmatch, our patented and language-independent search core, stands out for its exceptional retrieval capabilities. Developed from over 20 years of experience in eCommerce search, Worldmatch employs a sophisticated word matching approach. It ensures shoppers find exactly what they’re looking for, swiftly and accurately, even when faced with typos, variations and other common search query inaccuracies.

Combined with Atlas AI, with its human-like grasp of your assortment and individual shopper preferences, Wordmatch excels in ranking search results so each shopper sees what’s most relevant to them. This capability, grounded in advanced neural networks, personalizes the shopping experience from the very first click. <\/p>\n\n\n\n

Garnished with human intelligence<\/h2>\n\n\n\n

Although Atlas AI automates relevance, our solution still allows you to fine-tune your product rankings effortlessly. Unlike more rigid AI solutions that often act as “black boxes” with limited options for adjustment, here you’re in complete control. It’s as simple as adjusting a dial, enabling you to boost high-margin items, highlight seasonal products or tailor your strategy to align with specific business goals.<\/p>\n\n\n\n

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Summing up the reasons you’ll love it<\/h2>\n\n\n\n

Atlas AI is designed to be a game changer in product discovery. Here\u2019s what to keep in mind: <\/p>\n\n\n\n