Whether e-commerce shoppers are browsing a website, interacting with a voice assistant, or using an AI-powered shopping agent, successful product discovery requires a deep understanding of user intent.
Many shoppers don’t start their journey with a specific product in mind. Instead, they come with a goal or a set of needs. Consider a shopper expressing: “I’m looking for a product to hydrate my skin, but it must be cruelty-free and free from harsh chemicals that could affect my health.”
In a world where AI is on the rise, discovery engines must evolve to interpret natural language, identify relevant attributes, and surface products that match shoppers’ needs.
Today’s discovery platforms need to account for multiple dimensions at once. They start by unraveling true user intent and preferences, ensuring that recommendations and search results align with what the shopper actually wants – not just what they happen to type. At the same time, they consider real-time product availability and fulfillment options, ensuring users see only items that are in stock and ready for delivery. Behavioral signals such as clickstream activity, wishlist additions, and past interactions can add another layer of personalization, helping systems anticipate needs more accurately.
What are Vector Embeddings?
Vector embeddings form the foundation of modern e-commerce discovery systems. AI-Models (like Word2Vec, FastText, BERT, GPT, etc.) translate complex, unstructured data (such as product descriptions, user queries, and browsing behavior—into high-dimensional vectors (numbers) that machines can process and “understand”. These vectors form a vector space, where semantically similar items cluster together, allowing systems to recognize relationships beyond traditional methods based on explicit rules, dictionaries and heuristics.
For example, when a customer searches for “lightweight summer footwear”, vector embeddings help the system recognize that cork-sole sandals, mesh flats, and espadrilles are all relevant, even if those exact terms aren’t mentioned in the query or item descriptions. This semantic understanding drives a more intuitive product discovery experience.
How can you use Vector Embeddings in retail and e-commerce?
Search and product discovery shape every step of the online shopper journey – and for e-commerce brands, they’re true game-changers. A smarter, AI-driven search experience not only enhances the shopping experience but also drives higher conversion rates, increases average order value (AOV) and revenue per visitor (RPV), and strengthens customer loyalty. At the core of AI-powered search is vector search, powered by vector embeddings.
Vector Search and its applications
Vector search retrieves products based on their coordinates within a semantic space, where proximity between vectors reflects the similarity of meaning or relationship between items.
Using this, we can find the most relevant or ideal candidates for the shopper’s written query. These candidates are then ranked by factoring in true user intent, behavioral signals such as clickstream activity (session behavior, past purchases, brand preferences, etc.), product availability, fulfillment options, wishlist additions, and in some cases even community sentiment from reviews and ratings. All this data can also be vectorised and converted into vector embeddings allowing for richer representations of both user behavior and product attributes.
Together, these capabilities ensure that shoppers always see the most relevant products for their current needs, whether they’re first-time visitors or loyal returning customers. This transforms search from a basic utility into a powerful engine for discovery, engagement, and conversion, creating a smooth and intuitive journey from initial interest to final purchase.
How Vector Embeddings Supercharge Search Engines
Vector Embeddings can help elevate your search engine to deliver smarter, more intuitive results. They enable more relevant and conversion-optimized results that help users find what they need while driving greater business outcomes through:
- Query understanding and expansion – interpret and enrich user input to improve accuracy and recall.
- Semantic matching of queries to items – understand user intent even when exact keywords don’t match.
- Handling long-tail and vague queries – returning high-quality results for uncommon or natural-language inputs.
- “No results” fallback recommendations – by surfacing semantically related items when there’s no exact match.
- Auto-suggestions powered by embeddings – recommend queries or refinements based on similarity to successful past searches.
- Ranking and reranking results by relevance – using deep contextual similarity or behavioural patterns.
Recommendations powered by Vector Embeddings
Search is just one side of the coin. AI-powered recommendation engines personalize the discovery journey across the entire shopping funnel. By analyzing real-time behavioral signals – such as clicks, dwell time, wishlist activity, past orders and more – modern ecommerce platforms deliver tailored suggestions that increase conversion and customer satisfaction, reduce cart abandonment, and drive revenue growth.
Vector embeddings power these sophisticated recommendation systems by encoding products and user preferences into the shared vector space. As users interact with products, their actions shape their preference vector. This preference vector is then compared against product embeddings to surface the most relevant recommendations in real time.
This allows recommendation systems to understand the underlying features that make products appealing to specific users. For example, a shopper who frequently purchases eco-friendly skincare products may start seeing similarly aligned items – such as organic shampoos, sustainable makeup brands, or biodegradable packaging options – even across categories they haven’t previously explored.
By delivering relevant, personalized suggestions at the right moment, e-commerce brands can:
- Increase average order value (AOV)
- Reduce cart abandonment rates
- Boost conversion rates across the funnel
- Drive customer loyalty through more meaningful experiences
Datos’ Vector Embeddings for E-commerce
At Datos, we process billions of user queries and actions across the web and multiple domains, including e-commerce. Within the e-commerce industry alone, our clickstream data covers around 3700 websites. This allows us to observe all the actions users from our panel take on these sites and use that information to build comprehensive vector representations of user behavior.
We can generate vector embeddings for user queries, specific items, domains, user sessions, and more. This enriched knowledge can boost the capabilities of search engines and recommendation models, enabling them to better understand user intent and more accurately predict outcomes such as conversions, click-through rates (CTR), and beyond.
While Vector Embeddings are essential for many AI applications, they are computationally expensive and resource-intensive to train and run. To address this, Datos has two powerful solutions that reduce operational costs while improving shopping experience through more accurate intent recognition, improved classification, enhanced semantic similarity matching, and deeper insights into shopping behavior:
- Pre-built Vector Embeddings trained on massive clickstream corpora: Our model is pre-trained on a large-scale dataset of shopping journeys and user behaviors collected across e-commerce websites and major search platforms. Pre-built Vector Embeddings are ready to be customized for specific business needs and industry verticals, adaptable to a wide range of use cases, product catalogs, and niche markets.
- Datos out-of-the-box model: For custom Vector Embeddings, built with your data. We are ready to generate customized Vector Embeddings by fine-tuning our model on your user interactions and catalog data. Once fine-tuned, we deliver tailored vector embeddings in batches, enabling immediate improvements in shopping experience, relevance, and product discovery.
Datos Vector Embeddings provide powerful solutions tailored specifically for e-commerce use cases:
- Semantic Product Discovery: Semantic search enables a deeper understanding of user intent, allowing the system to grasp the concepts behind a query — not just the literal words. For example, when a shopper searches for a “casual summer outfit,” the system interprets the meaning behind the request, recognizing the desire for lightweight, seasonal clothing. It can then surface relevant products like sundresses, shorts, or lightweight shirts, even if those exact terms aren’t explicitly mentioned in product descriptions.
- Behavioral Vector Embeddings: By learning from millions of user interactions (clicks, purchases, dwell time, and other engagement signals) behavioral embeddings continuously refine the system’s understanding of item relevance and conversion likelihood. This allows search engines and recommendation systems to better predict what users truly want, surfacing more accurate results faster and minimizing irrelevant options that can clutter traditional systems.
- Recommendations: By analyzing the relationships between product, behavioral, and semantic vector embeddings, e-commerce platforms can generate highly effective recommendations. This deeper matching improves product discovery, boosts engagement, and drives higher conversion rates.
- Сhatbots and generative AI applications: Vector search plays a central role in Retrieval-Augmented Generation (RAG) systems. By injecting vector embeddings into a model’s context, generative AI applications – such as chatbots or shopping assistants – can enrich their responses with factual, domain-specific content. This leads to more accurate, contextually relevant interactions that improve customer satisfaction and trust.
Deep dive: Vector Embeddings for improved Recommender Systems
With our extensive global clickstream data (spanning over 3700 e-commerce websites worldwide), our Vector Embeddings are built to capture and reflect the complexity of real-world, ever-evolving shopper behavior.
By integrating these embeddings into e-commerce recommendation engines, we can help surface more relevant, context-aware results across shopper experience. This leads to smarter product discovery, higher engagement, and ultimately, stronger conversion and revenue growth. Our Vector Embedings help to identify:
- Closest items to a selected item – for “similar products” or “you may also like” sections.
- Best-matching items for a given query or category – for search and dynamic category pages.
- Frequently co-viewed or co-purchased items – to support bundles, upsells, or cross-sell recommendations.
- Trending items in a user’s behavioral cluster – for timely and personalized homepages or landing pages.
- Next best actions or items in the conversion path – to guide users through frictionless purchase flows.
- Recovery from zero-result or null queries – using semantic similarity to offer relevant fallback options.
- Tailored recommendations for long-tail or niche queries – increasing discoverability across the catalog.
Whether you’re building a recommender system from scratch or optimizing an existing one, our vector embeddings provide the depth and flexibility needed to make every user interaction smarter and more impactful.
What you can expect from Datos Vector Embeddings
- Trained on globally sourced, daily clickstream data across multiple industries and domains
- Fine-tuned on the clickstream data of tens of millions of users across thousands of e-commerce websites worldwide
- Seamless delivery: Batch transfers or daily updates
- Fully customizable: Adapted to specific product catalogs to meet unique business needs
Unlock Deeper User Insights with Datos’ Domain-Spanning Vector Embeddings
While you can vectorize and analyze the clickstream data from your own website, you’re still missing a crucial part of the picture: visibility into user queries, browsing paths, and actions across competitor websites, AI shopping agents, and search engines like Google.
That’s where Datos’ vector embeddings come in. They give you access to a much broader behavioral context – insights that extend beyond your own domain. This enriched knowledge can improve discovery metrics, reduce the number of “zero results” queries, and unlock additional performance gains across search and product discovery.
For your search engine, we can provide vector embeddings built across any domain or group of domains – covering user queries, paths to conversion, and other critical signals – fully customized to your specific needs. Alternatively, you can reduce computational costs by using our model as an out-of-the-box solution: simply send us your data, and we’ll return high-quality, processed vector embeddings ready for integration.
How do we train our model?
Datos pipelines incorporate daily updates to capture evolving shopper behaviors, new product trends, and seasonal variations. Our models are fine-tuned on domain-specific e-commerce data and learn from fresh clickstream data, capturing subtle user behavior to drive conversion rates and customer satisfaction.
Our vector embeddings are trained on globally sourced, up-to-date clickstream data across multiple industries and domains, using advanced modeling techniques, including transformer-based architectures, contrastive learning, and domain-specific fine-tuning. This training leverages:
- A panel of tens of millions of active users worldwide
- 15Bn+ URLs processed monthly
- Billions of user queries and actions
- Domain and industry categorization
Our clickstream data provides complex insights into anonymous user behavior across the web. We capture and analyze:
- How shoppers search and shop across platforms (Google, e-commerce sites, AI agents)
- User journeys from initial search to conversion
- Deep behavioral patterns specific to different product categories
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