Understand vectors and semantics
Beyond keyword matching. Searching by what shoppers mean
When it comes to commerce search, helping shoppers find what they are looking for efficiently and effectively is crucial for a successful shopping experience. Limiting search results to keyword match can lead only to missed opportunities and frustrated shoppers.
The traditional keyword search approach has been the foundation of the search functionality in commerce stores. However, as shoppers' expectations evolve and search technologies advance, there’s a need for more intelligent and effective search solutions. This is where Empathy's hybrid search, based on keyword and vector search, comes into play.
Upgrading search to the next level with vectors and semantic models
Integrating vector-based search capabilities into commerce search provides a better contextual understanding. Therefore, Empathy Platform embraces a hybrid search approach that combines the strengths of different search techniques to deliver enhanced search experiences in commerce stores. It integrates together keyword search, which matches search queries to indexed terms in the product catalogue, with vector search, which leverages advanced algorithms to understand the semantic meaning and context of search queries and documents.
The language used by shoppers is different from the technical one used by product teams describing the catalogue and its attributes.
Empathy Platform vector-based approaches help bridge this gap by mapping semantic similarities between your own shoppers’ search behaviors and your product catalogue only, without relying on external sources or compromising your data privacy. Thus, the search service gains a deeper understanding of your commerce ecosystem and the context behind search queries. It can recognize synonyms, handle misspellings or typos, and provide relevant search results, even when the search terms in the query are not an exact match to the indexed terms.
Get to know more about Empathy Platform's vector search:
Vector search aimed at individuals
The inferred semantic relationships got from vector embeddings can be used at different stages and by different stakeholders:
Shoppers
Improving product findability.
Considering the semantic meaning and context of search queries, shoppers get more accurate and relevant results, regardless of whether they're organic or sponsored results. This ensures that shoppers quickly find the products they are looking for.
Reducing the frustration of zero results.
Understanding the intent behind the shoppers’ queries, relevant alternative suggestions or related products can be provided when an exact match is not found. This helps shoppers overcome dead ends and discover search and product alternative options.
Inspiring product discovery.
Leveraging machine learning, natural language processing, and artificial intelligence techniques can provide contextualized recommendations and suggestions based on shoppers’ behaviors to improve query expansion. It leads shoppers to relevant products or similar intent suggestions.
Engage and personalize the shopping experience, encouraging shoppers to explore more and discover new products with Vectorized Recommendations.
Merchandisers
Improving the findability of campaigns and promotions with synonyms.
By adding relevant terms and attributes, merchandisers ensure their campaigns are easily discoverable through search. Additionally, synonym suggestions are provided to expand the reach of campaigns and ensure that shoppers find them regardless of the search terms used.
Explaining semantic models
Providing visual insights into the inner workings of semantic models helps merchants grasp shoppers’ intent and gain a clearer understanding of their preferences.
Make your product catalogue speak in your shoppers’ language with the Synonymize Suggestion tool within Empathy Plaform Playboard.
Catalogue managers
Improving product findability by enriching product catalogue tagging.
Enriching the product catalogue with tags to deliver more accurate and relevant search results, based on synonyms, relevant keywords, and specific attributes that align with shoppers’ queries.
Learn more about enriching product search catalogues with semantic models on the Experience vector search in Empathy Platform page.
Sellers and CPGs
Empowering brand-related insights and opportunities.
By analyzing queries and shoppers’ behavior, sellers and CPGs can gain a deeper understanding of how their brands are perceived and sought after. Leverage the insights to optimize product offerings, marketing strategies, and shopper engagement to strengthen brand presence.
Leveraging explainability for CPGs.
Providing insights on how to optimize product catalogue attributes for improved both organic and sponsored product positioning.
Get to know more about the Play Vector explainability tool to grasp what, why, and how of vectorized results.
Proprietary data consent integrity
Empathy Platform vector search is based on privacy control measures as a firewall against legal and reputational risks for brands. These measures ensure the data integrity of semantic models by verifying consent management in their commerce stores. Thus, proprietary tunning datasets can be used effectively and securely to extend and improve your commerce search experiences.
Semantic privacy by design
With privacy in mind, semantic models are built from scratch, based only on your shoppers’ behavioral data and your product catalogue information. It requires a comprehensive understanding of your brand's data and domains, leading to a real customized model approach to deliver relevant search experiences while respecting privacy constraints and keeping the integrity of your data.