5 min read

Approach relevance in commerce search

Optimizing organic product ranking for relevant shopping journeys

In the dynamic world of commerce search, providing a seamless and efficient search journey is crucial for shoppers’ experience and business success. Shoppers expect to find relevant and accurate product results quickly, and the effectiveness of a commerce search system directly impacts shopper trust and engagement. Thus, optimizing the relevance of search and product results becomes key for search engineers, merchandisers, and retailers.

Shaping product relevance in Empathy Platform

Relevance is all about ensuring that when a shopper enters a query in your commerce store, returned results closely meet what the shopper is looking for. It's like keeping a conversation with the commerce search and feeling understood, just like a personal shopper assistant would do in the offline world. The search can understand your shoppers’ needs and preferences by presenting products that not only match the search terms in the query but are also likely to be of your shoppers’ interest.

Product relevance ensures that your commerce search speaks your shoppers' language and guides them to the products they're most likely to love and purchase.

Relevance directly impacts how well Empathy Platform can help and guide your shoppers in finding the most suitable products quickly and efficiently. For example, for the query "running shoes", shoppers don’t expect a large collection of any type of shoes—casual sneakers, indoor trainers, etc. Instead, shoppers want their intent to be understood and get a list of sports shoes specifically designed for running on track, road, and trail, in various styles, brands, and price ranges.

To present the most fitting product results to shoppers, Empathy Platform combines a mix of top-notch AI-driven technology, machine learning mechanisms, search configurations, personalization tools, and sorting options, approaching product relevance from complex-to-simple perspectives and strategies for search engineers, merchandisers, and retailers:

AI search algorithms and shopping behavioral signals

Capacitating search engineers to deliver tailor-made and highly relevant shopping journeys

Integrating AI hybrid search gets the most out of both keyword and semantic matching, ensuring not only precision between queries and results but true alignment with shoppers’ intent. Semantic models and machine learning algorithms are trained to continually adapt to evolving shopper preferences and trends, improving search relevance.

Understand semantic search

Using wisdom of the crowd contextualization methods draws insights from anonymized collective shopping signals and shopper’s behavioral in-session interactions to understand shoppers' intent, ranking highly trending and popular products, commonly bought together items, and contextually related results without compromising data privacy.

Enhance search relevance with Contextualize
AI search relevance

Empathy Platform is in constant innovation, exploring decentralized uses of personal data where data privacy is at the core for shoppers to decide what, how, and when to share or interact with the commerce store. A combination of practices based on local-first, privacy-by-design, domain-specific semantic models, and read-only information helps for a search personalized relevance based on purposeful sharing, rather than invasive tracking-based personalization experiences.

Back office search configurations

Allowing merchandisers to meet business needs while fostering a finely tuned and purposeful shoppers’ experience

Using ranking configuration tools allows merchants to fine-tune ranking criteria, matching shoppers' preferences while meeting business strategy. Adjust the weight of product searchable fields in the product catalogue to dynamically influence product relevance on the search results ranking. So, those products that meet the established criteria get promoted on the search results collection. Ranking criteria range from textual match, business rules, or function score to more specific and complex criteria like semantic matching.

Learn how to weight product ranking criteria

Setting up search results ranking manually with boosting and burying controls allows for pushing products directly to ranking top or bottom positions or softly promoting or demoting their organic position according to product attributes based on inventory levels, promotions, popularity, and other factors alike.

Learn how to boost and bury products and product attributes

Dynamic and manual ranking for merchants

During the fine-tuning process of product relevance, various strategies come into play to influence the position of products. By adjusting the value of certain product attributes with soft boosting or burying controls, the position of products slightly increases or decreases on the results page. Hard boosting or burying the product itself ensures it is placed directly at the top or bottom of the results page, meeting not only shoppers’ preferences but also business strategy.

Interface manual controls

Empowering shoppers with manual ranking controls

Implementing sorting options, such as relevance, price, or customer ratings, enhances shoppers’ command over their experience. By offering a range of flexible filtering options, shoppers can tailor their search results to their precise specifications. These manual controls elevate shoppers' satisfaction by providing a self-managed, personalized approach to navigating and discovering products within the commerce store.

Get the most out of product sorting
Manual frontend configurations for shoppers

When it comes to finding products that match shopping intent, it's not just about product search experiences, but also about having relevant options when browsing the product catalogue. Sorting and filtering tools allow shoppers to control and customize their product discovery experience in your commerce store.

Generative AI and LTR mechanisms

To further enhance commerce search experiences, Empathy Platform is shaping gPlay, a generative AI-driven tool for commerce stores' back offices. This tool combines manual input and automated analysis under the framework of offline evaluations to accurately determine product relevance for specific queries. The search relevance algorithms adapt dynamically by leveraging Learning-to-Rank (LTR) mechanisms that are, in addition, trained with the the data sets obtained from the feedback evaluations.

Check out how to elevate search relevance with AI

Understand search relevance

Managing your commerce store becomes sometimes a tricky task and that is why explainability is one of the main aspects Empathy Platform focuses on. Dig deeper into how products are sorted in the results list depending on the different scoring criteria participating in the overal product scoring.
Interact with the Explain feature to perfectly understand how these scoring criteria are present in each product, compare some of them, and change the configurations if necessary.

Understand why products are scored and ranked in the SERP