On an e-commerce site, two visitors can type exactly the same query into the search bar without having the same expectations.
A customer searching for “running shoes” may be looking for a first pair to take up running. Another may be an experienced runner looking for a high-performance model. A third may prioritize price, while a fourth will attach more importance to the brand.
The query is identical. The purchase intent is not. It is precisely this difference that underpins the evolution of Product Discovery in e-commerce.
For a long time, the goal of an internal e-commerce search engine was relatively simple: match the words typed by a shopper with the products available in a catalog. Today, this approach is reaching its limits. Consumers expect a more relevant, more contextualized experience that is able to adapt to their needs.
Personalization is thus becoming one of the pillars of a high-performing e-commerce Product Discovery strategy.
Product Discovery refers to all the mechanisms that enable a consumer to discover, find and select the products most relevant to their needs. It therefore goes further than simple product search.
A traditional e-commerce search engine mainly responds to a query: the user types keywords and the engine returns a list of products matching those terms.
Product Discovery adds an essential dimension: understanding the need behind the search and making it easier to discover the products likely to meet it.
In particular, it can rely on: keyword search, semantic search, natural language understanding, navigation and filters, product ranking, recommendations, browsing context, real-time behavior, experience personalization…
The goal is therefore no longer simply to answer the question “which products match this query?”, but rather: “Which products are the most relevant for this visitor, at this precise moment?”. This distinction is fundamental for online retailers who want to turn their internal search into a real conversion driver.
Product search and Product Discovery are closely linked, but they do not pursue exactly the same goal.
Search generally responds to an explicit intent: “I know what I’m looking for. Help me find it.” Product Discovery also comes into play when the intent is less precise: “I know what I need, but I don’t know exactly which product to choose.”
Take the example of a consumer who types: “lightweight hiking jacket”. A traditional approach will look for products whose catalog data contains the terms “jacket”, “lightweight” or “hiking”. A Product Discovery-oriented approach can go further by taking into account the semantics of the query, but also the visitor’s preferences and context, to determine which products should appear first. Product Discovery thus turns search into a guided discovery experience. This evolution is important: depending on the context, the same product can be extremely relevant to one visitor and much less so to another.
A product’s relevance is not always objective. It depends on the consumer, their intent, their history, their behavior and the context in which they are searching. That is why two visitors using exactly the same keywords should not necessarily get exactly the same results. The same query can hide several intents.
Take, for example, the search: “3-seater sofa”. For a first visitor, the priority criteria may be price and immediate availability. For a second, comfort and material quality. For a third, contemporary style. For a fourth, a previously expressed preference for a particular brand.
If the engine displays exactly the same ranking to everyone, it implicitly assumes that all visitors have the same priorities. Yet this is not the case. Personalization makes it possible to move from average relevance to individual relevance.
This is probably one of the most significant shifts in e-commerce search. A traditional engine works mainly by matching a query against a catalog.
A modern Product Discovery approach seeks to understand more signals: the words used, the meaning of the query, the products viewed, the categories visited, the filters applied, clicks, add-to-carts, previous searches, interactions during the session…
These signals gradually make it possible to better understand what the visitor is likely to be looking for.
For example, a user views several pairs of trail running shoes, filters on a certain price range, then searches for “men’s shoes”. Taken in isolation, the term “men’s shoes” is particularly broad. But placed back in the context of their session, it can become much more precise. Context gives meaning to the query. This is precisely what an intent-based approach makes possible: treating each search not as an isolated action, but as a step in a purchase journey.
Personalization can come into play at several levels of the experience.
The first level consists of adapting product ranking. Two visitors search for “men’s sneakers”.
For one, athletic models may take priority. For the other, lifestyle models. A personalized ranking makes it possible to adapt the display order based on the available signals. The principle is simple: The right product is not just the one that matches the query. It is the one most likely to be relevant for this visitor. Ranking then becomes an essential component of Product Discovery. It is no longer just about ordering products according to generic logic, but about building a hierarchy tailored to the visitor’s context and affinities.
Product recommendation is another pillar of Product Discovery. But not all recommendations are equal. Systematically displaying: “Customers also bought…” can be useful, but it remains a relatively generic approach. A truly contextualized recommendation seeks to answer a more specific question: “Which product would be relevant for this customer, given what they have just done?”
Recommendation can then come into play after a search, on a product page, in the cart or at different moments of the journey. It becomes a way of guiding consumers through their discovery rather than a simple upselling tool.
Personalization can also begin even before the results are displayed.
From the very first letters typed, suggestions can be adapted to the context and the intents detected. Rather than only offering the most popular queries across all visitors, a personalized experience can favor the expressions most likely to match the visitor’s needs. This approach reduces the effort required to reach the right product.
Product Discovery does not stop at the search bar. Categories, filters and product listing pages are also important touchpoints. The same catalog can be presented differently depending on the context and the preferences detected.
In particular, product ranking can take into account the behaviors observed during the session in order to surface the most relevant items for each visitor.
This is the logic that Sensefuel applies to search as well as to product listing pages, with ranking capable of adapting to session context, history and real-time behavior.
Manual personalization quickly reaches its limits. In a catalog of a few hundred products, an e-merchandising team can still define relatively precise rules.
But how do you manage tens of thousands of SKUs, thousands of different queries and constantly changing behaviors? This is where artificial intelligence brings a new dimension to Product Discovery.
An AI dedicated to commerce can analyze numerous signals simultaneously and learn from the interactions that take place on the site.
It can thus gradually identify: the products that interest each visitor, affinities between products, behaviors associated with purchase intent, the products that convert best in certain contexts and changes in behavior over the course of a session.
The challenge is therefore not simply to use AI to understand the words typed. It is to use it to understand behaviors and adapt the experience accordingly.
At Sensefuel, AI analyzes users’ interactions with the site in order to adapt the search and product discovery experience in real time. The platform notably combines search, recommendation and ranking to individualize journeys.
Personalization does not necessarily have to depend on a long customer history. An unknown visitor can already send many signals during their session. They view a category, open several product pages, apply a filter, run a search, compare several items…
Each of these actions provides additional information about their intent. Real-time Product Discovery consists precisely in leveraging these signals as they emerge. The visitor’s profile therefore evolves as they browse.
This point is particularly important for new visitors or logged-out users: the experience can start to be personalized even before the online retailer has a significant purchase history. Sensefuel notably highlights this “real-time profiling” logic, with a product affinity profile built in real time for each visitor.
Personalizing the experience does not mean abandoning the online retailer’s commercial objectives.
Quite the opposite. One of the challenges of an effective Product Discovery strategy is to strike the right balance between relevance for the customer and commercial performance.
For example, an online retailer may want to: promote a strategic brand, showcase a new collection, speed up stock clearance, highlight certain high-margin items, promote a seasonal range, maintain the relevance of results despite these commercial objectives…
AI can then automate part of the personalization while leaving e-commerce and e-merchandising teams free to define their priorities. This combination of automation and business control is an important element of the model offered by Sensefuel.
A personalized search experience can have an impact at several levels of the purchase journey.
More relevant search
When the most relevant products appear more quickly, consumers spend less time browsing through results that do not match their needs.
Easier product discovery
Personalization makes it possible to surface products that the visitor would not necessarily have found on their own. It can thus turn a specific search into the discovery of complementary, alternative or better-suited products.
Reduced friction
The more searches, filters and pages a visitor has to go through to find what they are looking for, the more complex the journey becomes. A contextualized experience can reduce this friction.
Better conversion
Better relevance can also help improve the commercial performance of the search engine.
Sensefuel notably presents search as a central point of the purchase journey and highlights the goal of turning the intents expressed by visitors into conversion opportunities.
Personalization should not be seen as a standalone feature. It relies on several complementary building blocks.
1. Understand intent
The first step is to better interpret user searches. Natural language, synonyms, typos and complex queries must be understood so that relevance is not limited to simple word matching.
2. Collect the right signals
Navigation, searches, clicks, filters and interactions provide valuable information about intent. The goal is to turn these signals into actionable insights.
3. Adapt ranking
Results must be able to evolve based on the context and the visitor’s behavioral profile.
4. Personalize recommendations
Recommendations must be contextualized and come into play at the moment they can genuinely help the consumer.
5. Keep merchandising control
Automation should not eliminate the role of e-commerce teams. On the contrary, it should allow them to focus on strategy, while automating tuning and optimization tasks at scale.
The nature of e-commerce search is changing. For a long time, the search engine was essentially seen as a tool for finding a product in a catalog. The new generation of Product Discovery pursues a more ambitious goal: understanding each visitor in order to present them with a relevant selection at the moment they need it. This evolution is driven by several technologies: AI, semantic search, dynamic ranking, recommendations and behavioral analysis. But beyond the technology, the change is above all a matter of perspective. It is no longer about asking: “Which products match this query?” but: “Which products are the most relevant for this person, in this specific context?” It is this evolution that is gradually turning e-commerce search into a true discovery experience.
Not all consumers browse the same way, and not all have the same expectations. A single catalog can therefore hardly deliver an optimal experience if it is presented identically to everyone.
Personalization brings the digital experience closer to what happens in a physical store: a good salesperson observes their customer, gradually understands their expectations and adapts their suggestions.
The ambition of Product Discovery is to reproduce this ability at scale, with the help of artificial intelligence. For online retailers, the challenge is therefore no longer just to have a high-performing search engine. It is to build an experience capable of understanding, guiding, personalizing and converting. This is precisely the vision Sensefuel stands for: an AI platform that combines search, product discovery, recommendation and ranking to adapt the shopping experience to each visitor’s intents and behaviors.
Product Discovery is no longer just about helping a customer find a product. It is about helping them discover the right product, at the right time, in the right context.