Not long ago, online shopping followed a predictable rhythm: a customer searched, browsed a few products, and checked out — often without leaving a single site. Over the years, that path has splintered, fragmented by an increasing number of channels, algorithms, and AI-assisted shopping behaviors that redefine where discovery begins. A single purchase journey might start with a TikTok video, detour through Amazon, continue on an LLM, and end on a retailer’s site. Each hand-off heightens shoppers’ expectations for relevance and personalization.
The problem is, many retailers still view product discovery as a static moment — something that happens once, on one channel — rather than an ongoing journey that needs to be supported and connected across every touchpoint.
This guide clarifies what modern retail product discovery is and why it matters now. We break the journey into three overlapping stages (Define, Refine, and Decide) and show how to meet intent at each step with AI-powered tools.
You’ll also learn six practical ways to improve discovery and a quick primer on three technologies reshaping the field in 2026, including agentic AI and LLMs. When shoppers feel understood, discovery doesn’t just drive conversions — it builds connection.
Retail product discovery encompasses the entire experience of helping customers find relevant and desirable products, both online and offline.
Our 2025 State of Ecommerce report uncovered that today’s ecommerce path is no longer linear, with shoppers jumping between channels as they like.
This is where product discovery comes into play.
The right solutions can help retailers meet shoppers in as many places (channels) as possible with a tailored, connected experience. They help anticipate intent, respond to preferences, and reduce friction with every step. (On the flipside, outdated solutions can inadvertently create drop-off points that kill conversions. More on this later!)
For now, to understand how the shopping journey is evolving, we’ve separated the phases into three (increasingly) overlapping categories:
Enterprise retailers who meet intent at Define, reduce friction at Refine, and remove doubt at Decide experience measurable increases in conversions, RPV, and return visits.
Let’s dive into each stage below.
At the earliest stage, shoppers show up with a mission, not a SKU (i.e., “a gift for my young niece,” “replace my carry‑on,” “something for flat feet”).
As a result, entry points are fragmented. Many begin on Google or Amazon, while others start on retailer sites, and a fast-growing share begin on TikTok or with LLMs like ChatGPT, Gemini, or Copilot.
The key reality: product discovery often starts offsite, but the expectations formed there get carried onsite — and shoppers defect quickly if that handoff breaks. They expect your experience to recognize the intent they just expressed, not make them start over.
Engaging potential customers early in the discovery process is crucial for improving product relevance and increasing brand visibility. Understanding users' behaviors and preferences at this stage allows businesses to tailor the discovery experience, making it more effective and personalized.
The following combination of on- and offsite solutions can help businesses meet the moment:
During this stage, shoppers know what they want (“espresso machines,” “carry‑on suitcases”) and are working on filtering noise and building confidence in their selection.
Hyperpersonalization fits in here, as it deals with presenting shoppers with the best products for them in that specific context. However, our recent findings show there's a persistent personalization gap. Forty-one percent say their favorite retailer still treats them like strangers, and only 20% see frequent personalization.
But shoppers don’t just want mirrors of past behavior. Sixty-three percent say the most important factor is matching the current need, not repeating brands they’ve bought before. Delivering truly relevant results that accurately match user queries is crucial at this stage. Providing genuinely relevant search results not only helps shoppers find what they need faster, but also increases their confidence and satisfaction with the shopping experience.
In the last stage, shoppers are down to the wire when comparing product recommendations. Uncertainties about product specs and how they suit their needs may cause them to drop at the last minute. So, trust and efficiency decide the sale at the last mile.
This is why reviews are the dominant anchor, with 67% of modern shoppers citing them as a top trust signal. Effective decision-making at this stage relies on clear comparisons, ratings, and analysis to help shoppers choose the best product.
At the same time, costs and speed create the most friction: 62% abandon for shipping costs and 51% for long delivery times. And when it comes to the final leg, the checkout process, one‑click and accelerated checkout matter across generations, with 64% saying it’s at least somewhat important.
As we’ve said before, shoppers have high expectations for speed, relevance, and clarity. The sad truth is that most ecommerce sites aren’t meeting them.
Constructor surveyed over 1,500 US and European shoppers and found that:
The good news is that enterprise retailers can (and do) improve business metrics and foster brand loyalty — with the right partners. Those that switch to a suite of AI-powered discovery tools built for ecommerce use cases see measurable gains, including:
In a competitive market, speed and relevance in discovery drive performance across the entire customer journey.
Upgrading discovery doesn’t require starting from scratch. The most effective strategies combine smart technology with human control. As aforementioned in the Define, Refine, Decide framework, here are a few areas to focus on and solutions to consider:
Forget rigid, keyword-matching engines that might not hit the mark every time a customer searches. Today’s enterprise ecommerce leaders are turning to AI-driven search engines that more accurately understand shopper nuance and deliver attractive results
Thanks to large language models (LLMs), transformers, and other advanced AI technologies (more on these below!), these engines understand what shoppers mean, not just what they type. That means customers can use broad or conversational queries, such as “comfortable walking shoes for travel” or “gifts under $50 for tech lovers,” and still receive attractive results.
This global retailer specializing in athletic footwear returns attractive results for the query “comfortable walking shoes for travel,” regardless of the data (or lack thereof) collected on the shopper.
Effective product recommendations are driven by leading AI technologies and clickstream data, dynamically optimizing results to enhance both shopper satisfaction and business performance.
Recommendations need to appear in the right place at the right time to hit KPIs. They should be placed frequently in appropriate locations that align well with the shopper’s journey and intent. This extends beyond the home page to product detail pages, category pages, carts, and more.
But effective recommendations go beyond placement. A comprehensive ecommerce recommendations strategy comprises four foundational building blocks: the right recommendations engine, business objectives, optimal placement, and user experience.
Learn more about the right recommendation strategy in the Constructor Guide to Ecommerce Recommendations.
Forget static product finders that walk shoppers through the same questions every time. Today’s enterprise retailers are pairing guided quizzes with agentic AI to create experiences that feel both structured and personalized.
A retailer of high-quality home products does just this, using question-and-answer flows with free-form input to help shoppers find the perfect coffee or espresso machine.
The quiz captures everything from feature preferences like “milk frothing” or “temperature control” to space available for an espresso set-up, like “not much” or “a lot.” At the end of the quiz, shoppers can type out a conversational request to clarify any other final needs or preferences.
This agentic AI-powered quiz allows consumers to narrow a broad category like ‘espresso machines’ into a tailored set of recommendations that reflect their unique priorities.
Traditional category pages often leave shoppers overwhelmed with too many options and little guidance. Modern enterprise retailers are transforming browse into a personalized journey by dynamically adapting product rankings, filters, and visuals to each shopper’s context.
By drawing on signals like past browsing history, real-time behavior, and even local inventory, browse pages become smarter with every click. Shoppers see items most relevant to their preferences and goals — whether that’s highlighting sustainable products, surfacing complementary items for a past purchase, or tailoring product grids to emphasize styles in their size and price range (this can also apply to sponsored listings).
Home24, a leading ecommerce furniture company, complements organic results with sponsored listings in its category pages, boosting its bottom line without frustrating customers.
The result is a browsing experience that feels curated rather than generic. Instead of scrolling endlessly through hundreds of SKUs, customers find a smaller set of products that resonate with them, boosting engagement, satisfaction, and ultimately conversion.
Combine your raw catalog data and customer behavioral data with Generative AI and machine vision to enrich categories and optimize products for discovery.
Use customer behavioral data to inform the creation of trending attributes and support current search trends, and review and manage enriched attribute and category data within your merchant tools dashboard.
Enterprise retailers are increasingly integrating visual search, which allows shoppers to upload or take a photo and find similar products. It’s especially useful for fashion, home, and lifestyle categories where style and aesthetics matter.
Visual discovery also complements voice or conversational interfaces, enabling a more intuitive, cross-sensory shopping experience.
If you’re looking to create more enjoyable online shopping experiences and drive revenue, check out more product search and discovery tools that are built from the ground up with advanced AI.
Product discovery is rapidly evolving, driven by new interfaces and shopper behaviors. Here are some underlying technologies to watch — more importantly, understand.
Ecommerce AI agents are autonomous digital assistants designed to interpret context, data, and business goals — and then take purposeful action. They continuously learn, adapt, and improve to deliver more effective outcomes (unlike traditional chatbots that depend on scripted responses and rigid decision trees).
Transformers (or the “T” in ChatGPT) are advanced algorithmic models trained on large amounts of ecommerce data. They don’t just model the distances between words in vector space; they can process all words in parallel and consider the complex relationships between search queries and product data.
This makes them useful for processing complex natural language search queries (e.g., “find me hiking boots under $150 that come in women’s size 8”), leading to more accurate and attractive search results for the user.
Large language models (LLMs) use transformer architectures. They’re a category of AI models that have also been trained on massive amounts of text data to perform tasks related to natural language understanding and generation.
This creates a model that can mimic what people really want in a given situation. For example, the model can start to understand that when someone searches for “pants” in the U.S., they mean trousers, but when they search for it in the UK, they may mean underwear.
They also have other applications in the product discovery space, including filtering out irrelevant results.
To learn more about how emerging AI technologies impact the ecommerce landscape and — ultimately — how they can have an undeniable impact on your company’s bottom line, check out our guide, Forward. Fast. The Future of AI in Product Discovery.
Enterprise retailers can’t afford to treat product discovery as a secondary feature. It’s foundational to the shopping experience and a major revenue driver.
Whether you’re optimizing existing search tools or exploring new ones like agentic AI, the takeaway is clear: modern discovery experiences meet shoppers where they are, adapt to how they think, and quickly connect them with what they want.
With AI-first solutions that combine customer signals, business objectives, and merchandising controls, you can deliver smarter experiences that convert. See how here.
How does product discovery tie into overall customer experience and sales?
Product discovery defines the shopping experience. If customers can’t find what they want easily, they’ll abandon the session — often for a competitor or Amazon. Strong discovery drives satisfaction, loyalty, conversion, and captures valuable first-party data to inform future strategies.
Can product discovery tools be used across multiple channels?
Yes. Modern discovery platforms bridge online and offline signals, enabling features like buy-online-pick-up-in-store (BOPIS) / Click and Collect; store availability and inventory filters; and personalized, cross-channel marketing campaigns (that have been proven to increase sales by 300%+).
Advanced omnichannel discovery ensures customers see attractive options wherever they shop, thanks to its ability to understand user context (like location, season, or past interactions) and proactively surface the most relevant products.
What about conversational commerce? How does it impact product discovery?
Conversational commerce has a long history. These days, the emerging use of chatbots or voice assistants that guide customers to products through dialogue represents a new mode of product discovery. Instead of relying on keywords or filters, shoppers can simply ask questions in natural language (like “What’s a good gift for a coffee lover under $75?” or “Which running shoes work best for flat feet?”).
Modern conversational AI understands the nuances behind these requests, interprets intent, and delivers tailored results in real time. The experience feels less like a search query and more like a conversation with a knowledgeable store associate, reducing friction and creating more confident buying decisions.
What product discovery platforms or solutions are available to enterprise retailers?
Enterprise retailers can choose from a variety of solutions that cover Search and autosuggest, Browse, Recommendations, AI shopping agents, and more. Leading platforms are purpose-built for ecommerce use cases, combining AI with shopper signals and advanced merchandising controls to create the hyper-personalized experiences that drive exponential growth.
What metrics prove the ROI of product discovery?
Key metrics include conversion rate, average order value (AOV), revenue per visitor (RPV), recommendation conversions, and return visits. Enterprise retailers using AI-first discovery tools report measurable lifts across these KPIs, such as a 13% increase in conversions and a 92% increase in recommendation conversions.