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Quick answer: Product discovery optimization involves refining your website’s structure, navigation, and product information for an increasingly AI-powered search landscape. Minimizing user friction and enhancing personalization on your e-commerce site is critical for dominating AI-powered product discovery.
AI-powered shopping is alive and kicking, and it’s already transforming product discovery optimization. While the process isn't new, the explosion of AI tools and shopping assistants has made it ever more important your products may now be appearing in AI summaries, product snippets, comparisons, and shopping assistants.
With your products competing with the competition across an expanding array of touchpoints, your focus should be on turning any extra visibility into real sales.
Learn how investments in AI tools, seamless checkout solutions, and flexible payment options can help usher your product catalog into the agentic AI era.
Table of contents
Product discovery refers to the target customer searching for and identifying relevant products that meet their current needs. Search engines and modern AI web crawlers use product discovery data to understand what a customer wants based on search queries, browsing behavior, click paths, and more.
Product optimization is the process of enhancing your website’s product catalog, search features, navigation, and product recommendations to maximize engagement, conversions, and retention.
While product discovery is linked to user intent and product relevance, product optimization focuses on enhancing user experience through product page optimization, ultimately helping to increase e-commerce conversions.
Product discovery | Product optimization | |
|---|---|---|
Goal | Understand what the user is looking for and surface the most relevant products | Enhance page performance by maximizing engagement, conversion, and retention |
Focus | Mapping user intent, product attributes, semantics, and matching products to queries | Improving existing experiences by tweaking UX, reducing friction, and improving conversion signals |
Who owns it | Product managers, merchandising experts, and UX researchers | Data analysts, Conversion Rate Optimization (CRO) leads, and growth leads |
Search queries, clickstream patterns, filters used, product metadata, product images, and taxonomy | Search queries, clickstream patterns, filters used, product metadata, product images, and taxonomy | CTR, add-to-cart rates, bounce rate, session duration, A/B test results, return rates, and satisfaction metrics |
Outputs | Improved search relevance, better product matching, refined filtering, and improved navigation paths | More relevant ranking, demoted poor performers, and optimized layouts |
How AI systems use each | Intent recognition, query expansion, embedding-based similarity, and understanding which products should appear at all | Predict conversion likelihood, rank by performance, quality, and user engagement |
Optimizing your product catalog for AI discovery is crucial for success in this crowded marketplace. Download the AI Product Discovery Checklist to start implementing a product optimization strategy today.
Instead of scrambling to tweak every element of your e-commerce site for AI product discovery, start with a clear goal, shared taxonomy, and a single source of truth for all your product data.
Without these foundations in place, even the best product optimization techniques will be diluted by redundant updates, misaligned priorities, and inconsistent signals to AI systems.
Treat AI readiness like a product management exercise. Set a single and shared objective, agree on a uniform taxonomy, map the responsibilities, and define what “good” looks like before making sweeping changes to your store.
A good starting goal could be to increase product visibility in AI summaries, increasing the search-to-cart rate by X%, or reducing the checkout time by Y%. Whatever it is, a single outcome will help improve your team’s focus and experiments toward a clear, uncomplicated objective.
What is not tracked is not measured. You should be aware of every channel that feeds product data into AI crawlers and shopping assistants:
Your product categories, variants, and all associated naming conventions should speak the same language. Without it, AI tools will get conflicting signals when crawling your website, marketplace pages, and other channels.
By ensuring a master database of product information, any updates you make will automatically reflect correctly on every channel. PayPal store sync, a new feature that makes your products discoverable on leading AI tools like Perplexity, helps avoid confusion caused by inconsistent product information.
It’s best to rewrite a few product titles or descriptions, or update a handful of filters, before going all out on your entire website navigation, search, and content features. This allows you to test and scale only the things that improve product discoverability without wasting resources indiscriminately.
Customer feedback often provides actionable insights via chats, support queries, reviews, and other interactions. Prioritize burning pain points when making content updates to address shopper dissatisfaction, tackle missing product information or confusing categories, and improve AI interpretation.
Data hygiene is one of the most impactful product discovery tools, since AI models are only as good as the data they’re fed. If your product titles, identifiers, descriptions, or pricing fields aren’t clean, consistent, and AI-friendly, they’ll directly hurt AI product visibility and conversions.
Removing duplicate and outdated listings, standardizing product IDs, and infusing product descriptions with natural language to signal intent might sound basic, but they’re critical to a robust product optimization strategy.
Your stock keeping unit (SKU), global trade item number (GTIN), and variant taxonomy should be standardized across product information channels to ensure consistent signals to AI shopping assistants and generative AI shopping tools.
Global identifiers that don’t change with the channel in question work best, since they also help bring standardization across AI shopping surfaces.
Consistency shouldn’t be limited to your product identifiers and categories. Your product titles must follow a clear pattern so that AI crawlers don’t encounter ambiguity when ranking your product pages or featuring them in AI summaries.
Just like any other website, your online store needs to have schema markup to help search engines and AI crawlers understand the products, availability status, ratings, and breadcrumbs on your website. It allows AI search results to better display rich content from your website, improving visibility and click-through rates.
A clean and consistent catalog keeps things unambiguous, but you first need to get rid of duplicate or expired listings that can confuse AI models. Consolidate listings to concentrate conversion signals for AI tools, and improve ranking signals for your products.
Your customers won’t always search for the exact product titles you have on your site, no matter how relevant you might think they are. Enrich your product catalog with natural phrases and product synonyms that customers search for, so that AI models can map colloquial queries to the right products.
Some examples of natural-language queries are:
You might see better intent signals by starting product descriptions with the main pain point, such as “best ergonomic chair for lower back pain”. AI tools prioritize intent-focused text when matching products to user queries.
You should automate prices and stock numbers to reflect the real-time status for AI crawlers, as stale information can hurt both the customer experience and AI product discovery. All connected channels should receive updated and consistent product information.
Once your data foundation is clean, consistent, and ready for AI, the next step is to focus on your on-site experience. The navigation, search, filtering, and product information elements on your site are not only important for transforming the customer experience, but also make it easier for AI tools to find your products.
Your site navigation should be decluttered and as simple as possible to avoid confusing both customers and AI crawlers. Next, search should be context-aware, fast, and visual, suggesting products based on user queries and autocorrecting typos to avoid “no results”.
The filters should be limited, meaningful, and they should change the search results in real-time. Your product listings should also be scannable and comparison-ready, so they can be featured in AI summaries.
One of the keys to smooth and simple navigation is limiting your catalog to 1–3 levels or sub-categories. For fewer than 100 products, 1-2 layers are enough, and for up to 1,000 products, 3 layers should be the limit.
This helps your customers understand the site structure quickly, avoids friction, and minimizes bounce rates. AI crawlers, on the other hand, can map relationships quickly, strengthening relevance signals.
Having multiple categories with overlapping products is unavoidable when your products are mapped to multiple use cases. However, try to consolidate redundant categories that confuse shoppers and AI tools. Streamlined categories avoid relevance signals from getting split up across multiple pages and help provide a clear intent to AI crawlers.
It’s good practice to display the site path to help the user understand where they are in terms of the hierarchy of the site (e.g., Home > Men’s Apparel > Footwear > Indoor Shoes). This aids customer experience as well as AI product discovery by helping search engines correctly map where your products sit. It increases the chances of AI crawlers displaying your product pages in AI summaries and snippets.
Making the active menu sticky can help prevent your customers from feeling lost as they browse your store. A sticky header isn’t just a nice-to-have for the shopper, but also important for them to get their bearings and feel in control of the browsing experience. It also can help reduce the chances of abandoned sessions.
Early suggestions, even before the user finishes typing the search query, help guide them to popular or relevant products. It minimizes the chances of “dead-end” queries with no search results.
Plus, you inform AI crawlers about the most commonly searched terms on your site. For instance, auto-suggestions for a “desk” query can be:
Besides auto-suggest, you should also incorporate autocorrect for identifying what users are looking for, even when they make typos. This is important to avoid “no results” in search queries and help users discover the products they want. Moreover, AI models learn to disregard misspelled queries in favor of autocorrected search terms.
Don’t wait for the user to finish typing to display the search results. Allow your site to show relevant products dynamically as the user is typing and modifying the query every instant. This is one of the most impactful product discovery tools since it speeds up the process and enhances the user experience.
Visual search can feel like a bonus, especially to your millennial and Gen Z audiences. Allowing shoppers to upload an image or tap one of the displayed images to find similar products can boost their browsing experience. AI systems also use this behavior to map images to products.
Modern shoppers expect to see relevant products even when typing concerns or requirements like “dry skin,” “broad toe,” or “combo under $100.” Tagging your products with natural language inputs will enhance user experience and allow AI models to connect lifestyle goals with specific products.
Not all filters will apply to every search query. For instance, attributes like “lace” and “slip-on” have no business being in a user query for slim-fit jeans. Try to limit the list of filters to relevant attributes and avoid overcrowding to prevent decision fatigue, helping users reach purchase decisions quickly.
Users don’t think in one dimension. They have multiple criteria in mind when arriving on your site, and allowing parallel filters helps support that behavior. Multiple filters help shoppers refine search results effectively, signaling to AI crawlers which combinations drive conversions.
It sounds simple, but it isn’t as universal as you’d think. Before your customer clicks “Apply” after choosing their preferred filters, they should see how many products match each filter. As they select one filter after another, the product counts should keep updating to reflect the narrowing selection.
You can go a step further and update the search results instantaneously as the user checks and unchecks their preferred filters. This reduces friction by eliminating the wait time, and it also allows AI models to understand which attributes shoppers value the most.
Besides basic sorting criteria, add bestselling, top-rated, and seasonal picks where relevant. Personalizing the sorting options for repeat visitors by adding recommended picks or repositioning sorters based on past behavior will enhance the buyer experience.
Optimizing your site’s navigation, search, and filtering options helps visitors have a fulfilling browsing experience. What keeps them coming back is how well you personalize their experience using recommendations and recently viewed products.
It makes them feel valued, avoids rework on their part, and maximizes the probability of conversions. Moreover, personalized recommendations and scannable, comparison-friendly content not only make the shopper’s job easier, but also help AI surfaces easily scan information and feature it in summaries and snippets.
You can display personalized recommendations for shoppers based on multiple inputs:
Customers may be more likely to complete purchases if your store recommends products that closely fit their search behavior and purchase history. You can also use AI personalization to provide real-time recommendations during the product discovery phase by leveraging PayPal’s Shopping Personalization Platform which has 80M shopper profiles enabled for shopping personalization in the US.1
No one likes to waste time shopping online, especially younger segments like millennials and Gen Z. Displaying recently browsed products on the PDPs and cart pages will help shoppers pick up where they left off, increase the chances of conversion, and send strong repeat interaction signals to AI models.
Additionally, keeping your checkout screen decluttered is one of the best mobile optimization strategies for your digital-native customers.
Just like your on-site search, your product collections ought to reflect consumer concerns and lifestyle needs, such as “travel essentials,” “sleep support,” and “gifts for mom.” These collections will make it easy for AI crawlers and agentic AI to map your products to consumers’ lifestyle queries.
PayPal agentic AI and APIs are helping to make this happen through our 438 million active consumer and merchant accounts.2 Our unified omnichannel profiles further position us to provide useful recommendations to your customers across the entire shopping journey.
Social proof is as relevant as ever to building user trust in your products, especially for new shoppers. Display short, high-quality review snippets and user photos to ease purchase hesitation. These authentic reviews also give concrete signals to AI crawlers to better feature your products in search results.
Your site doesn’t have to be limited to static landing pages. Create new landing pages around seasonal demand and specific gifting moments with curated selections. PayPal agentic AI and APIs use these seasonal pages to tag your products to timely intent queries from consumers.
AI models prefer fresh and accurate information when ranking your pages over time. Stale content loses weight as AI surfaces prioritize the competition. Regular review and maintenance help you avoid “no results” queries and refresh product and pricing information in time for seasonal spikes.
Listening to customer search data, support queries, and return reasons will help you prioritize content updates. And tracking sales KPIs will inform you on how well your updates are working.
Monitoring your on-site search queries reveals the gaps in your catalog. Weekly reviews will uncover missing synonyms, product gaps, and ambiguous descriptions based on real shopper behavior. Fixing them will help deliver cleaner intent signals to AI systems.
“No results” queries are failures for both the customer experience and AI product discovery. Adding missing synonyms and redirects will improve product discoverability for consumers and reduce negative signals that AI crawlers could interpret as a subpar catalog.
Updating your product pricing, availability, and structured data well ahead of seasonal demand shifts gives you a competitive edge. It helps maximize visibility and prepares your site to send cleaner signals to AI models to rank your products.
Product discovery optimization is a never-ending exercise. However, it begins with aligning your team around shared AI product discovery goals and refining your product data and site experience for both customers and AI agents.
PayPal’s agentic AI may help you improve product discoverability by seamlessly exposing your products to AI surfaces, fast-tracking conversions. Thanks to our worldwide network of shoppers and merchants and consumer trust in our systems, PayPal may help convert your AI visibility into a seamless checkout.