Using AI to Improve eCommerce Product Discovery: 9 Neat Ideas

Your customers don’t want to dig through endless pages or guess the right keywords. They want to find the thing. 

Fast.

That’s where AI steps in. Smarter search, chatbots, personalized picks; all working behind the scenes to make shopping effortless.

These 9 product discovery AI tricks will turn browsers into buyers. 

Ready? Let’s go!

1. Use Vector Search to Improve Search Relevance

Nothing kills the vibe faster than a “no results found” page. 

It’s the digital equivalent of walking into a store, asking for something, and getting a shrug in response. Not a great look, especially when you're trying to convert curious visitors into loyal customers.

That’s where vector search comes into play.

Why Keyword-Based Search is (Kind of) Dead

Imagine this: someone types in “lightweight jacket for fall hikes.”

If your site is running on basic keyword search, it’ll look for products with exact matches, such as “lightweight,” “jacket,” “fall,” and “hike.” If your product title is “Autumn Shell Windbreaker,” you might miss that sale.

Keyword search is like an old-school librarian who only pulls books with exact phrases from a catalog. Great in 2005. Painfully rigid today.

How Vector Search is Different (and Smarter)

Vector search uses machine learning to understand the meaning behind the query. 

It turns that sentence into a complex math equation, a vector, and compares it with vectors assigned to your product descriptions, reviews, metadata, and even blog content.

Instead of matching keywords, it matches intent.

Someone types “jacket for windy morning runs,” and your site suggests:

• Your best-selling windbreakers
• A customer-favorite softshell with mesh lining
• A packable layer recommended for trail runners

Even if the word “jacket” never appears in your product name.

Vector search makes your site feel like a personal shopper, not a static list. It keeps your customers discovering instead of dead-ending.

It also works incredibly well when paired with other AI tools:

• Personalized product rankings
• NLP-based recommendations
• Semantic filters for narrowing down options

2. Deploy AI Product Discovery Chatbots

When was the last time you actually enjoyed using a website’s search bar?

Exactly.

Most of us would rather text a friend: “Hey, do you remember that skincare brand you said doesn’t make you break out?”

Now imagine your eCommerce site could be that helpful friend. That’s what an AI-powered product discovery chatbot does. It turns your “meh” site experience into a real-time, personal shopping convo.

Real Talk: People Are Tired of Filters

Think about your own online shopping habits. 

You land on a site. You click “Filters.” Then you get…

• 63 checkboxes
• 9 dropdowns
• A headache

Now imagine instead you could just say: “Show me a lightweight daypack for under $50 that fits a water bottle and has cute colors.”

Boom. The chatbot replies: “You might love this mint green daypack—it’s $42, fits 18L, and our customers say it’s perfect for short hikes.”

No filters. No tabs. Just a human-style chat.

What Makes Product Discovery Bots So Powerful?

Shoppers who interact with a discovery chatbot often:

• Stay longer on-site
• View 2–3x more products
• Convert at higher rates
• Come back (because shopping felt good, not exhausting)

They’re not rule-based or scripted like those clunky “Hi! How can I help you?” bots from 2016.

Today’s AI discovery bots are built on language models that actually understand what people are asking. 

Even if it's vague, messy, or kinda emotional.

Here’s what they do behind the scenes:

• Interpret natural language like “I need something cute for a bachelorette trip”
• Cross-check real-time product inventory
• Pull in product suggestions that match style, budget, use-case—even vibe
• Adjust on the fly based on follow-up questions

They're not just helpful. They sell.

Pro Tip: Train your chatbot using post-purchase surveys.

Most brands train bots on product names, categories, and specs. That’s fine.

But if you start feeding it how customers describe what they bought and why, you’re building a tool that speaks the customer’s language.

So when someone writes “Bought this because it makes my WFH desk feel cozy,” your bot can connect future queries like “cozy desk vibes” to that same product.

3. Build Personalized Category & Collection Pages

Two customers walk into a bookstore. They both head toward the “Fiction” section.

• One beelines for dark psychological thrillers.
• The other? Cozy small-town romances with cinnamon roll protagonists.

Same category. Different tastes.

If your eCommerce site treats them the same, shows them the same top sellers, same filters, same product grid, you’re saying: “Figure it out yourself.”

Why Static Collection Pages Don’t Cut It Anymore

Traditional collection/category pages are like museum exhibits: Organized, logical, but the same for everyone.

That might work if you have 12 products. But once you have 50, 100, or more? 

You need to curate, not just display.

That’s where AI-personalized collection pages come in.

What Does This Type of Personalization Look Like

Let’s say you sell activewear. You’ve got a “Women’s Leggings” collection.

Three different customers click on that category. Here’s what they each see, thanks to personalization:

Rachel, who’s into yoga and comfort:

• Soft, high-rise leggings
• Reviews mentioning “buttery feel”
• “Perfect for lounging or slow flow classes”

Lana, a serious runner:

• Compression leggings
• Sweat-wicking fabrics
• Product titles like “SprintPro Series”

Jessie, the artsy type:

• Colorblock designs
• Bold patterns
• Social proof like “as seen on TikTok”

Same URL. Three totally different vibes. 

All powered by behind-the-scenes AI that’s learning:

• What products did each person click
• What time of day do they shop
• What kind of words do they respond to in reviews
• How long do they linger on product pages

But great personalization uses micro-affinities, such as tiny behavioral signals that add up over time.

Things like:

• Do they prefer products with video reviews?
• Do they scroll more on product pages that mention “minimalist”?
• Do they favor products under $60, even when looking at luxury items?

Pro Tip: Create “Ghost Collections” to Test Hidden Affinities

Set up alternative category pages (not publicly visible) based on themes like:

• “Cottagecore Aesthetic”
• “Travel Essentials”
• “Minimalist Neutrals”

Then use personalization software to pull products from these ghost collections into visible ones, only for shoppers who show signs of matching.

You’ll start to notice patterns like: “People who love neutrals always buy when they see beige and olive in the first row.”

That’s the kind of intel you can’t get from standard analytics.

4. Use Autosuggestion-Based Product Finders

Let’s talk about undecided shoppers.

You know the type.

They land on your homepage, hover over a few categories, maybe search “something cute for vacation”... then they bounce.

Not because they didn’t want to buy. Because they didn’t know what to buy.

Enter: autosuggestion-based product finders, your store’s way of saying, “It’s okay, we’ve got you. Let’s figure it out together.”

Why This Works: Shoppers Want to Be Guided, Not Dumped into a Maze

When someone walks into a physical store and says, “I’m going on a beach trip,” 

A good store associate doesn’t point to 200 products and walk away.

They ask questions:

• “Do you need outfits or gear?”
• “What’s your style - bold, chill, boho?”
• “How hot is it going to be?”
  

And based on your answers, they suggest just the right thing. That’s exactly what an AI-powered product finder does on your website.

How Autosuggestion-Based Product Finders Really Work?

Think of it like a quiz… but smarter.

It suggests filters and options in real time, based on how the shopper responds.

Let’s say someone starts typing “gift for…”

Your product finder jumps in with:

• “A close friend?”
• “Your partner?”
• “Someone who’s hard to shop for?”

Then it might ask:

• “What’s your budget?”
• “What’s their vibe - funny, cozy, practical?”

Now it’s not just search. It’s a conversation that gently leads to conversion.

Pro Tip: Let Your Product Finder Learn from What People Don’t Choose

Most people use product finders to surface the right product.

But here’s the secret: The wrong answers are even more valuable.

If your customers consistently say “No” to certain suggestions or filters, that’s intel gold:

• Maybe your $90 serum is being shown to bargain shoppers
• Maybe people hate citrus scents, and you didn’t realize
• Maybe 70% of shoppers want 3-step skincare, not 12-step routines

Use that data to tweak not just product positioning, but inventory, pricing, and copy.

5. Leverage Behavioral Data for Dynamic Recommendations

Alright, real talk.

We’ve all had that moment where you buy a toaster, and the website starts aggressively recommending… more toasters.

You’re like, “I just bought one. Do you think I’m starting a toaster collection?”

That’s what happens when a store uses dumb recommendations, aka generic “People also bought” widgets that don’t take your behavior into account.

But with AI and behavioral data? You can be creepy good without being creepy.

Your Customer Leaves Clues. AI Picks Them Up.

Every scroll, click, pause, hover, cart addition - it’s all a signal.

But most stores only act on one thing: a sale.

Huge mistake.

Because a person’s journey to buying is full of gold:

• Browsing soft fabrics but never clicking on rough textures? Noted.
• Always pausing on products under $60 but never adding $80 ones? Noted.
• Clicking on product reviews but ignoring specs? Noted.

AI uses this micro-behavior data to dynamically shift recommendations in real time.

It’s not just “You liked this, so try that.”

It’s more like: “You spent 22 seconds on this candle that says ‘notes of fig and sandalwood,’ so here are three other things with cozy, earthy vibes you didn’t even know you wanted.”

Story Time: The Pajama Cross-Sell That Made the Sale

One of our clients sells loungewear. 

They had this visitor who:

• Clicked on a cotton sleep shirt
• Then hovered on 3 robe options
• Added a fuzzy pair of socks to cart
• Left, came back 4 hours later

Instead of spamming them with “You left socks in your cart!”

The site gently suggested: “Bundle your faves & save 15%! Pajama Night In Set just for you.”

That person didn’t just check out. They added slippers and wrote a glowing review the next day.

That’s the power of behavior-based nudges.

Not pushy. Not random. Just… intuitive.

Pro Tip: Track What They Avoid, Not Just What They Click

Most stores only look at clicks. But what your customer doesn’t click is just as important.

For example:

• They view 5 red dresses, but always back out before adding to the cart? Maybe red’s not it.
• They click when “handmade” is in the title, but skip over anything labeled “imported”? Flag that.
  

Use this data to:

• Refine your recommendation logic
• Rewrite product copy to match their values
• Dynamically remove low-affinity items from homepages & category pages
  

Basically: stop recommending stuff they’ve already said no to, even silently.

6. Minimize Zero-Search Results with Intelligent Fallbacks

A customer types in “minimalist bamboo desk lamp” and hits enter.

And your site says: “No results found.”

Oof. That's the digital equivalent of walking into a store, asking for help, and getting a blank stare.

Let’s be real: Nothing makes people bounce faster than a dead-end search result.

And it’s totally avoidable if your site uses intelligent fallbacks.

What Are Intelligent Fallbacks?

Think of them as your site’s polite way of saying,

“Hey, we don’t have exactly that… but here are some things you might like.”

Instead of returning zero results and ghosting the shopper, AI steps in to:

• Autocomplete and reframe the query in real-time
• Suggest semantically related items (even if the words don’t match)
• Surface trending, best-selling, or thematically similar products

It’s not just about avoiding the awkward “nothing here” moment.

It’s about guiding the shopper gracefully.

How Do Intelligent Fallbacks Really Work? 

A shopper once searched a client’s kitchen store for “matcha spoon.” Nothing came up.

But they did sell:

• Bamboo whisks
• Ceramic scoops
• Matcha kits

The shopper would’ve left…

But the AI fallback caught the word “matcha,” matched it semantically with tea accessories, and gently nudged: “Looking for tea tools? Try these top-rated matcha kits.”

That customer ended up spending $68. A “no results” page would've lost the whole sale.

Pro Tip: Use AI-Generated Queries as Soft Suggestions

Here’s something most stores don’t do -

When someone searches and finds nothing, offer a line that says:

“Not finding what you’re looking for? Try these related searches:”

Then show 3-4 AI-generated alternatives based on their original query.

For example:

Original search: “vegan leather loafers under $50”

Smart fallbacks:

• “faux leather slip-ons”
• “budget-friendly vegan shoes”
• “affordable non-leather flats”
  

Even better? Include 1-2 trending terms in the same category to gently pull them into discovery mode.

7. Create AI-Driven Merchandising Rules

You know those days when you just know which products to push?

Maybe you’re sitting on too much inventory.
Maybe your supplier just bumped margins.
Maybe it’s 93°F and you’re still featuring flannel.

You’re juggling priorities like:

• “We need to move this stock fast.”
• “This product has our best margin.”
• “This one’s seasonal gold.”
• “But the algorithm keeps pushing that random low-margin item.”

That’s where AI-driven merchandising rules come in:

You blend your human logic with the machine’s data smarts.

And suddenly, your store knows what to show, when to show it, and why.

What Does AI-Driven Merchandising Actually Mean?

AI-driven merchandising means you can set conditions,  like “always prioritize X if Y is true”, while letting AI handle the micro-decisions.

Let’s break it down:

• You set the goals → “Push high-margin items” or “Clear winter inventory”
• AI reads the signals → Customer intent, trending searches, geo-location, stock levels
• The site adapts in real time → Personalized product order, featured collections, homepage layouts

It’s like you being the creative director, and AI being the world’s most efficient visual merchandiser.

How Does AI-Driven Merchandising Work?

A home goods client had a popular throw pillow that everyone loved.

But it had low margins. Meanwhile, a similar pillow (fewer clicks, better margins) was collecting dust.

They blended merchandising rules:

• Prioritize the higher-margin pillow
• But if someone showed a strong interest in earthy tones or organic cotton → show the fan-favorite
• On mobile, lead with the one with better reviews
• During weekend traffic? Push bundles

Result?

Better margins, fewer stockouts, happier customers.

The AI didn’t take over; it took direction.

Pro Tip: Use Merchandising Rules to Solve for Operational Realities

AI isn't just about making customers happy; it can also help your warehouse, finance team, and buyer.

Here’s how:

• Got overstock in one region? Tell AI to push those SKUs to shoppers in that ZIP code.
• Need to hit revenue goals before month-end? Prioritize products with better AOV.
• Want to reduce returns? Demote products with high return rates for first-time buyers.

These are the things AI can do, but only if you set the rules.

8. Enable Visual Search Capabilities

Ever tried to describe a boho macrame wall hanging with gold accents in a search bar?

Good luck spelling “macrame” on the first try.

Your shoppers feel that frustration daily, especially in visual-heavy verticals like:

• Fashion
• Home décor
• Jewelry
• Beauty
• Outdoor gear (“that teal foldable camping chair with mesh sides” isn’t exactly keyword gold)

That’s where visual search comes in.

What’s Visual Search, Really?

Let customers upload a photo, maybe from Pinterest, maybe from real life.

Then your site shows them visually similar products instantly.

Think of it like: Ctrl+F for the real world.

The AI maps shapes, colors, textures, and patterns in the uploaded image and compares them to your product catalog in real-time.

And boom: Your site becomes a discovery engine for people who don’t know what to type—but know exactly what they want.

The “I Saw It on Instagram” Moment

A customer sees a cozy linen jumpsuit on Instagram.

No brand tag. No idea where to find it.

They upload it to your site’s visual search tool.

Your AI surfaces three similar styles in under five seconds, two of which are on sale.

That’s not just product discovery. That’s conversion gold.

Pro Tip: Use Visual Search as a Silent Loyalty Driver

Here’s the twist nobody talks about:

Visual search reduces search fatigue and builds loyalty quietly.

When your store “just gets it” without the shopper needing to explain themselves, it feels like magic.

"I don’t need to guess the right keyword. I just uploaded and boom, options I love."

That feeling of effortlessness? That’s what keeps people coming back.

9. Continuously Improve with Feedback Loops

Here’s the truth most eCommerce owners eventually face:

Even your best AI features, smart search, dynamic recs, and slick chatbots can miss the mark.

The good news?

Your shoppers are telling you exactly how to fix it. You just need to listen.

And by “listen,” we mean build smart feedback loops.

What’s an AI-Powered Feedback Loop, Anyway?

Think of it as your store’s version of:

“Hey, how’d we do?” but without annoying popups.

You quietly track what shoppers do:

• Where they click (and where they don’t)
• What they search for but can’t find
• When they bounce mid-discovery
• What filters they repeatedly use (or avoid)

Then, you use AI to interpret that behavior not just as data points, but as nudges for improvement.

How does an AI-Powered Feedback Loop Work?

Let’s say your category page has 12 filters.

But your AI notices that 80% of shoppers only interact with 3 of them.

Worse, some filters increase bounce rates.

A smart feedback loop notices the pattern, alerts you, and even suggests changes:

• Promote the high-performing filters
• Hide or reword the weak ones
• Reorder filters to match user behavior patterns

That’s not a guess. That’s evidence-based optimization.

Pro Tip: Use Silent Signals, Not Just Surveys

Don’t rely solely on reviews or CSAT forms.

Most shoppers won’t fill them out.

Instead, train your AI to learn from:

• Rage clicks
• Scroll speed
• Time spent on product vs. bouncing
• Add-to-cart behavior without checkout
• How often shoppers refine a search (that’s a sign your search failed initially)
  

Let the friction points guide your next update.

How to Get Started with AI Product Discovery

Okay, so maybe you’re thinking:

“This sounds amazing… but where the heck do I start? I sell candles. Or sneakers. Or pickleball gear. I’m not OpenAI.”

Let’s start with basics: 

Step 1: Audit What You’ve Already Got

Before adding fancy AI layers, look at your existing setup:

• Is your current search showing relevant results?
• Are customers bouncing from certain category pages?
• Are your filters actually helpful or just cosmetic?
• Where are people getting stuck?

This isn’t about judgment. It’s about knowing where AI can give you the most bang for your buck.

Step 2: Start with One AI Use Case

You don’t need to do all 10 strategies on Day 1.

Pick one from this list that solves a real problem you’ve noticed.

Here’s a cheat sheet:

• Getting a lot of “no results”? 👉 Try vector search or intelligent fallbacks
• People can’t find what they want? 👉 Try a product finder or AI chatbot
• Bounce rate on category pages? 👉 Try personalized collections
• You’re drowning in dead inventory? 👉 Use AI merchandising rules

Step 3: Use No-Code or Low-Code Tools

There are so many plug-and-play AI tools today that don’t require dev time.

Look for:

• Shopify apps or BigCommerce integrations with AI layers
• Headless CMS tools that support personalization
• Chatbot builders like Tidio, Heyday, or even Meta’s AI integrations
• Product recommendation engines like Clerk, Nosto, or Rebuy

Test them in a low-stakes environment (like a sandbox or collection page) before rolling out sitewide.

Step 4: Run a 30-Day Pilot, Not a Full Launch

Treat your first AI feature like a test kitchen.

You’re not opening a Michelin-starred restaurant yet; you’re just taste-testing recipes.

Set simple KPIs:

• Did search conversions go up?
• Are people staying longer on product pages?
• Is cart size increasing with AI recs?

If it works, double down. If it flops, tweak it. Either way, you’re learning.

Step 5: Make Sure AI Aligns with Your Brand Voice

Don’t let AI turn your cozy boutique store into a robot mall.

If you’re known for witty copy or warm service, make sure your AI chatbot sounds like you.

If your store is minimal and sleek, your discovery tools should reflect that.

Remember: AI isn’t a replacement for your brand, it’s an amplifier.

Related readings for eCommerce AI:

20 Tasks eCommerce Founders Should Delegate to AI

How to Use AI this BFCM to Get More eCommerce Sales

Using AI for Conversion Rate Optimization: 7 Proven Strategies for eCommerce Stores

How to Use AI for eCommerce Email Marketing - 7 Real-World Strategies

20 Ways eCommerce Brands Are Using AI (Real Examples)

FAQs: AI Product Discovery for eCommerce

1. What is AI product discovery, exactly?

AI product discovery uses artificial intelligence to help your customers find the right products faster and easier whether that’s through smarter search, personalized recommendations, visual search, or chatbots that guide users in real time. Instead of just keyword matching, AI understands intent, behavior, and context to deliver relevant results that feel personal.

2. How is AI different from regular search or filters?

Traditional search looks for exact keywords, and filters often require users to know what they want upfront. AI-powered discovery goes deeper: it understands what the customer means, even if they don’t say it perfectly. For example, it can show products similar in style or function, recommend based on browsing habits, and even help customers who aren’t sure what they want yet.

3. Will using AI for discovery affect my SEO or product listings?

Great question! AI-powered tools typically work within your existing product catalog and don’t replace your core listings or SEO setup. Instead, they enhance the customer experience on-site by improving how products are found and recommended. Just keep your product data clean and updated AI thrives on quality data!

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