The core problem: the future is AI-first, but product data is still fragmented

Svetlana (Pixyle AI) highlighted that product data often sits across multiple systems (PIM, ERP, storefront tools), multiple teams, and—too often—spreadsheets being passed around. Marketing wants emotional storytelling; ecommerce needs standardized attributes for filters and search. The result is inconsistent naming, missing values, and subjective tagging. That fragmentation hurts sales because filters break, relevance drops, and shoppers hit dead ends.

Paige (Prefixbox AI) framed it as a foundational gap: retailers want AI agents and conversational commerce, but many still rely on manual processes for basic product information. Without fixing that base layer, advanced discovery won’t deliver.

Table stakes still aren’t covered

A big reality check: many ecommerce sites still fail at basic search expectations—handling non-product queries (shipping/returns), supporting simple attribute searches reliably, and understanding subjective intent like “cheap” or “high quality.” Conversational commerce builds on fundamentals; it doesn’t replace them. If the basics aren’t solid, the leap to next-gen discovery is hard.

What “good” product discovery looks like: foundations first, then optimize for conversational

1) Strong foundations

• Accurate categorization and clear category naming
• Standardized attributes (color, material, fit, occasion, sleeve length, etc.)
• Clear, descriptive product titles and descriptions

Svetlana gave a practical example: “midnight blue” may sound great in marketing copy, but if your taxonomy expects “navy,” AI and search systems struggle. Creativity is fine—structure still needs consistency.

2) Optimize for conversational & agentic discovery

Once the basics are in place, add what conversational search needs:

• Use cases and occasions (“office,” “training,” “wedding guest”)
• FAQ-style Q&A on PDPs to match question-based searches
• Rich descriptions that include context, not just features

Agentic commerce is already a channel: optimize for machines, not only humans

A key point: retailers now need to think about two audiences:

1. humans searching onsite
2. AI engines/agents discovering products across platforms

To help machines “understand” products, structured content matters more than ever. That includes using formats AI can reliably parse—like JSON-LD/schema markup—and ensuring it contains the right fields (attributes, offers, availability, and reviews).

The impact: better discovery, higher conversion, massive efficiency gains

Svetlana shared patterns they’ve seen after enrichment:

• Zero-results pages drop sharply (often close to zero)
• Add-to-cart from search increases (example uplift shared ~12%)
• Conversion lifts 5–8% driven by better product data

Efficiency gains are just as important. AI shifts teams from manual tagging and copywriting to review/approval and tone-of-voice control—unlocking more scale with the same headcount.

Practical checklist from the webinar

• Standardize key attributes; avoid free-text chaos
• Write richer titles/descriptions including use cases
• Add PDP FAQs to match conversational intent
• Improve image-level signals (alt text, short videos, viewpoints)
• Embed structured data (JSON-LD) including reviews/ratings
• Connect enrichment back to PIM/storefront so updates scale

Final takeaway

AI-ready discovery starts with AI-ready product data. The retailers who fix foundations now—and use AI to scale enrichment—will be easiest to find in both onsite search and the fast-emerging agentic web.

For more details, watch the full recording of the webinar:

1. Exact Match Search

Nike Air Zoom Pegasus 40

This is the simplest query type: the shopper knows the exact product name or model number.

Best Practices:

• Ensure perfect indexing of titles, SKUs, and model numbers
• Handle spacing, punctuation, and pluralization variations
• Return only the relevant product as the top result

2. Product Type Search

Running shoes or gaming laptops

This is one of the most common types of ecommerce search queries. The user knows what type of product they want, but not which specific product.

Best Practices:

• Broaden results to the full category
• Use filters to allow quick narrowing
• Highlight popular subcategories

3. Feature Search

4K monitor, cotton t-shirt or waterproof jacket

Feature searches rely heavily on structured product data. Poor or inconsistent attribute tagging makes results collapse.

Best Practices:

• Normalize attributes (always store the same feature in the same field)
• Support synonyms (“puffer” = “down jacket”, “cotton” = “100% cotton”)
• Return results even when attributes aren’t explicitly written in the title

4. Thematic Search

Summer dresses or winter running gear

These rely on contextual understanding, (seasonality, temperature, use cases, trends) that may not appear in the product data.

Best Practices:

• Map themes to product attributes
• Automate theme detection using AI enrichment
• Design landing pages for thematic queries

5. Use-Case Search

Laptop for travel or shoes for flat feet

Here the shopper expresses intent, not attributes. These are often the highest converting queries because the user expresses a specific problem.

Best Practices:

• Train the search engine to recognize intent
• Promote products tagged with compatible use cases
• Use dynamic ranking to prioritize best-fit items

6. Compatibility Search

Charger for iPhone 14 or filters for Dyson V11

These require relational understanding between products.

Best Practices:

• Use product-to-product compatibility data
• Ensure model-number synonyms are supported
• Avoid returning incompatible items at all costs

7. Subjective Search

Quality camera, comfortable sofa or affordable TV

These queries include subjective adjectives that require interpretation.

Best Practices:

• Map adjectives to measurable features (e.g., “affordable” → lower price tier)
• Use ratings, reviews, and popularity as ranking signals
• Avoid literal interpretation; focus on intent

Subjective search shows whether your engine understands meaning, not just text.

8. Non-Product Search

Return policy, shipping time or store hours

A surprising number of ecommerce search bars still fail to return basic informational pages.

Best Practices:

• Index content pages, FAQs, and help articles
• Display rich snippets for policies and processes
• Use content relevance as a separate search vertical

9. Symbol, Abbreviation & Formatting Search

55 inch TV or 55″ tv, msi gf63 or 205 55 16

Many ecommerce search engines break when formatting changes.
Research shows 70%+ of sites don’t handle abbreviations and symbols well, causing lost conversions.

Best Practices:

• Normalize formatting (spaces, dashes, slashes, quotation marks)
• Support numeric pattern detection
• Map abbreviation variants (“in” = “inch” = “)

Why Search Query Types Matter

Failing to support just one of these query types can lead to:

• Zero-result pages
• Lower conversion rates
• Higher bounce rates
• Misleading relevance
• Poor customer trust
• Lost revenue

Most E-commerce platforms support only 2–3 of the above types, while modern AI search solutions support all 9, automatically.

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UX Best Practices for High-Converting Ecommerce Search

1. Always Avoid Zero Results

Use spell correction, synonyms, fallback categories, and partial matching to surface relevant options.

2. Use Filters to Reduce Cognitive Load

Shoppers should be able to refine based on the attributes that matter most.

3. Prioritize Personalization

Search ranking should learn from user behavior and adapt dynamically.

4. Optimize Mobile Search Experience

Mobile shoppers rely on autocomplete and filters even more than desktop users.

5. Ensure Search Is Fast and Predictive

Autocomplete should show results within 100–150 ms to feel instant.

Summary

Ecommerce search is no longer a simple utility, it’s a revenue engine. To meet shopper expectations, a search bar must understand all 9 search query types, deliver fast and accurate results, and be supported by clean, consistent product data.

Retailers who invest in search intelligence consistently see:

• higher conversion rates
• fewer failed searches
• more engaged users
• stronger category discovery
• increased revenue

Prefixbox AI provides the infrastructure to support this modern search experience, combining AI enrichment, predictive search, dynamic ranking, and merchandising control.

The Evolution: From Keywords to Understanding

Search has moved far beyond keyword matching and backlink games. With semantic models and LLMs, engines understand content and shopper intent. For merchants, this means:

• Optimize on-site search for conversational, intent-rich queries.
• Ensure product data and content are structured and complete so AI services can parse and surface it.

Vector Search: Meeting Shoppers’ Intent (Not Just Their Keywords)

Vector search recognizes concepts, not just exact terms. If a shopper types “party dress,” vector search can retrieve relevant products even if “party dress” isn’t a literal tag, positioning results between related ideas like cocktail and elegant dress.

Why it matters:

• Handles conceptual and long-tail queries.
• Works alongside traditional keyword search; results can be re-ranked for relevance.
• Reduces manual synonym management thanks to semantic understanding.

Prefixbox highlights rolling out AI vector search and measuring real impact, citing a retailer case with >45% revenue increase and >28% AOV uplift.

If you’re on Salesforce Commerce Cloud, Prefixbox is available via AppExchange, enabling this capability on your store.

Be Discoverable Where Shoppers Start (GPT & Friends)

Shoppers increasingly research and buy through conversational assistants. Example from the webinar: a user asks for hiking pants and receives specific product models plus direct links to brand sites—all within one chat.

How to surface your products in conversational engines:

• Complete, rich product data: size, color, brand, category, attributes, plus correctly tagged images.
• Structured content: clear schema and consistent information architecture.
• Natural-language product descriptions: write like you’re answering a question; think FAQs, buying guides, product comparisons, and reviews.
• Classic SEO still counts: titles, headings, meta descriptions, internal linking, performance, and overall brand/domain reputation.

Reality check: GPT isn’t brand-exclusive; it can (and will) recommend competitors. Your content quality, structure, and authority determine whether you’re included.

Conversational Commerce On Your Site: Agents That Do the Work

Shoppers crave conversational experiences. If you don’t provide them, they’ll have them elsewhere. The transcript introduces Salesforce Agentforce as a way to bring this experience into your channels.

What Agentforce enables:

• Starts with a conversation (text/voice), forms a plan, and performs actions.
• Uses retrieval augmented generation (RAG) to safely access your data (catalog, order status, customer info).
• Personal Shopper Agent (announced at Dreamforce; GA in February per transcript) to:
  • Answer questions, make product recommendations,
  • Add to cart, assist checkout, and handle order tracking (capabilities expand over time).
• Works across your website and channels like WhatsApp/iMessage.

Practical Roadmap: Test, Measure, Operationalize

1. Test & Learn: Try multiple AI use cases and UI patterns (chat UI vs. rich grid after a long query).
2. Measure: Track what boosts engagement and conversion for your audience.
3. Operationalize:
   • Get the right product data in the right structure.
   • Feed search/agents with real behavioral data to improve recommendations.
   • Keep content conversational and comprehensive (FAQs, comparisons, guides, reviews).

FAQ

Q: Does vector search replace keyword search?
A: No. They work together. Vector search handles conceptual queries; keyword handles exact matches. Re-ranking brings the best results up first.

Q: How do I get featured in GPT-style recommendations?
A: Provide complete, structured product data, conversational descriptions, and authoritative content (reviews, guides, FAQs). Maintain strong technical SEO and brand trust signals.

Q: What does an AI agent actually do on my site?
A: It converses with shoppers, retrieves data, recommends products, and (as capabilities expand) helps with cart, checkout, and support—all within a trusted, guardrailed system.

Conclusion

Product discovery now spans on-site semantic search and off-site conversational engines. Retailers who pair vector + keyword search, invest in structured, conversational content, and deploy on-site AI agents will win the next era of discovery—from search to chat.

For more details, watch the full recording of the webinar:

David summed it up simply:

“Every effort counts… until it’s out of sync.”

When Good Teams Still Produce Bad Results

Most modern e-commerce setups look like this:

• An SEO agency
• A paid media team
• An email/CRM partner
• A web dev or CRO team
• Internal marketing & operations

Each team is smart and motivated. Each is tracking its own KPIs. But as David pointed out, those KPIs almost never tell the whole story.

That’s when fragmentation shows up:

• Email campaigns with amazing open and click rates…
  but the landing page doesn’t match the offer, so users bounce.
• Paid ads that look profitable in the ad platform…
  but send traffic to generic category pages with poor UX.
• Leadership deciding “TikTok doesn’t work for us”…
  even though, when you look at the data in context, it’s actually one of the best assist channels.

It’s rarely a tool problem. It’s a coordination problem.

From Silos to a “Common Brain”

To fix this, David introduced the idea of the “common brain”: a way of working where all teams contribute to one shared understanding of the business.

What does that look like in practice?

• One coordinating actor (person or agency) responsible for aligning everyone.
• A shared project board (they use Asana, but any tool works) where all agencies and teams see priorities, timelines, and dependencies.
• A common calendar of campaigns, launches, tests, and dev work.
• Monthly alignment calls where everyone shares insights, not just reports numbers.

Instead of each team optimizing its own slice of the funnel, they start collaborating around one shared outcome: business growth.

That’s also where partners like Prefixbox and Vevol Media work best together: when search, UX, performance marketing, and product strategy are aligned around the same customer journey.

Using Technology to Simplify, Not Complicate

Tech is supposed to make life easier bu,t in many stacks, it actually adds to the chaos: too many apps, too many dashboards, not enough integration.

David’s first move with new clients is surprisingly simple:
a clean-up.

• Remove unused or redundant apps.
• Consolidate tools where possible.
• Make sure you’re actually using the features you pay for.

Often, brands can save 10–30% of costs just by rationalizing their stack. Then, instead of checking five different dashboards, David recommends aggregating data into one central view that shows how channels influence each other.

Iringo added how this plays out at Prefixbox: on-site search data doesn’t just improve conversion — it becomes an insight engine that feeds marketing, merchandising, and UX. When that data is shared, not siloed, it creates a feedback loop that improves the entire customer journey.

The Mindset Shift: Stop Thinking Like a Small Business

Toward the end, Iringo asked what mindset shift brands need most if they suspect their setup is fragmented.

David’s answer had two parts:

1. Realize you’re not a small business anymore.
   You can’t run a 6–7 digit store with one person as the bottleneck for every decision. You have to delegate and trust specialists.
2. Stop judging teams by vanity metrics alone.
   It’s never just “ads’ fault” or “email’s fault.” Success is an aggregated outcome. Look at customer lifetime value, return rates, abandonment, purchase timing, and how channels work together, not in isolation.

Once teams start seeing the impact of unified execution, momentum builds. Trust grows, decisions become more data-backed, and, as David put it, the whole process develops a rhythm.

Unified execution isn’t about doing more.
It’s about doing the right things together, and letting every effort truly count.

For more details, watch the full recording of the webinar:

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Paid Media: Winning the Attention Battle in Q4

James emphasized that Q4 is not the time for conservatism. While CPMs rise, the opportunity to offset slower months makes aggressive, well-planned campaigns worth the investment. His four-stage roadmap for BFCM success includes:

• Data-Driven Planning
  Review 3–4 years of historical performance data.
  Focus on engagement spikes, best-performing offers, and profit margins (not just sales volume).
  
• Craft Irresistible Offers
  Move beyond flat discounts.
  Use tactics like gift-with-purchase, bundles, or “buy more, save more” to increase average order value (AOV) without eroding margins.
  
• Creative Production
  Lean on proven, evergreen ads.
  Add seasonal overlays or text updates instead of testing unproven creative angles.
  
• BFCM Calendar Strategy
  Start warming up audiences in September/October with teaser offers, video views, and email signups.
  Differentiate offers throughout November, ramping up toward Black Friday.
  Extend the momentum with “cool down” sales or founders’ specials after Cyber Weekend.
  

Pro tip: Ensure inventory alerts and automations are in place to pause ads when products sell out. Nothing is worse than driving clicks to unavailable items.

CRO: Turning Clicks Into Conversions for Black Friday 2025

With traffic secured, Soma highlighted seven + one CRO priorities to maximize revenue:

1. Streamline Checkout – Allow guest checkout, minimize fields, and offer fast payment options like Apple Pay/Google Pay.
2. Mobile First – With 70%+ of BFCM traffic on mobile, optimize load speed, navigation, and tappable CTAs.
3. Site Speed & Reliability – Even a 1-second delay can cut conversions by 7–10%. Stress test your infrastructure.
4. Personalization – Use AI-powered search, filters, and recommendations to make discovery seamless.
5. Trust Signals – Highlight reviews, return policies, guarantees, and social proof to reduce hesitation.
6. Urgency & Scarcity – Show stock levels, countdown timers, and last-chance offers (but don’t overuse pop-ups).
7. Post-Purchase Upselling – Cross-sell and upsell at checkout, thank-you pages, and via confirmation emails.
8. (+1) AI Agents – Chat-based shopping assistants can guide customers from discovery to checkout, offering a fully conversational commerce experience.

Trends to Watch in 2025: From Q5 to Generative SEO

Both speakers emphasized that the shopping season doesn’t end with Christmas.

• The Rise of Q5
  Coined by Meta, Q5 refers to the post-holiday window in January. While CPMs drop, purchase intent remains high, especially for health, fitness, and beauty brands. Extending BFCM campaigns into Q5 can deliver outsized ROI.

• AI Reshaping Search & Discovery
  Google’s new AI Mode in Chrome and AI Overviews in Search are redefining SEO. Instead of traditional keyword queries, shoppers ask conversational, long-tail questions (e.g., “affordable waterproof boots under €100, size 9”). This shift has birthed GEO – Generative Engine Optimization. To win in this AI-first search era:
• Maintain clean product feeds and structured data.
• Develop comprehensive content that answers related sub-questions.
• Double down on trust signals (reviews, authority, transparency).

Sites that adapt stand a chance of being featured directly in AI-generated results—prime visibility real estate.

Summary

Black Friday 2025 success requires a blend of data-driven ad strategy, flawless user experience, and early adoption of AI tools. Brands that:

• Warm up audiences well before November,
• Optimize every step of the conversion funnel, and
• Embrace Q5 and AI-driven discovery,

…will be the ones driving record-breaking results.

For more details, watch the full recording of the webinar:

A well-organized Shopify product catalog is the foundation of every successful Shopify store. When your product data is structured, clean, and easy to manage, shoppers can discover products faster, search engines can index them better, third-party apps can be integrated easier and your team can scale without headaches.

What’s more, a well-structured product catalog optimizes it for generative engines and increases the chance of being included in AI answers, like Google’s AI Overview or ChatGPT.

Metafields: Add Unique Details to Product

Metafields let you extend your product catalog beyond Shopify’s default fields. They’re ideal for capturing unique product attributes that help shoppers make informed decisions and keep your catalog structured. Examples include:

• Care instructions (e.g., “Machine wash cold, tumble dry low”)
• Fabric composition (e.g., “80% cotton, 20% polyester”)
• Warranty length (e.g., “2 years”)
• Technical specifications (e.g., laptop battery life, screen resolution)
• Benefit statements (e.g., “Moisture-wicking fabric keeps you cool”)
• Custom trust badges (e.g., “Fair Trade Certified”, “Organic Cotton”)

Where to set up metafields in Shopify

• Admin → Settings → Custom Data → Products → Add Definition
• Choose the field type (text, number, image, video, etc.)
• Populate via the product editor or bulk editor
• Connect through the theme editor’s Dynamic Source

Metafields are best for: Adding product-specific details (like materials, care, or specs) without creating multiple templates—making each product page richer and more relevant for shoppers.

For even more details, visit Shopify Academy, Shopify Help Center or the developer documentation.

Metaobjects: Structure Repeatable Content Blocks

Metaobjects allow you to create custom content models that store structured, reusable data, separate from any one product, so you can link this content to multiple products or pages via metafields. This makes it easy to maintain consistency and update info in one place.

Real-World Examples:

• Product Highlights (Feature Cards)
  Create a Product highlight metaobject with fields like image, title/heading, caption, description. Then link it to products so you can display key selling points (e.g. “Long-lasting battery,” “Eco-friendly materials”) dynamically.
  
• Size Charts & Fit Guides
  Make a metaobject that holds structured size data (e.g. chest, waist, inseam, length). Attach the right size chart to each product (or category) so shoppers always see correct sizing info.
  
• Brand or Designer Profiles
  Build a metaobject for Brand Profile with logo, name, origin, story, images. Then attach it via metafield to all products of that brand — so brand details appear consistently across the catalog.
  
• Ambassador / Influencer Profiles
  Shopify’s docs show using metaobjects to model Ambassador profiles (image + bio) and reuse them in multiple places (product pages, collection pages, campaign pages).

Where to set up metaobjects in Shopify

Create a metaobject definition

• Admin → Content → Metaobjects → Create Definition
• Define fields (e.g. image, title, text) and configure settings (Storefront access, publishing)

Add entries (instances)

• Once the definition is ready, add entries (e.g. a “Highlight A”, “Highlight B”, or a specific brand profile) in the metaobject list panel.

Link metaobjects to products (via metafields)

• Create a product metafield whose content type is a metaobject reference (single or list)
• Choose the metaobject definition you’ll reference (e.g. Product Highlights)
• Assign one or multiple entries to each product’s metafield field

Display metaobject content in your theme

• Use the theme editor’s Dynamic Source / Connect option to connect metaobject fields to blocks/settings in templates
• Ensure that metaobject reference types and block settings have compatible types (e.g. a metaobject’s image field matches a block’s image setting

Metaobjects are best for: when you need consistent, structured content blocks that are reused across products (or beyond) — e.g. size charts, highlight lists, brand profiles, or care guides. They prevent duplication, simplify updates, and keep your catalog clean and scalable.

Product Options: Shopper-Facing Variants

Product options are the backbone of your Shopify product catalog when it comes to customer choices. These create actual variants that shoppers select and buy, directly tied to inventory and pricing.

Examples include:

• Size
• Color
• Material

Where to set up metaobjects in Shopify

• Admin → Products → Select Product → Variants → Add Options
• Shopify generates the variants automatically

Product Options are best for: Any choice that changes the product in checkout (e.g. SKU, price, or stock).

Tags: Organize and Automate Behind the Scenes

Tags aren’t visible to shoppers but are a powerful way to organize your catalog internally.

Use them for:

• Automated collections
• Admin filtering
• Shopify Flow or app workflows

Where to set up tags in Shopify

Admin → Products → Select Product → Tags (right sidebar)

Tags are best for: Organization, categorization, and backend automation, not customer-facing details.

Comparison: When to Use Which

Tool	Best For	Examples	Where to Set Up
Metafields	Adding unique, product-specific details beyond Shopify’s default fields	Care instructions (“Machine wash cold”), fabric composition (“80% cotton, 20% polyester”), technical specs (battery life)	Admin → Settings → Custom Data → Products → Add Definition
Metaobjects	Creating structured, reusable content blocks that can be linked to multiple products	Product highlights cards (eco-friendly materials), size charts & fit guides, care instruction sets, ingredient lists	Admin → Content → Metaobjects → Create Definition
Product Options	Managing shopper-facing variants that change SKUs, inventory, and checkout	Size (S, M, L), color (Forest Green, Midnight Blue), material (Leather vs Vegan Leather), storage capacity (64GB vs 128GB)	Admin → Products → Select Product → Variants → Add Options
Tags	Organizing and automating behind the scenes (not visible to shoppers)	Seasonal collections (“Spring 2025”), campaign labels (“Email-promo”), workflow triggers (“Dropship”, “Pre-order”)	Admin → Products → Select Product → Tags (right sidebar)

Quick rule of thumb:

• Affects checkout → Use Product Options
• Organizational label → Use Tags
• Unique product detail → Use Metafields
• Reusable structured block → Use Metaobjects

BONUS: Optimizing Your Shopify Product Catalog for Google AI Overviews

Search is changing, and so must your product catalog. With Google AI Overviews (GEO), shoppers may ask conversational questions like:

“Affordable waterproof hiking boots under $100 in size 9”

To get your products surfaced, your product catalog must be machine-readable and structured:

• Add schema.org structured data for key attributes (price, size, colour, availability).
• Use consistent naming (don’t mix “navy blue” and “royal blue” as separate values).
• Keep filterable attributes (size, colour, material) in metafields or options, not buried in descriptions.
• Highlight trust signals (reviews, return policies) as structured content.

Best practices for maintaining a scalable Shopify product catalog

1. Keep naming consistent (e.g. always “XL,” not “Extra Large”).
2. Limit variant sprawl — too many variants can overwhelm both customers and your admin.
3. Use metafields for unique, product-level info.
4. Use metaobjects for structured content blocks.
5. Regularly audit tags — remove outdated or unused ones.
6. Check catalog performance — large stores should test for speed and indexing issues.

For all the essentials on product page SEO check our other article.

Final Thoughts

A clean, structured Shopify product catalog is more than admin hygiene, it’s a growth lever. The right combination of metafields, metaobjects, product options, and tags makes your catalog flexible for merchants, simple for shoppers, and visible in the evolving world of AI-driven search.

By mastering these tools, you’ll not only improve your store’s conversion rate, but also ensure your products remain competitive in the age of conversational commerce.

1) Your foundation: platform trade-offs

Your commerce platform shapes everything from release velocity to uptime. Weigh financial cost and time cost across two models:

• Self-hosted (e.g., WooCommerce-type setups): often cheaper to launch and highly flexible, but ongoing maintenance (security patches, plugin conflicts, infrastructure downtime) becomes a hidden tax—especially on peak days like Black Friday.

• Hosted (e.g., Shopify-style platforms): higher sticker price, but updates, compliance changes (e.g., Consent Mode v2), and scaling are handled centrally in minutes—not days.

Whatever you choose, insist on native integrations across payments, analytics, consent, ERP, and mobile. Native connectors reduce fragility and keep Ecommerce tech adaptable as your stack evolves.

2) Product discovery that actually converts

Search isn’t “just a feature.” It’s one of the largest revenue drivers because shoppers can’t buy what they can’t find.

AI-powered search only matters if it uses vector/semantic retrieval, not just keyword matching.

Vector search understands concepts like “flowy dress for a summer wedding under $150,” returning relevant options even when the exact words aren’t in the product title. Brands moving from text-match engines to true AI search typically see conversion and revenue lift, plus far less manual rule-tuning.

3) AI agents move from support to shopping

Agentic commerce isn’t five years out—it’s here. Train an AI agent on your catalog, FAQs, PDFs, and content to deliver a guided, associate-style experience 24/7. Early adopters report responses that match—or beat—human accuracy most of the time, with rapid time-to-value.

Practical tip: blend chat + results in one UI with rich product cards; customers want conversation and visuals.

4) Personalization that feels like a great salesperson

The goal is simple: show the right item to the right customer to lift conversion and AOV. Success depends on:

• Sufficient volume (rule of thumb: 200–500+ orders/month) so models actually learn.
• Clean catalog attributes (canonical color/size vocabularies; avoid duplicates like “red,” “red2,” “rojo”).
• Smart placement (don’t show alternative couches in cart; do show complements and repeat-purchase staples where relevant).

When implemented well, personalization often delivers 10–40× ROI; sessions that engage with recommendations commonly show 30–50% higher conversion and AOV.

5) Lifecycle automation is better than paid re-acquisition

Email/SMS/push are your always-on associates. Choose platforms with native connectors to your stack and robust automation + A/B testing (subject, content, send time). Trigger browse/cart flows and education sequences to reclaim demand more efficiently than ads: critical as paid media costs rise.

6) Analytics you’ll actually use

Keep a small, durable KPI set visible weekly and monthly: conversion rate, AOV, revenue per user, CAC, LTV, and cart abandonment. Pair GA4 Enhanced Ecommerce with server-side tracking once you pass ~500–1,000 orders/month to see past cookie consent gaps. Dashboards (e.g., ecommerce-focused BI layers) are a later acceleration, not a prerequisite.

7) Proving ROI (without fooling yourself)

Define one primary metric per initiative (e.g., revenue per user for recommendations).

• If you have volume, run a clean A/B test (aim for ~5,000 measured events/month for the layer you’re testing).
• If you lack volume, use sequential testing only for big changes; seasonality can swamp small effects.
• Instrument custom events (agent interactions, rec widget clicks) before you test.
  Calculate profit impact and compare to tool cost; buy what returns profitable lift, cut the rest.

8) A simple evaluation framework for Ecommerce tech

Use a FIRE-style lens: Flexible, Inexpensive, Rapid, Easy.

Prefer cloud-native, composable tools with native connectors, quick implementation, fast feature velocity, and everyday usability—so your team keeps shipping as the market shifts.

9) Operate like this: audit → outside eyes → gap plan

Run an honest audit of costs (money + time), have a third-party review for blind spots, then prioritize a gap plan toward your “castle.” The brands that move first on modern Ecommerce tech (semantic search, agents, clean data, native integrations) will widen the distance every month.

Bottom line: Customers now expect conversational, visual, and personalized journeys. Build your stack so discovery feels inevitable, data flows cleanly, and experiments answer one question: did this make us more money, reliably?

For even more details, watch the full recording of our webinar:

From keywords to conversations

For years, Google trained shoppers to compress intent into three words. Now, tools like ChatGPT have flipped the script: a shopper types, “I need a flowy dress for a summer wedding under $150,” and expects a helpful, guided response. When sites still return literal, keyword-based results, shoppers bounce—to an AI agent that understands context.

What’s changing:

• Natural-language queries replace rigid filters.
• Expectations are set by AI assistants, not legacy site search.
• Patience is thin—if the right result isn’t in the first screen, customers leave.

Why the AI agent wins

An effective AI agent interprets intent, clarifies details, blends content and products, and personalizes results—just like an in-store associate. It also supports multimodal interaction (text, images, and voice), meeting shoppers where they are and how they prefer to communicate.

Key capabilities:

• Understands ambiguous requests (“guest dress for Spanish wedding”).
• Personalizes with first-party data and loyalty context.
• Presents rich, visual product cards, not just blue links.

Don’t just rank, be discoverable to AI agents

Discovery now starts beyond your domain. If your products aren’t understood by external AI agents (ChatGPT, Perplexity, voice assistants), you may never enter the consideration set.

Make products agent-discoverable:

• Structure your product data (rich attributes, clean taxonomy).
• Write conversational, FAQ-style copy that maps to questions an AI agent can summarize.
• Establish authority signals through consistent, accurate content.

The infrastructure shift: vector search + MCP

Delivering conversational commerce isn’t a copy change: it’s an architecture change.

1. Vector search
   If your search returns literal matches, shoppers feel the gap immediately. That’s why traditional keyword search has started to lag behind lately. A modern stack uses semantic/vector retrieval to map “pretty summer wedding guest dress” to relevant results, even if those exact words aren’t in the title.
   
2. Model Context Protocol (MCP)
   To transact across a growing ecosystem of AI agents, expose commerce capabilities (search, cart, checkout, order history) through a standard interface. MCP acts like “USB-C for AI,” letting any compliant AI agent discover products and complete tasks. Major players are aligning around this approach, and brands that implement MCP-style endpoints will be easier for agents to work with—meaning more visibility and conversions.

Voice is next (and natural)

Conversational discovery will increasingly be spoken. Voice lowers friction and fits how people actually ask for help. Your AI agent experience should support voice input and responsive, visual output (cards, carousels, video) to keep the journey fluid.

Two places to win today

• Off-site, via third-party ai agents: Ensure agents can understand, rank, and recommend your products.
• On-site, via your own agent: Blend chat and search into a single, visual, guided experience that feels like a great store associate.

Your 90-day action plan

• Upgrade search to a vector/semantic engine.
• Restructure data and enrich product attributes.
• Rewrite content in conversational, FAQ-friendly formats.
• Expose APIs (search, cart, checkout, account) with MCP-style tooling.
• Prototype an AI agent UI that merges chat, results, and product cards—desktop and mobile, text and voice.

Bottom line: Agentic commerce isn’t a future bet—it’s the current customer expectation. Brands that become AI-agent ready now will own discovery, loyalty, and growth as this shift accelerates.

For even more details, watch the full recording of our webinar:

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Quick FAQ on Shopify Search Analytics

What is Search Analytics?

Search Analytics is the process of collecting, analyzing, and interpreting data from your store’s search activity. It shows what products customers want, which searches lead to sales, and where gaps in your catalog or UX exist.

Why are search analytics important for Shopify stores?

Because it provides demand-driven insights. While traffic analytics shows how visitors arrive, search analytics reveals what they’re looking for once they’re on your site: helping you optimize merchandising, marketing, and product availability.

Which search analytics are the most important?

The three main areas to focus on in Shopify search analytics are:

• Daily charts (search and engagement metrics)
• Popular searches
• Zero result searches

These are covered in more detail later in the article.

Where to find search analytics in Shopify?

Shopify has some built in search reports under Shopify’s Admin Dashboard -> Analytics. Be aware that these reports only populate if you use Shopify’s native Search & Discovery app.

If you use a more specialized third-party solution, such as Prefixbox AI Search & Filter, you’ll also have access to a dedicated analytics section. These apps often provide more detailed metrics, and you can combine them with Shopify’s native sales and session reports for a complete picture of customer behavior.

How often should Shopify merchants review search analytics?

At least weekly. For stores with frequent promotions or seasonal demand, checking daily ensures you can quickly catch trends, stock issues, or technical problems.

Daily Search and Engagement Trends on Shopify

Start with daily search and collection engagement charts available through your Shopify reports and analytics integrations. You’ll see something like this:

If you notice an upward or downward trend, you should check the following 5 factors:

1. Product Catalog or Site Changes – Did you update your Shopify catalog or change product visibility? Cross-check with Shopify’s built-in traffic reports or Google Analytics.
2. Seasonal / Calendar Effects – Holidays, back-to-school, and other recurring events impact search behavior.
3. Marketing & Campaigns – Shopify discount codes and promotions can spike interest in certain keywords.
4. Technical Issues – Ensure your search function, apps, and indexing are working properly.
5. User Behavior Shifts – Look for emerging keywords that reflect new customer preferences.

Similar to Search engagements and results, most apps offer a Category chart too, showing how shoppers interact with Category pages.

Learning from Popular Shopify Searches & Categories

Your Popular Searches and Categories report is one of the most powerful parts of Shopify’s search analytics.

Treat search volume as a leading indicator of demand.

Similarly, Popular Categories report shows the most visited product collections.

If a product is highly searched but not converting, investigate: stock availability, product description clarity, or checkout UX friction.

Compare this data to Shopify’s Most Sold Items report. If they don’t align, check these 4 factors:

1. Stock levels – Are top-searched items often out of stock?
2. Pricing – Compare searched product price points to your top sellers.
3. Product Data – Enrich product pages with attributes, images, and reviews.
4. Synonym & Typo Handling – Make sure common variations (e.g., “tshirt” vs. “t-shirt”) deliver relevant results.

Insights from Shopify search analytics should feed into your buying strategy, promotional campaigns, and content calendar.

Zero-Result Searches: Fixing Search Gaps

The Zero Result Searches and Zero Engagement Searches reports highlight missed opportunities. They differ in:

• Zero result searches – List of keywords that showed no results
• Zero engagement searches – Searches where shoppers didn’t click or engage

How to handle zero result and zero engagement searches

• Add synonyms for frequent zero-result queries.

• Expand product metadata so Shopify indexes products under more search terms.

• Spot catalog gaps: if customers search for products you don’t carry, it’s a signal for buying decisions.

Optimizing Shopify’s Zero Result Pages

Customize “no result” message to provide helpful search tips such as:

• Check your spelling
• Try fewer keywords
• Browse related collections

Include contact options on zero result pages so customers can request help or products.

Showcase bestsellers or related products to keep shoppers engaged.

This ensures that even when Shopify can’t find a result, the customer still has a chance to convert.

For more on zero result page best practices, check our previous blogpost on the topic.

Bonus: Advanced Analytics with Prefixbox AI Search & Filter

If you want to go beyond Shopify’s built-in reporting, the Prefixbox AI Search & Filter app provides a richer layer of search analytics designed for ecommerce growth.

With Prefixbox, you can track:

• Search Volume & Engagement – Monitor how many searches take place and how shoppers interact with results.
• Conversion Metrics – Understand how often searches lead to product views, add-to-carts, and purchases.
• Zero-Result & Low-Engagement Queries – Pinpoint failed searches and fix them before they cost you sales.
• Top Queries, Categories & Products – Identify customer intent and align your merchandising strategy.
• Revenue Attribution – See how much revenue your search function directly generates.

These actionable insights give Shopify merchants the ability to continuously optimize product discovery, improve search relevance, and boost conversion rates. Prefixbox’s AI-powered search and advanced analytics work hand-in-hand to ensure you’re not just collecting data, but using it to grow!

Final Thoughts

For Shopify merchants, search analytics isn’t just a data point, it’s a roadmap to growth. By tracking daily trends, analyzing popular searches, and optimizing zero-result experiences, you can increase conversions, improve customer satisfaction, and stock smarter.

And with tools like Prefixbox AI Search & Filter, you can move beyond basic Shopify search analytics and unlock the full power of customer intent data.

Glossary

Find all the definitions for the shown metrics from the article:

Search engagement metric definitions

Metric	What it means
Search ER (Engagement Rate)	% of searches where the shopper clicked, added to cart, or converted
Search CTR (Click-through Rate)	% of searches that led to a product click
Search CR (Cart Rate)	% of searches that led to a cart event
Search Success Rate	% of searches that showed at least one relevant result

Search result metric definitions

Metric	What it tracks
Searches	Total searches during the selected period
Unique searches	Number of unique search terms (excluding duplicates)
SERP (Search Engine Results Page) Click events	Product clicks from the search results page
SERP Cart events	Add-to-cart events from search results
Search session count	Count of individual search sessions (per user per visit)

With customer expectations at an all-time high, seamless product discovery is key to satisfaction and loyalty. Prefixbox enables retailers to deliver ultra-relevant product recommendations and intelligent search experiences in real time, tailored to individual preferences and behaviors. When combined with Antavo’s dynamic loyalty engine with agentic AI, brands can now reward and retain shoppers not only at checkout but from the very first click.

“Loyalty isn’t just about what customers earn, it’s about how they experience your brand at every touchpoint,” said Michelle Ellicott-Taylor, Global Head of Partnerships at Antavo. “Partnering with Prefixbox allows us to bring that philosophy to life, pairing smart search with strategic loyalty engagement to create smoother, more rewarding digital journeys.”

“At Prefixbox, we believe product discovery is a key driver of customer satisfaction and loyalty” said Paige Tyrell, Chief Growth Officer of Prefixbox. “Joining forces with Antavo lets us support brands in going one step further by turning helpful search experiences into personalised loyalty moments that drive deeper engagement and return visits.”

Together, Prefixbox and Antavo empower E-commerce businesses to build stronger emotional connections with their customers by integrating loyalty mechanics into every touchpoint, from product search to post-purchase engagement. The result? Smarter shopping journeys that increase conversion and foster brand advocacy.

About Antavo

Antavo is revolutionizing the customer loyalty landscape with its cutting-edge AI Loyalty Cloud. As the market’s most powerful pure-play loyalty technology, Antavo’s platform seamlessly combines advanced AI capabilities with effortless integration, setting a new standard in the industry.

Antavo’s innovative Loyalty Planner speeds up implementation by making program planning up to 10 times faster, while the flexible Loyalty Engine, featuring an intuitive Workflows editor, brings any loyalty concept to life. At the heart of the solution is Timi AI, a groundbreaking agentic AI that guides and enhances your work at every step. 

This excellence has not gone unnoticed. Antavo is recognized by industry leaders such as Forrester, Gartner, and IDC, and it’s the preferred choice for global brands, loyalty consultants, and system integrators worldwide. Antavo’s diverse client portfolio, including household names like KFC, Skims, C&A, Flying Tiger, Notino, Scandic Hotels, Kathmandu, Brightline and Benefit Cosmetics, spans industries from fashion, beauty, retail, travel, and hospitality, showcasing the versatility and effectiveness of the platform.

Experience the future of customer loyalty with Antavo. Visit antavo.com to learn more.

About Prefixbox

Prefixbox is a leading AI-powered product search and discovery solution for E-commerce retailers.

Prefixbox’s powerful product discovery suite of AI Search, AI Agents, and Product Recommendation help retailers increase conversion rate by 45% without manual optimization.

Their robust product discovery solution is used by leading retailers like Carrefour, Leroy Merlin, and Bauhaus.

Prefixbox AI Search is now natively available in the Shopify app store and is the first of its kind to earn the ‘Built for Shopify’ badge. For more information visit: prefixbox.ai