1. How to Structure Product Data Like AI Depends on It (Because It Does)?

The strongest GEO foundation is clean, consistent product data.

As Svetlana shared in our webinar, AI engines rely heavily on structured product attributes. Retailers should standardize key fields like:

• Category
• Color
• Material
• Fit
• Occasion
• Price

Avoid inconsistencies like ‘navy’ vs ‘dark blue’ or ‘sneakers’ vs ‘trainers’.

Example:
If one product is tagged ‘Evening Dress’ and another similar item is labeled ‘Formal Gown,’ an AI search engine may treat them as separate categories. Standardized attributes ensure both appear when a shopper asks:
‘What should I wear to a formal dinner?’

For electronics, the same applies: ‘USB-C charger’ vs ‘Type-C adapter’ should be unified for AI clarity.

2. How to Write Product Descriptions for Use Cases, Not Just Features?

Your product pages should go beyond specs. One of the biggest GEO mistakes is writing descriptions only for product features, not for shopper intent.

Modern shoppers search with questions like:

• ‘Best shoes for walking all day?’
• ‘Which moisturizer works for sensitive skin?’
• ‘What laptop is good for video editing?’

Descriptions must cover use cases, in language that matches conversational search.

So include:

• Where the product can be worn or used
• Occasion and lifestyle fit
• Comfort and body fit
• Shopper-friendly benefits

Example (fashion):
‘This slim-fit cotton shirt is ideal for summer office wear or smart-casual events.’

Example (homeware):
‘This non-stick pan is perfect for quick weekday meals and easy cleanup in small kitchens.’

Descriptions that answer real questions are far more likely to surface in AI recommendations.

3. How to Optimize Titles for Both Detail and Natural Language?

Product titles remain one of the strongest GEO tips. AI-friendly titles should be descriptive, structured, and aligned with conversational intent.

Instead of:
Jacket – Black
Use:
Men’s Black Waterproof Jacket for Winter Hiking

Example:
A shopper might ask:
‘What jacket should I wear for cold rainy hikes?’
A detailed title helps AI connect your product to that exact intent.

For beauty products:

Instead of: Serum – 30ml
Use: Vitamin C Brightening Serum for Dark Spots and Uneven Skin Tone

The goal is to match how people naturally ask questions.

4. Why to Implement Schema Markup (JSON-LD) on Every Product Page?

AI engines don’t interpret webpages like humans do. They prioritize structured signals. That’s why JSON-LD schema markup is essential.

Add schema to product pages with details such as:

• Product name and category
• Brand
• Description
• Attributes (color, size, material, fit, pattern)
• Offer details (price, currency, availability)

Example:
If your schema includes ‘material: leather’ and ‘occasion: formal’, AI can recommend it for:
‘Best formal leather shoes under €200.’

Structured data is the machine-readable backbone of GEO, it is essentially the language AI systems read first.

5. Why Retailers Must Include Reviews and Ratings

AI search recommendation engines heavily weigh review signals when suggesting products, because reviews act as trust indicators. As one of the quick and easy GEO tips, Make sure rating data is included in your structured markup whenever possible.

Example:
If a shopper asks:
‘What’s the best-rated espresso machine for beginners?’
AI is far more likely to surface products with visible rating markup than those without reviews.

6. How Can Retailers Make GEO Scalable Across Thousands of SKUs?

Manual optimization doesn’t scale.

The best GEO-ready retailers connect structured attributes directly to their PIM (Product Information Management) system, ensuring product data stays:

• consistent
• standardized
• automatically updated

Example:
If stock or pricing changes daily, schema should update automatically, otherwise AI may recommend outdated offers.

For large catalogs, GEO becomes a system-level strategy, not a one-time page edit.

7. How Can Retailers Optimize for AI Answer Citations Beyond Product Pages?

One of the most overlooked GEOtip is definitely our last one: creating AI-citable content blocks outside of product pages.

Generative engines don’t only pull from product data, they also rely heavily on clear, factual supporting content like FAQs, guides, and category explanations.

Retailers should publish short, structured Q&A content that directly matches shopper intent.

Example:
If you sell skincare, a dedicated FAQ like ‘What ingredients help with sensitive skin?’ increases the chance that AI assistants cite your store as the trusted source.

The key is to make these answers:

• short and direct
• written in natural language
• supported by product links
• placed on indexable pages (category, FAQ, PDP)

In GEO, retailers who win aren’t just the ones with better products, but the ones who provide the clearest answers AI engines can reuse confidently.

GEO Tips Wrap Up

Generative Engine Optimization isn’t about chasing algorithms.

It’s about making your catalog:

• structured
• conversational
• trustworthy
• machine-readable
• recommendation-ready

Retailers who invest now in clean product data, AI-friendly descriptions, schema markup, and review signals will be the ones AI engines cite when shoppers ask ‘What should I buy?‘.

How Do Shopify Agentic Storefronts Work?

Agentic Storefronts work by exposing a merchant’s product catalog to participating AI platforms through Shopify. Once enabled in the Shopify admin, product data such as titles, pricing, variants, and availability can be accessed by AI agents that are integrated with Shopify.

When a shopper asks an AI assistant a shopping-related question, such as “What’s a good espresso machine under $500?”, the AI can pull relevant Shopify products, present options, answer follow-up questions, and complete the purchase through Shopify checkout. Orders, payments, taxes, and fulfillment all remain within the Shopify ecosystem.

This allows AI platforms to become fully functional sales channels, while Shopify remains the commerce backbone.

Why Did Shopify Build Agentic Storefront?

Shopify built Agentic Storefronts because consumer behavior is shifting rapidly toward AI-driven discovery. More shoppers are starting their journeys by asking questions, not typing keywords into search engines or navigating menus.

Shopify Agentic Storefront ensures Shopify merchants remain discoverable wherever AI-based shopping happens.

What Is Agentic Commerce?

Agentic commerce is a model where AI agents act on behalf of shoppers to research products, compare options, and complete purchases autonomously or semi-autonomously.

Instead of presenting a list of links, agentic systems actively reason about user intent and take action. Shopify Agentic Storefront is one of the first large-scale implementations of this concept in ecommerce.

How Is This Different From a Traditional Online Store?

Traditional ecommerce assumes shoppers will come to a merchant’s website, browse categories, apply filters, and search manually. Agentic commerce flips this model.

With Agentic Storefronts, the customer journey often starts and ends in a conversation. The AI agent handles discovery, narrowing options, answering questions, and executing checkout. The store no longer has to be the starting point, it becomes the fulfillment engine behind the scenes.

This doesn’t eliminate websites, but it significantly changes their role in the overall journey.

What Is the Agentic Commerce Protocol (ACP)?

The Agentic Commerce Protocol (ACP) is a standardized way for AI platforms to interact with commerce systems like Shopify. It allows AI assistants to browse products, check availability, build carts, and initiate checkout, without custom integrations for each merchant.

In simple terms, ACP turns AI platforms into marketplaces, enabling them to transact directly with stores. This is what allows ChatGPT or Gemini to sell real products rather than just recommend them.

What Is MCP (Model Context Protocol), and How Is It Different?

Answer:
MCP, or Model Context Protocol, is a framework that helps AI models access structured data and tools safely and consistently. It’s commonly used to build custom AI agents that can reason over APIs, product data, and internal systems.

The key difference is scope and intent:

• ACP is about enabling external AI platforms to sell products as a new channel.
• MCP is about building controlled, customized AI experiences tied closely to a merchant’s own systems.

They are complementary technologies: ACP expands reach, MCP enables depth and customization.

How Will Agentic Storefronts Change Ecommerce Overall?

Agentic Storefronts signal a major shift in ecommerce: from website-centric shopping to conversation-centric shopping.

AI agents reduce friction by handling comparison, research, and decision-making in a single interface. For shoppers, this means faster, more confident purchases. For merchants, it means discovery happens earlier, in more places, and often outside the traditional storefront.

Over time, AI agents will become another permanent sales channel, alongside websites, apps, marketplaces, and physical stores.

Does This Mean Merchant Websites Are Less Important?

No,but their role evolves.

Websites remain essential for brand identity, trust, rich browsing, and post-purchase engagement. However, AI agents increasingly handle early-stage discovery and decision-making. This makes on-site experiences even more important once shoppers arrive. They expect clarity, speed, and intelligent guidance.

Where Do Third-Party AI Agents Fit In?

Shopify Agentic Storefront brings products into external AI platforms. Third-party AI agents like Prefixbox AI Agent focus on what happens inside the store.

Prefixbox AI Agent acts as an in-store conversational sales assistant. It helps shoppers ask natural language questions, find relevant products, compare options, understand compatibility and specifications, and move confidently toward checkout, without leaving the merchant’s site.

In this sense, Prefixbox AI Agent becomes another sales channel inside the store, complementing Shopify’s external agentic channels rather than competing with them.

Why Do Merchants Need Both External and In-Store AI Agents?

Because modern commerce happens across multiple touchpoints.

Agentic Storefronts capture demand in AI platforms where customers start their journey. In-store AI agents convert that demand once shoppers arrive on the site. Together, they create a continuous, AI-powered shopping experience, from discovery to conversion.

This dual approach ensures merchants don’t just reach customers, but also guide them effectively to purchase.

Final Takeaway

Shopify Agentic Storefront marks a fundamental shift: conversations are becoming storefronts. Merchants who combine external AI sales channels with in-store conversational agents will be best positioned to win in this new era, capturing discovery wherever it happens and converting intent wherever shoppers land.

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.

---

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:

---

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:

---