Online research is changing rapidly. Users are no longer satisfied with a list of links: they ask a question and receive a synthetic, contextualized answer that is already interpreted by an AI. Conversational platforms, AI Overviews, and augmented engines are changing the way products are discovered and evaluated.

This evolution is creating a new challenge for brands. Optimizing for traditional engines is still essential, but insufficient. We now have to work on a second discipline: GEO, in order to appear in generative responses.
The nerve of war is no longer just the click, but the understanding : how to make sure that AIs understand your products better than those of your competitors?

1. Why SEO is no longer enough to generate visibility

SEO remains a central role for all e-commerce sites. It structures the understanding of the catalog, orients relevance signals, and builds domain authority. AI engines still rely heavily on these signals when selecting their sources.

But a gap has grown: AI engines no longer link to pages, they Synthesize information. The content is therefore only visible if it is understood, extracted, and reused in their responses.
In this context:

• a page that ranks can remain invisible in an AI response,
• a product without detailed attributes cannot be recommended
• a poorly defined category can be ignored in favor of a better structured competitor.

Engines are no longer limited to indexing: they interpret, compare, describe, prioritize.
The battle is therefore played out in the quality of product data, not only in the quality of the page.

2. GEO: the new discipline to optimize the way AIs cite you

GEO — Generative Engine Optimization — consists in structuring information to make it usable by AIs that respond to users.
Unlike SEO, its objective is not to obtain a better positioning, but to be:

• quoted,
• present,
• recommended,
• compared,
• Described correctly.

Above all, AI engines are looking for:

• complete and consistent attributes,
• factual descriptions,
• structured data,
• legible comparisons,
• FAQs,
• standardized information.

GEO does not value storytelling.
It values precision, the substructure And the consistency.

This logic is very similar to working on product flows for Shopping Ads, where the objective is already to provide algorithms with accurate, usable data that is aligned with the intention. Tools like Smart Asset and Feed Enrich by Dataïads can help structure, enrich and harmonize this data on a large scale: generation of reliable structured data, creation of additional attributes to describe each product accurately, and production of pages capable of answering questions and prompts formulated by users and prompts formulated by users with generative LLMs engines.

For example: “I am looking for a light red dress for a summer evening, I want to be cooler and stay under 200 euros”. With the multimodal AI models integrated into Dataïads solutions, current PDPs can be enriched to be referenced by LLMs on this type of request, thanks to the addition of semantic elements from the product flow, third-party sources, customer reviews, but also visual and video analyses via image recognition. This combination increases the product's ability to be understood, categorized, and selected in GEO responses.

This structured data can then be encoded in the formats expected by LLMs, using the attributes created or enriched to complete the pages, reinforce the context, or generate contextualized pages dedicated to specific intentions.

3. What AIs are really analyzing in your e-commerce content

Traditional engines evaluate a page using signals: keywords, backlinks, speed, UX, global relevance.

AI engines work differently. They break information down into entities, relationships, and contexts.

3.1. AIs read product attributes as truth signals

Name, material, dimension, use, use, compatibility, segmentation...
Each attribute becomes a brick of understanding.

A product with 10 clear attributes is more valuable than a page with 1000 words of fuzzy text.

3.2. They assess the coherence between your various contents.

A model should be described in the same way:

• on your product page,
• in your category,
• in your flow,
• in your short descriptions,
• in your structured data,
• in your creative assets.

The slightest inconsistency reduces the engine's confidence in the data.

3.3. They prefer structures that are easy to extract.

Tables, lists, comparators, technical blocks.
The more “copyable” the information is, the more it is used.

3.4. They rely on existing authority signals

Reviews, quotations, factual accuracy, regular updates.
Even the smallest lag — a poorly timed price, incorrect availability — can reduce the likelihood of being recommended.

This mechanism reinforces a conclusion: a clean, enriched and coherent catalog becomes a strategic advantage.

4. The convergence between SEO, product flow and Shopping optimization

For retailers and e-retailers, this evolution touches the heart of business.
Three pillars converge:

4.1. SEO structures the overall architecture of the site

• consistency of categories,
• quality of content,
• mesh logic,
• technical performance.

4.2. Product feeds the Shopping Ads algorithms

• enriched titles,
• accurate descriptions,
• full attributes,
• product segmentation,
• up to date data.

4.3. AI engines unify these two worlds

They select the best signals from the pages, the flow, the schema, the reviews, the media — to make a unique response.

It requires a vision Product intelligence : each product should be described, enriched and contextualized in a uniform way, regardless of the channel that reads it.

5. Essential SEO best practices to stay visible

SEO remains a prerequisite for any GEO strategy.
AI engines cannot cite you if your content does not meet a minimum level of quality and authority.

5.1. An irreproachable technical base

• Core Web Vitals mastered,
• crawlability,
• clean indexing,
• mobile-first,
• clear hierarchy of pages.

5.2. Complete product pages

• unique descriptions,
• optimized visuals,
• exhaustive attributes,
• similar models,
• customer reviews,
• price: consistency + clarity.

5.3. A rich and coherent schema

AIs use structured data massively to understand:

• categories,
• characteristics,
• price,
• availability,
• relationships between products,
• frequently asked questions.

A good structure allows the engine to rebuild or complete an unambiguous response.

5.4. High-density information content

Buying guides, comparisons and educational files remain important signals of trust.

Their role is evolving:
They are no longer just used to attract traffic, but to become sources that AIs can cite.

6. Building a GEO ready catalog: the complete method

The transition to GEO requires a structured and gradual approach.
Here is a method directly inspired by the analysis of generative engines.

6.1. Phase 1: diagnosis and prioritization

Objectives:

• assess the quality of product data,
• detect missing attributes,
• measure cross-channel consistency,
• analyze the risks of confusion produced,
• identify priority categories

This step is based on:

• your existing pages,
• your Merchant Center feed,
• your data layers (ERP, PIM, CMS),
• your textual and creative content.

6.2. Phase 2: technical architecture and structured data

An AI engine only recommends what it fully understands.
This requires:

• a complete schema (Product, ImageObject, Review, FAQ, Offer...),
• clear product hierarchies,
• controlled parent/child relationships,
• stable synchronization between feeds and pages.

A bad hierarchy can lead an AI to “merge” two similar products or to recommend an outdated model.

6.3. Phase 3: content optimization oriented to AI extraction

Here, the work focuses on how information is presented.

Examples of optimizations:

• short lists for key points,
• comparative tables for the variants,
• Product Q&A,
• technical summaries,
• stylized but factual descriptions,
• precise segmentation (use, material, category, season, style).

This phase prepares the ground for other related building blocks such as post-click landing pages or rich advertising assets.

6.4. Phase 4: enrichment produced on a large scale

This is the most complex transformation for large catalogs.

Objectives:

• complete all the missing attributes,
• generate standardized descriptions,
• harmonize product highlights,
• align the textual structure,
• structure the visuals (angles, contexts, variations),
• reinforce the signals of intent.

This work is at the heart of product intelligence and is part of the logic of solutions such as:

• Feed Enrich for flows,
• Smart Landing Pages for the contextualized post-click experience,
• Smart Creative for the creation of product assets,
• Smart Asset for coherent multimodal variants.

Each module shares a common objective: to make each product legible, intelligible and activatable.

6.5. Phase 5: measurement, iteration and multi-source attribution

KPIs are evolving.
It is necessary to measure:

• the appearance in the AI responses,
• the rise in brand queries,
• voice search performance,
• the zero-click impact on indirect conversions,
• the consistency of data between flows, pages, creations, assets,
• the evolution of CTR on Shopping Ads.

Success is not just about position.
It now reflects the brand's ability to be Included, recognized and mobilized by the AI ecosystem.

7. Specific challenges by e-commerce sector

Each vertical has its own challenges in an SEO + GEO world.

7.1. Fashion and beauty: the rise of visual search

AIs use images to understand cuts, materials, colors and uses.
Fashion catalogs should:

• multiply the angles,
• describe the materials finely,
• segment by style, silhouette, usage, weather,
• structure alt text + image schema.

7.2. High-tech: mandatory technical precision

AI engines only recommend a product if its specifications are accurate, up to date, and understandable.

This involves:

• perfect synchronization of models (SKU, GTIN, version),
• standardized characteristics,
• strict consistency control.

7.3. Home, garden & DIY: the importance of content of use

AIs prefer practical content:

• choice guides,
• tutorials,
• maintenance tips,
• compatibilities.

This content makes it easier to cite in AI responses and strengthens the position of authority over key categories.

7.4. Health: increased requirement on verification

AIs apply caution filters.
They promote:

• validated sources,
• factual content,
• careful formulations,
• expert quotes.

8. Product flows as the backbone of GEO

AI engines are increasingly exploiting structured databases.
Product flows therefore become a central source of truth.

An enriched flow allows:

• a better understanding of the product,
• a better appearance in Shopping Ads,
• increased consistency with the pages,
• a reduction in errors between channels,
• better readability by AIs.

Product data is no longer just an advertising medium.
They become a strategic lever for media activation, unifying SEO, SEA, AI, and post-click experience.

Conclusion: e-commerce visibility requires product data control

The border between SEO and GEO is blurring.
AI engines rely on the same signals as traditional search engines, but interpret them differently.
What matters now is not only to be found, but to be understood.

For brands, this involves three transformations:

1. consider product data as a strategic asset;
2. harmonize flows, pages, content and assets;
3. prepare the catalog for a world where recommendations are generated, not clicked.

Brands that anticipate this transition will have a lasting advantage.
Those who wait may find out too late that their products have disappeared from the answers.

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