How do merchant data optimization and a GEO strategy redefine visibility in AI search engines?

01.05.2026 | Marcin Luks | 18 min

“1.16 PLN/km”); Polish currency zł rendered as PLN. ========

Modern digital commerce is evolving, combining classic indexing with a model of generating ready-made answers. Market success increasingly depends on the quality of merchant data and on appropriately supplementing search-optimization efforts with a Generative Engine Optimization (GEO) strategy. AI systems such as Google AI Overviews, ChatGPT, Perplexity, and Claude increasingly play the role of knowledge verifiers. This requires companies to base traditional SEO on structured product-data engineering. Analytical forecasts suggest that the volume of queries on traditional search engines may change, because users readily use multi-source answers in their decision-making. A hybrid strategy – a fusion of solid organic search optimization with data formatting via Schema JSON-LD – is now one of the most important elements of building brand visibility in the market of AI assistants.

How Is the Search Paradigm Evolving in the Era of Generative Engine Optimization?

Traditional search is being supplemented by AI synthesis, which delivers the user a ready-made solution directly in the interface. This phenomenon, known as zero-click search, reached 58.5% in the US and 59.7% in the European Union in 2024, which means that a large part of queries generates no direct traffic to external sites.¹ The change in user behavior encourages marketing directors to update their success metrics, because it’s now worth supporting hard-won SEO authority with data readable by AI assistants. About 12% of the URLs cited by models such as ChatGPT hold positions in Google’s top ten standard results, which shows that generative algorithms apply additional criteria for selecting sources.

Implementing structured data in technology clusters proves that digital assistants prefer well-organized code. When investors ask about innovative hubs, AI systems more readily position those entities that make it easier for them to read the data, which supports raising capital without the need to constantly increase advertising budgets.

GEO: a field of activity aimed at providing LLMs with well-organized, substantive knowledge in order to win a recommendation in the chat window, constituting an extension of classic SEO.

AI models process user intent and break queries down into many sub-questions, looking for specific parameters in databases. In this ecosystem, the presence of product data in cited sources is important, which depends on technical correctness and the substantive depth of the descriptions. Companies that don’t adapt their content to the requirements of machine systems may lose potential in purchasing processes, especially since 73% of B2B buyers use AI tools for preliminary market research. This transformation encourages boards to combine marketing based on classic page views with attention to citations in assistants.

Market parameter	Traditional SEO (2020-2024)	The GEO and AI Search era (an extension from 2025)
Main goal	First page of results (Top 10)	Presence in the citation and the AI answer
Zero-Click rate	Approx. 30-40%	Exceeding 60% (up to 93% in AI Mode)
Optimization unit	Keywords and phrases	Entities, attributes, and structured data
Buying path	Search -> Click -> Page	AI -> Education -> Conversion / Google verification
Main technology	Hyperlink indexing	Retrieval-Augmented Generation (RAG)

Why Do Classic CTR Metrics Change Their Meaning in Purchasing Processes?

The introduction of generative answers affects the classic click-through rate on the results list. For informational queries, a drop from 1.76% to 0.61% was recorded. This data requires a broader perspective, however, because brands correctly cited by AI often gain higher-quality traffic. Traffic from assistant recommendations can have conversion rates three to five times higher than standard traffic, because users arrive at the site already having basic knowledge. This means that fewer clicks can translate into stable profit if a brand cares about the synergy of SEO and AI recommendations.

How Does the RAG Mechanism Condition the Selection of Data for Context Windows?

Presence in generative results is largely based on the Retrieval-Augmented Generation (RAG) mechanism. Well-prepared, technical SEO pages with structured data implemented are an efficient source of facts for it. Because relying solely on models’ internal knowledge bases can generate errors, AI systems scan the web live in search of credible documents. An organization with well-organized code makes it easier for the machine to retrieve and understand its content. The GEO strategy optimizes this process, which supports visibility metrics such as Answer Share of Voice.

Why Do Merchant Data Form the Foundation of Recommendations in the Shopping Graph?

In AI-supported commerce, product data from feeds makes it easier to communicate with systems such as Google AI Overviews and ChatGPT Shopping. The quality and completeness of the product file, precise technical attributes, and the implementation of structured data – which let AI place a product in the global Shopping Graph – have a big influence on whether an offer is recommended. Gaps in the product file can lower an offer’s chances of a recommendation, because models avoid guessing parameters on their own.

An analysis of the medical-equipment market shows that extensive descriptions often need support from structured data. Starting the text with a clear table of the equipment’s clinical purpose makes it easier for AI models to correctly cite the facility in their summaries and diagnoses for patients.

Shopping Graph: Google’s dynamic database containing information about products, brands, and reviews, linking user intent with the actual offering on the market.

For generative engines, hard technical data – such as proprietary tests, measurements, or reviews – is valuable, as it raises the domain’s expert authority. Standardizing units and feature types in the file lets AI extract the most important parameters for comparisons more efficiently. In the e-commerce industry, this means the need to support general descriptions with precise technical data placed in dedicated fields.

Which Product-Feed Attributes Most Strongly Support Citability?

Optimizing for AI tools favors the use of detailed product titles containing the brand, type, and a key technical parameter (e.g., “Columbia OutDry Extreme jacket – waterproof, men’s, L”). The product_highlight field is useful and should explicitly list benefits for specific use situations.¹ Adding information about accessories, replacements, and an FAQ section directly to the product file makes it easier for models to give comprehensive advice.

Feed attribute	Function in AI systems	Required characteristics
Title	Identifying the main entity	Brand + Model + Key Parameter
Description	Building semantic context	150-300 words, the language of benefits and applications
Product Highlight	Fast extraction of advantages	Short, technical bullet points
GTIN / MPN	Unification in the Shopping Graph	Mandatory for product recognition
Custom Attributes	Personalizing recommendations	Material, compatibility, certificates

How Does Multimodality Change the Way AI Displays Products?

Modern AI models analyze images on a par with text, which favors publishing high-quality photos with correct alt descriptions and video materials with transcripts. Generative systems more readily promote sellers who provide interactive experiences, such as virtual fitting rooms or 3D views. Combining good graphics with structured text lets models create richer answers, which consumers browse more often on mobile devices.

How Does Schema.org Structured Data Facilitate Communication with AI?

JSON-LD markup in the code is the fundamental language of communication for crawlers. This helps reduce AI’s computational costs and can speed up the selection of a brand for an answer by 73%. It provides AI systems with an organized network of information about the document, reducing communication noise. Operating with clear categories makes it easier for the system to categorize concepts, which helps avoid hallucinations from the machines.

In the construction industry, publishing instructions solely in the form of graphics can be a barrier for AI. Implementing HowTo microdata with the exact order of steps makes it easier for machines to assist contractors, more often mentioning the manufacturer’s brand in the process.

Schema.org: a universal set of markers added to a page’s code that makes it easier for crawlers (including Google’s bots) to understand the specifics of a site’s content.

Using the right markers helps integrate a brand’s identity into Google’s global knowledge network, which builds trust in the domain. Research indicates that about 92% of the domains visible in the new AI panels have entity code implemented.¹ Implementing schemas is an important element supporting search optimization for companies that care about their expert image.

Which Schema Types Are Most Important in a GEO Strategy?

In the data architecture, the Organization, Article, FAQPage, and Person schemas hold a key place. The Organization schema ties together the identity of the entire company, while Person helps secure the digital authority of experts and content authors. Article defines the authors and verification dates of posts, which signals to models that the knowledge is still current.

Structure type	Function from a machine perspective	Business benefit
FAQPage	Maps question-and-answer relationships	Makes it easier to deliver ready-made definitions for a chatbot
HowTo	Encodes processes and instructions	Positions the brand as B2B expert support
Organization	Builds a profile in the knowledge graph	Lends credibility to the company’s identity online
Person	Links content with a scientific body of work	Builds trust in the authors (supports E-E-A-T)
Product / Offer	Defines commercial parameters	Supports presence in purchasing recommendations

Why Is the Person Markup Important for the E-E-A-T Assessment?

AI analyzes content authors, and many employees who verify potential business partners use assistants. Caring about reputation encourages marking key personnel’s bios with the Person schema, which links a given text with the creator’s experience. The absence of such actions means AI may draw on information from less credible sources, which can be risky in B2B processes.

How Does the RAG Mechanism Affect Data Reading and Visibility?

Presence in AI recommendations is based on Retrieval-Augmented Generation (RAG), and Schema is a simple way to introduce a company’s organized knowledge into the chat window. Because models rarely have the latest prices built in, RAG prompts them to look up data online in real time. The system first scans the web in search of JSON-LD data and then weaves it into the answer for the customer.

Dynamic service price lists – for example at airport parking lots – can be skipped by bots if they aren’t clearly coded. Rebuilding tables into simple HTML formats often makes it easier for AI to include the offer in its recommendations.

RAG (Retrieval-Augmented Generation): a technology that prompts an AI model to look up current documents online before generating a final answer.

Well-organized code makes it easier for the machine to read facts, which reduces the risk of an assistant misinterpreting the offer. In regulated industries (finance, health), AI systems more readily rely on SEO texts that are confirmed by Schema markup. Adapting technology for RAG is now an important pillar of acquiring traffic.

Which Technical Challenges (SSR, INP, TTFB) Affect Citations?

Server stability and page speed translate into whether an AI model manages to process the data within its narrow time limit. Client-Side Rendering (CSR) with dynamic JavaScript can hide part of the code from AI bots such as GPTBot and ClaudeBot. To avoid this, pages should use Server-Side Rendering, which supports a full reading of the data.

Server speed matters a lot. With identical price lists and Schema implementations, AI models advising business customers retrieve information more often and more efficiently from a wholesaler with a faster response time (e.g., 150 ms) than from slower domains.

SSR (Server-Side Rendering): a technique of generating a page’s complete code on the server before sending it, which significantly makes it easier for crawlers to quickly access full information.

A fast server response (Time to First Byte under 200 milliseconds) translates into a visible increase in the number of citations in AI assistants. Slow sites can be pushed into the background by crawlers, because machines prefer fast data sources. Appropriate hosting helps the AI assistant manage to analyze the offer in time.

How Does INP Affect Algorithms’ Trust in a Domain?

The INP (Interaction to Next Paint) indicator is a measure of a page’s responsiveness, rigorously monitored in SEO processes. Sites that work too slowly (a reaction above 2.5 seconds) can lose visibility in the classic results and are included less often in the AI Overviews panels. Optimizing this indicator raises algorithms’ overall technical trust in the page.

Why Is Data in PDF Files a Barrier for AI?

Price lists saved in PDF files or complex infographics are hard for AI assistants to process in a short time. Interactive calculators on a site should generate results in the form of plain HTML text. It’s worth describing the calculated amounts on the page in exact text, which makes it easier for crawlers to capture these values for their summaries.

What Is the Asymmetric Strategy in robots.txt File Settings?

When managing visibility, it’s worth considering an asymmetric strategy in the configuration of the robots.txt file. It consists of blocking bots that scrape content solely to train their models (e.g., GPTBot), while preserving access for bots that crawl the web in order to provide answers to users (e.g., OAI-SearchBot). Globally blocking all AI bots limits a company’s visibility as a reference source.

Blocking Google’s core bots in the robots.txt file out of fear of content theft can lead not only to disappearing from AI assistants, but also to drastic declines in traditional organic results.

Asymmetric Strategy: controlling access for bots, consisting of restricting access for training agents while simultaneously allowing bots responsible for users’ current searches.

Understanding the function of individual bots is important for combining SEO goals with a presence in AI. Appropriate settings help protect content without cutting yourself off from new channels of reach.

Agent Name	Software	Main Function	Decision (Recommendation)
GPTBot	OpenAI	Fetches data for model training	Block (Disallow)
OAI-SearchBot	OpenAI	Searches for facts to cite live	Allow (Allow)
ClaudeBot	Anthropic	Fetches articles to train models	Block (Disallow)
PerplexityBot	Perplexity	Visits pages to answer questions	Allow (Allow)
Googlebot	Google	Responsible for SEO and AI Overviews	Allow (Allow)
Google-Extended	Google	Fetches data to improve the Gemini model	Block (Disallow)

What Is Stealth Crawling and Why Are Text Files Sometimes Insufficient?

Stealth crawling is the activity of bots that falsify their headers to impersonate ordinary users and bypass the prohibitions in robots.txt. It has been observed that some platforms can make millions of requests while disguising themselves as popular browsers. Protecting against such traffic often requires the support of application firewalls (WAF), which analyze the actual behavior of scripts.

Protection Against Data-Aggregating Bots

Bots such as CCBot massively collect the resources of websites and then make them available to a wide group of developers. A lack of appropriate safeguards means that the content you create becomes widely available in analytical databases. Blocking such tools is one of the steps in protecting a company’s intellectual property.

Which New KPIs Are Worth Basing Marketing Analytics On?

Managing marketing today encourages expanding analyses to include the degree to which a brand is suggested by assistants. New, useful metrics include Answer Share of Voice (ASOV) and an analysis of the impact of such recommendations on the cost of acquiring business contacts. Visibility in these channels is often a good predictor of customer engagement in the sales funnel.

Combining classic SEO with orderly price lists in JSON-LD format makes it easier for B2B brands to reach decision-makers who take their first research steps in artificial-intelligence interfaces, thereby increasing their share of citations.

ASOV (Answer Share of Voice): a metric that determines how often a brand appears in AI summaries for key queries in a given industry.

The AI Citation Rate indicator is also useful; it determines the number of citations obtained from AI answers. The Position-Adjusted Word Count (PAWC) ratio analyzes the size and nature of a mention of the company. In turn, GEO-Adjusted CPL helps estimate the cost of acquiring a lead while taking into account the outlays on the technical optimization of servers.

Metric (KPI)	What does it concern?	Why is it important in business?
ASOV	Percentage of recommendations by algorithms	Predicts brand share in the modern B2B funnel
AI Citation Rate	Number of citations obtained	Confirms the substantive effectiveness of the content
PAWC	Size and quality of the mention	Assesses the informational value of the recommendation
GEO-Adjusted CPL	Customer cost taking GEO into account	Helps calculate the profitability of optimization
QQ Velocity	Decision time from the query	Measures the efficiency of customer education before contact

How Is the Tone of Answers in Chatbots Monitored?

The Citation Sentiment metric analyzes the tone (positive, neutral, negative) of machines’ statements about the company in the summaries they are asked to produce. This makes it possible to identify descriptor drift, when a model begins to link a brand with outdated opinions from external forums. Regularly monitoring this aspect supports care for the brand image.

The Impact of AI Education on Sales Processes

Traffic coming from recommendations in chat is often characterized by higher purchase intent, which supports the work of salespeople. This can be measured with the Time-to-Outcome Reduction indicator, which analyzes the time saved in the negotiation cycle. A customer arriving from an assistant’s recommendation is often already substantively prepared for the conversation.

How Do Usage Costs Help the Automotive Sector With AI Assistants?

The total cost of ownership of a vehicle (TCO) is one of the knowledge formats that algorithms readily process for users. Breakdowns of spending on fuel, depreciation, or servicing build an article’s substantive position in the process of evaluation by bots. Automotive brands support their presence in search engines by providing clear calculations of operating costs.

In the dealership sector, rebuilding traditional analyses into transparent tables with leasing installments and cost estimates makes virtual advisors more willing to cite such pages as concrete pricing sources, which generates interest among business customers.

TCO (Total Cost of Ownership): a complete breakdown of costs, covering purchase, servicing, fuel, insurance, and depreciation over a given period.

Domains offering numerous mathematical breakdowns and taking current regulations into account are often preferred by algorithms during the RAG process. Phrases such as “based on an analysis of 1,200 cars in the 2023-2025 period” constitute a good substantive reference. Updating such reports helps maintain expert status.

What Are So-Called “Answer Nuggets” and How Should They Be Prepared?

Bots handle concise blocks of knowledge, called answer nuggets, well. An example is a concrete value: “The estimated TCO cost for the Volvo XC60 model in 2025 is 1.16 PLN/km.” Such information is easy for the system to cite. In accordance with good practices for creating substantive content, it’s worth supplementing the amounts given with appropriate context and justification.

The Role of Hyper-Locality in AI Summaries

Assistants can take local realities into account in their calculations, such as parking costs or the requirements of Clean Transport Zones in a given city. A concise explanation of how “local regulations for BEV vehicles reduce a fleet’s expenses in Kraków by a certain amount” makes it possible to build a very useful answer, which favors a higher rating of the material by bots.

How to Adapt a GEO Strategy to Company Size?

The optimization process looks somewhat different in small companies than in corporations based on Headless CMS systems. While fighting for an established SEO position, companies can simultaneously optimize their structures for AI assistants. This is an iterative activity in which every improvement in code readability makes it easier for artificial intelligence to scan data.

Even excellently prepared answer-first content will not work fully if old rules in robots.txt block bots’ access. Only a technical audit and the removal of blocks on the server opens the way to queries from generative channels alongside standard SEO traffic.

NAP: a set of information about a company – Name, Address, Phone. The consistency of this data across various platforms makes it easier for artificial intelligence to attribute it flawlessly to a single entity.

Smaller enterprises often focus on the consistency of FAQPage plugins, Organization Schema, and the uniformity of NAP data. Medium-sized companies build authority by introducing confirmed evidence (e.g., external footnotes). Large-scale organizations, in turn, use APIs and entity architecture to efficiently export consistent data directly to browser environments.

Reach and Size	Primary Point of GEO Changes	Support Tools	Analysis Metrics
SMB (Smaller)	Consistency of the NAP and contact database	CMS systems, Schema plugins	Visibility in the Local area
Medium Business	Empirical data and evidence	Semrush AI Toolkit, SSR	Citation frequency
Corporation	Centralization and Entity Graphs	Headless CMS, fast API	ASOV share in the segment

What Is the IndexNow Protocol and Why Does It Facilitate Data Updates?

The IndexNow protocol is a technology that allows search engines to be notified directly of the publication of new content or a change in price lists via API. This mechanism speeds up the recording of changes, which is useful in systems that require fresh information. Taking care of data currency helps make a brand resilient to situations in which an AI model would recommend an outdated offer.

How Does the Front-Loading Principle Support Analytical Bots?

Statistics indicate that about 44.2% of the citations most frequently used by assistants are found in the first 30% of the length of the analyzed texts. The front-loading strategy consists of placing specific, most important conclusions and results at the very beginning of a publication, preferably in a question-and-answer format (QAE). Such an arrangement allows algorithms to find and use the necessary data more quickly.

What Is the Future of Marketing on the Threshold of E-Commerce 2028?

It is expected that by 2028 a noticeable portion of purchase suggestions will be formulated by AI algorithms combining a user’s profile with merchant data. The B2B market may develop toward agentic commerce, where virtual assistants will help entrepreneurs compare offers and communicate via APIs. This encourages building articles in a way that is friendly both to the reader and to LLM queries.

In the B2B tourism industry, a hybrid strategy – consisting of maintaining SEO-optimized blogs and adding fixed trip programs in FAQ format for AI – secures traditional organic traffic while also making it easier to cite the brand in the summaries of assistants advising companies.

Agentic Commerce: a support model in which intelligent software helps analyze, select, or even negotiate purchases based on previously defined criteria.

The development of Visual Share of Voice (vSOV) technology, which supports the analysis of logo exposure in video materials with great precision, is also progressing. Entities that effectively integrate their SEO texts and tabular data with the new readability standards for AI can gain interesting opportunities in the evolving e-commerce market.

FAQ

Schema structured data provides machines with a clear structure of information, which can speed up the selection of documents by bots by as much as 73%.

Content marked with correct JSON-LD records increases in presence in summaries, and over 92% of domains appearing above classic results have correct code.

A lack of microdata prompts AI models to guess the meaning of the text, which slightly increases the risk of the brand being omitted from the generated recommendation.

The asymmetric strategy consists of blocking bots that massively fetch data for training, while simultaneously allowing bots that search for information live for the user.

This makes it possible to maintain control over the company’s resources while simultaneously supporting visibility in conversations with artificial-intelligence assistants.

Globally cutting off all AI bots limits a brand’s access to purchasing processes and can negatively affect current organic SEO traffic.

ASOV measures a brand’s share in artificial-intelligence answers to queries that are strategic for the company.

A company exceeding the 30% threshold of presence in assistants’ recommendations can support sales growth.

This indicator complements the analysis of classic SEO positions, making it easier to determine the combined strength of a brand in the modern search ecosystem.

SSR (Server-Side Rendering) provides bots with fully assembled code, whereas loading content on the browser side (CSR) can hide key parameters from many AI bots.

Because some bots fetch raw data without running scripts, using SSR makes it easier for assistants to read prices and product features flawlessly.

This is currently a useful method that complements classic SEO efforts and supports the operation of microdata.

Answer-First encourages placing short, ready-made definitions at the beginning of texts, which harmonizes with the way LLM models summarize materials.

About 44.2% of the fragments cited in generative models are found in the initial 30% of the volume of the analyzed document.

Creating strong, specific introductions of 40-60 words raises the chance of them being used as a reliable source.

Nuggets are coherent blocks of information containing hard values and numbers, arranged in a way that is easy to use in machine summaries.

Providing a precise parameter, e.g., “TCO in 2025: 1.16 PLN/km”, gives algorithms ready analytical data to use in chat.

Using this format strengthens the perception of a domain as an expert and a reliable source in B2B analyses.

The AI Citation Rate determines the number of a model’s references to content from a given domain when answering advisory queries.

Brands correctly indicated in these summaries often record a noticeable increase in clicks compared to brands that are cited less often.

A presence in the listing builds trust, prompting users to go to the site and deepen their knowledge.

Discrepancies in the NAP format (Name, Address, Phone) across various profiles can be misleading for verifying algorithms, which may hold the system back from a confident recommendation.

For AI systems, identical addresses are a signal of the authenticity of a service establishment.

Inconsistent data in business listings can make it harder for assistants to effectively recommend contractors’ services in a given region.

The IndexNow protocol automates notifying systems about the freshest publications and modifications via API.

Thanks to this, changes can be visible much faster, which favors presenting current promotions and price lists in AI chat.

Using this solution boosts the technical assessment of a site’s credibility while assistants are acquiring knowledge.

Descriptor drift is a situation in which algorithms begin to associate a brand name with outdated or unwanted discussion topics being conducted online.

Monitoring the Citation Sentiment indicator makes it easier to catch such changes and allows for a relatively quick publication of corrections.

Maintaining precise data with Schema supports the correct interpretation of a brand’s profile by bots.

It is indicated that by 2028 as much as half of purchase suggestions may come from RAG systems, based directly on the databases from a store’s product file.

The phenomenon of multimodality will progress, i.e., the simultaneous analysis of video, photos, and structured descriptions by assistants in real time.

The development of tools supporting e-commerce may ultimately allow for more efficient support of negotiations in the background through algorithms.

Basing your own articles on verified statistics can noticeably raise the informational value of the material in the eyes of language models.

Bots look for hard numerical values from which a user can more easily draw conclusions, often ignoring general messages.

Supporting an argument with a specific amount or dimension increases the likelihood of a given text being used as a reference.

Optimization techniques for artificial intelligence are a natural complement to standard SEO, which still takes care of a site’s strong baseline position.

These approaches do not exclude each other, but rather support one another, building solid visibility both on traditional lists and in assistants’ windows.

Such combined action allows an enterprise to function more efficiently in the market.