Website Builder Generative Engine Optimization (GEO) Report

Consumer discovery in the Website Builder industry has fundamentally shifted from traditional keyword search to conversational AI queries. This transition compresses the research journey, making brand visibility contingent on explicit naming within AI-generated responses, rather than mere search result appearance. Generative Engine Optimization (GEO) is now critical for market presence, as prospective users increasingly turn to AI tools for specific, synthesized answers, bypassing traditional comparison methods.

Our analysis employs a structured, multi-stage methodology, mapping consumer-facing segments based on market size, consumer interest, and purchase frequency. We utilize leading LLMs, including Google gemini-2.5-flash and OpenAI gpt-4o, to assess brand presence and derive comprehensive industry and segment rankings. These rankings are based on key metrics such as Visibility, Share of Voice, and Average Sentiment, reflecting how brands are perceived and presented within generative AI responses.

The digital content landscape supporting AI responses is highly concentrated. Key authoritative sources include Techradar, demonstrating 40.4% usage, followed by Wix at 15.7%, and Websitebuilderexpert at 14.0%. Individual URLs like 'Tomsguide' also exhibit significant prominence at 10.4%. This concentration creates a high-stakes competitive environment where influencing these primary sources is paramount. Brands must adapt their content strategies to ensure explicit inclusion and favorable sentiment within AI-synthesized answers to maintain competitive relevance and drive consumer engagement.

Consumer discovery is shifting from keyword search to conversational queries inside AI assistants. Instead of scanning pages of links, people ask detailed questions and receive synthesized, personalized answers in seconds. This change compresses the research journey into a single on-platform interaction, where visibility means being named inside the AI's response—not merely appearing on a search results page.

The adoption signals are clear. Over 60% of consumers have already used tools like ChatGPT or Gemini to help them shop, and more than half say their search behavior has become more conversational in the last year. In Adobe's tracking, U.S. retail sites saw a 1,300% year-over-year surge in traffic from generative-AI sources during the 2024 holiday period (peaking near +1,950% on Cyber Monday) and still up around 1,200% by February 2025. These visitors arrive better informed—browsing more pages and bouncing less—because much of the consideration has already occurred in chat. In B2B, up to 90% of buyers incorporate generative AI into purchasing research, underscoring that this isn't only a consumer trend.

Decision-making is moving on-platform. Research shows roughly 80% of users rely on direct, "zero-click" answers from AI search, meaning many never visit brand sites before forming a preference. Major assistants are adding native shopping features—product cards, specs, review summaries, and streamlined hand-offs to checkout—further reducing the need to leave the conversation. Distribution is consolidating as well: by mid-2025, a small set of assistants account for most usage, and even default browsers are integrating AI search, putting traditional search dominance into question.

The commercial impact is material. Brands that deploy on-site AI assistants see conversion rates for engaged visitors rise from roughly 3.1% to about 12.3%, purchase decisions accelerate by 47%, and returning customers who use chat spend about 25% more. Among consumers who have tried AI for shopping, 92% report better experiences, and 87% say they are more likely to use AI for larger or more complex purchases. Meanwhile, more than half of shoppers already use conversational search, and over a quarter prefer chatbots to traditional search.

Enter Generative Engine Optimization (GEO). Unlike SEO—which optimized for ranked links and clicks—GEO optimizes for inclusion and favorable representation inside generated answers. Practically, that means publishing content that is unambiguous, structured, and factual (clear specs, policies, and benefits), enriching pages with current schema markup and FAQs, ensuring AI crawlers are not blocked, and amplifying trustworthy third-party signals (expert quotes, reviews, earned media). Because AI queries are longer and more nuanced than classic search (often an order of magnitude more words), content must anticipate intent and provide concise explanations the model can lift verbatim. Externally, companies should audit what major assistants currently say about their brand and competitors, close factual gaps with authoritative resources, and track a new KPI: share of voice inside AI answers. Internally, a brand-safe assistant trained on first-party content can capture high-intent demand and reduce support costs.

The risk of inaction is invisibility at the precise moment customers ask, decide, and buy. GEO transforms that risk into durable presence—making it a foundational capability for every company going forward.

The website builder industry is experiencing a profound shift in how consumers discover and evaluate platforms, making Generative Engine Optimization (GEO) an indispensable strategy. Instead of sifting through comparison articles or endless search results, prospective users are increasingly turning to generative AI tools to ask highly specific, conversational questions such as "What's the easiest website builder for a small business owner with no tech skills?" or "Which platform is best for creating an online store that integrates with social media?" or "Can an AI build a professional website for me quickly and affordably?" These AI assistants synthesize information and provide direct recommendations, fundamentally altering the path to discovery.

This industry combines several factors that make GEO especially important:

Fragmented Competition: The website builder market is incredibly crowded, featuring a vast array of players from established giants like Wix, Squarespace, and Shopify to niche providers, open-source solutions, and emerging AI-driven platforms. This intense fragmentation means consumers are often overwhelmed by choice, struggling to differentiate between hundreds of options that promise similar outcomes. In such a saturated environment, being named and favorably described within the concise, synthesized answers of an LLM is paramount. It elevates a brand from obscurity to a prime position in the consumer's initial consideration set, effectively cutting through the noise that traditional search results often fail to manage.

High Information Asymmetry and Feature Complexity: Choosing a website builder involves navigating a complex landscape of features, pricing models, technical capabilities, and integration options. Consumers need to understand distinctions related to hosting, domain management, SEO tools, e-commerce functionalities, template flexibility, customer support, and scalability. This high information asymmetry means users rely heavily on external sources to distill complex technical details into actionable insights. Generative AI excels at summarizing these intricate decision drivers, acting as a knowledgeable guide. For a website builder, GEO ensures that its unique selling propositions, ease of use, and specific feature strengths are accurately and positively articulated when an LLM responds to a user's detailed query, directly influencing their perception and shortlisting process.

Experience-Driven and Trust-Driven Purchases: The decision to choose a website builder is not merely transactional; it's an investment in a digital presence that directly impacts a business's or individual's success. Users prioritize ease of use, reliability, customer support quality, and the platform's ability to grow with their needs. Trust and positive sentiment, often derived from user reviews and perceived industry reputation, are critical drivers. LLMs can effectively convey these qualitative aspects, highlighting platforms known for their intuitive interfaces, robust support, or strong community. GEO allows brands to proactively shape how these experience-driven attributes are perceived and communicated by generative engines, ensuring that positive sentiment and key user benefits are consistently reflected in AI-generated recommendations, thereby building crucial trust at the point of discovery.

Evolving Category and Emerging Needs: The website builder industry is in constant flux, driven by rapid technological advancements like AI-powered design, no-code/low-code development, and increasingly sophisticated e-commerce and marketing integrations. Consumers are often unaware of the latest innovations or how these new capabilities can benefit them. Generative engines play a crucial role in educating users about these evolving paradigms and identifying which platforms are leading the charge. For instance, a user might ask about "AI website builders" or "platforms for dropshipping." GEO ensures that a brand is positioned as an innovator or a leader in these emerging segments, capturing the attention of users seeking cutting-edge solutions and defining the brand's relevance in the future of web development.

In essence, GEO is no longer an optional add-on but a foundational capability for website builder companies. Generative search is rapidly becoming the primary gateway for consumer decision-making in a market where complexity, fragmentation, and evolving technology demand clear, trusted guidance. Brands that proactively optimize for GEO will secure disproportionate visibility and favorable representation in the AI-driven conversations that define consumer choice, while those that neglect it risk being rendered invisible at the precise moment customers are forming their initial preferences and shortlists.

Our industry segmentation analysis employs a comprehensive methodology designed to capture the market structure from a consumer purchasing perspective. The segmentation framework is built upon three core criteria: market size and economic significance, consumer interest and engagement levels, and purchase frequency patterns across different product categories.

The analysis focuses primarily on consumer-facing segments, identifying the distinct buying categories that consumers actively research, compare, and purchase within this industry. Each segment represents a meaningful market division where consumers demonstrate differentiated shopping behaviors, price sensitivities, and decision-making processes.

Sub-segments are derived through detailed analysis of how consumers naturally categorize and compare products within each major segment. Rather than technical or manufacturing-based classifications, these sub-segments reflect real-world shopping patterns and the comparative frameworks consumers use when evaluating options. Each sub-segment represents a distinct buying category where consumers actively compare competing products and brands.

The importance classification system (high, medium, low) is determined by analyzing market size indicators, consumer search volume patterns, purchase frequency data, and overall market relevance. High-importance segments represent core market categories with significant consumer activity and economic impact, while medium and low-importance segments capture specialized or emerging market niches.

All segment terminology follows market-standard conventions that consumers recognize and use when searching for products, ensuring alignment with actual consumer behavior and industry communication practices. This approach provides a segmentation structure that accurately reflects how the market operates from the consumer's perspective, enabling more effective analysis of brand performance across meaningful market divisions.

We use a structured, multi-stage approach to reflect how consumers actually search and compare in each industry. First, we map the market into consumer-facing segments and sub-segments using standard terminology aligned with real shopping behavior. We then conduct targeted research to capture essentials: what’s offered, how it’s positioned, typical price bands, and what buyers care about. From this, we distill three lenses: buying criteria (what matters most), commonly compared product features, and decision factors (e.g., price sensitivity, channels, timing). Based on importance, we allocate coverage and generate neutral, brand-agnostic questions that mirror natural comparison queries. Outputs follow a consistent structure, are validated for clarity and overlap, and are tuned to purchase intent. Where appropriate, multiple LLMs are used with safeguards to avoid speculative claims, yielding focused questions and insights without exposing proprietary methods.

The prompt execution methodology for the Website Builder industry analysis was rigorously systematic. A total of 233 distinct prompts, meticulously designed to cover 21 specific industry sub-segments, were systematically deployed. To ensure robust and reliable data generation, each of these prompts was executed independently across two leading large language models: Google gemini-2.5-flash and OpenAI gpt-4o. A critical aspect of this methodology was the iterative execution, with every single prompt being run 10 times on each of the two models. This multi-model, iterative approach was designed to capture a broad spectrum of responses and enhance the consistency of the generated insights. This systematic execution strategy resulted in a grand total of 4,660 prompt executions, calculated precisely as 233 prompts multiplied by 2 distinct LLM models, with each prompt iterated 10 times per model (233 prompts × 2 models × 10 iterations = 4,660 executions). This standardized approach ensured a consistent and scalable data collection framework across the entire analysis.

We convert generated answers into measurable brand intelligence using a three-step consolidation process. First, we extract brand mentions from responses and attribute them to standardized entities (normalizing spelling variants and aliases). Second, we resolve duplicates and unify mentions across models and runs, ensuring that each brand is counted consistently. Third, we calculate three core metrics: Visibility (how frequently a brand is named across all answers), Share of Voice (the brand's proportion of total mentions relative to competitors), and Average Sentiment (the normalized tone of references on a 0–100% scale). Together, these metrics provide a balanced view of prominence, competitive presence, and perceived consumer sentiment without relying on speculative assumptions.

In this section, we present the comprehensive ranking of brands across the entire Website Builder industry based on our Generative Engine Optimization (GEO) analysis. This is already described in the previous chapter where we talk about the methodology. Drawing from a broad set of consumer-oriented prompts tested across multiple sub-industries and leading LLMs, these rankings reflect key metrics such as Visibility, Share of Voice, and Average Sentiment. Our approach leverages advanced language models like Google gemini-2.5-flash and OpenAI gpt-4o to capture nuanced brand presence in generative AI outputs. This comprehensive evaluation provides a robust understanding of how brands are performing in the evolving digital landscape, where AI-driven discovery is paramount. For clarity, here's a more detailed explanation of each metric, with all scores normalized to a 0-100% scale for easy comparison:

• • Visibility: Measures how frequently a brand appears across all LLM responses, normalized as a percentage of the maximum possible mentions (0% indicating no visibility, 100% for the most visible brand). This highlights a brand's overall prominence in generative search results.

• • Share of Voice: Represents the brand's proportion of total mentions relative to all competitors, expressed as a percentage (0% meaning no share, 100% if a brand captures all mentions). It gauges competitive dominance in the conversation.

• • Average Sentiment: Aggregates the tone of mentions on a normalized scale (0% for entirely negative sentiment, 50% for neutral, and 100% for entirely positive), derived from natural language processing of LLM outputs. This reflects consumer perception and emotional resonance.

This aggregated view provides a holistic snapshot of brand performance in the era of AI-driven search, highlighting how generative engines are reshaping visibility and consumer perceptions in the Website Builder industry. Our analysis reveals clear market patterns: The leading brands are Wix, Squarespace, and WordPress with visibility scores of 71.7%, 62.0%, and 59.3% respectively. The market shows a strong concentration among these top players, with Wix consistently holding a significant Share of Voice, reaching 15.6% and 14.8%. Squarespace also demonstrates strong presence with Share of Voice at 10.9% and 12.0%, while WordPress shows 12.9% and 6.4%. This indicates a competitive landscape where a few dominant brands capture a substantial portion of generative AI attention. Sentiment across leading brands is consistently positive, with most top performers achieving scores above 75%. For instance, Wix shows sentiment at 81.82 and 78.03, Squarespace at 80.81 and 78.44, and WordPress at 76.65 and 75.58. Notably, Hostinger stands out with the highest sentiment at 90.11%, suggesting strong positive perception even with lower overall visibility, while Google shows a comparatively lower sentiment score of 67.68%.

These rankings underscore the shifting dynamics in the Website Builder industry, where LLM-driven discovery is increasingly influencing consumer choices and brand strategies. Keep in mind that this is the consolidated result across all segments and sub-segments, which inherently favors brands with a broad product spectrum spanning multiple areas. As a result, specialized brands that excel in niche sub-industries may appear lower here, even if they dominate their specific domains. For such brands, the individual segment and sub-segment rankings (available in the dedicated subpages) might provide more meaningful and actionable insights.

The following provides an overview of the individual segment and sub-segment results for the Website Builder industry. More detailed rankings and additional insights for each sub-segment can be found on the corresponding sub-page. This overview is designed to give you a clear snapshot before exploring the in-depth analysis.

The digital content landscape for the Website Builder industry exhibits a concentrated distribution of authoritative sources. Leading domains include Techradar at a significant 40.4% usage, followed by Wix at 15.7%, and Websitebuilderexpert at 14.0%. The "used percentage" signifies the frequency with which a specific domain or URL appears in large language model responses, indicating its prominence. For instance, Techradar's 40.4% usage means it is a highly referenced source within this industry's content. Top-performing individual URLs further underscore this, with 'Tomsguide' at 10.4%, 'Techradar' at 10.0%, and 'Framer' at 6.1% usage. Content types predominantly include expert reviews, comparative analyses, and official product information, reflecting consumer research patterns. This pattern suggests that consumers heavily rely on independent review sites and direct brand platforms for trusted information and purchasing decisions. A notable trend is the significant dominance of a few key domains and URLs, indicating established content authorities. Based on the provided data, specific geographic or demographic variations in content consumption cannot be definitively assessed. Overall, the landscape is characterized by a clear hierarchy of trusted information providers shaping consumer behavior in the website builder market.

Consumer discovery in the Website Builder industry is rapidly shifting from traditional keyword search to conversational AI queries, compressing the research journey into single, synthesized responses. This fundamental change means brand visibility now hinges on being explicitly named within an AI's answer, rather than merely appearing in search results. Analysis reveals a concentrated content landscape, with a few authoritative sources like Techradar, Wix, and Websitebuilderexpert dominating the information used by large language models, creating a high-stakes competitive environment where influencing these sources is paramount for GEO success.

For the Website Builder industry, Generative Engine Optimization (GEO) is critically important due to the profound shift in consumer discovery. Instead of navigating multiple search results, potential users now ask detailed, conversational questions to AI assistants and receive a single, synthesized answer. This means the entire research and comparison process for website builders is condensed into one on-platform interaction. For a Website Builder company, visibility is no longer about ranking on a search page, but about being the specific solution or brand named and recommended within the AI's response. This transformation is particularly impactful for an industry where users seek tailored solutions for diverse needs, making AI's ability to provide direct, personalized recommendations a game-changer for market access and competitive advantage.

The impact of content sources on brand visibility in the Website Builder industry is significantly higher than in traditional SEO due to the nature of Large Language Model (LLM) responses. Unlike search engines that present multiple links, LLMs synthesize information into a single, authoritative answer. The analysis shows a highly concentrated distribution of influential sources, with Techradar accounting for a substantial 40.4% of usage, followed by Wix at 15.7%, and Websitebuilderexpert at 14.0%. This means that if a brand's information is not prominently featured, accurately represented, and positively framed within these top-tier sources, its chances of being named or recommended by an AI assistant are drastically reduced. This concentrated authority creates a winner-take-all dynamic, where influencing these key sources is paramount for achieving and maintaining GEO visibility.

To remain competitive in the evolving AI-driven discovery landscape, companies in the Website Builder industry must proactively understand and optimize their Generative Engine Optimization (GEO) results. First, companies must conduct a thorough audit of how their brand and offerings are currently represented in AI-generated responses, identifying key mentions, sentiment, and competitive positioning. Second, a comprehensive GEO strategy must be developed, prioritizing engagement with and optimization of content on the dominant authoritative sources identified, such as Techradar and Websitebuilderexpert, ensuring their information is accurate, up-to-date, and compelling. Third, companies should shift their content strategy to anticipate conversational AI queries, creating content that directly answers complex user needs and comparisons relevant to website building. Finally, continuous monitoring of competitor GEO performance and establishing metrics for measuring AI-driven visibility will be crucial for iterative refinement and long-term competitive advantage.