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Top 15 Best Google Analytics Alternatives in 2026
Google Analytics is still widely used, but it is not the best analytics tool for every website, SaaS product, or startup. For a solo founder, a dashboard filled with dozens of reports can make a simple question harder to answer: Where are visitors coming from, what are they doing, and which pages or campaigns are producing results? For SaaS teams, the problem is different. Website traffic alone does not tell you whether visitors sign up, activate a feature, return later, or become paying customers. Product teams often need funnels, retention, cohorts, session replay, feature usage, and user journeys. Privacy is another reason companies look for Google Analytics alternatives. Some businesses want cookie-free analytics, EU hosting, self-hosting, or complete ownership of their analytics data. > QUICK SUMMARY > > * Plausible Analytics: A simple privacy-focused Google Analytics alternative with a clean dashboard, goals, custom events, Google Analytics import, funnels, user journeys, and revenue attribution on higher plans. > > * Fathom Analytics: A cookie-free analytics platform focused on making website traffic, campaigns, conversions, and revenue easier to understand without adding a complicated analytics setup. > > * Simple Analytics: A privacy-friendly analytics tool with a simple dashboard, no cookies, no personal data collection, EU hosting, and a free plan for hobby websites. > > * Matomo Analytics: A deeper Google Analytics alternative that supports website and ecommerce analytics, event tracking, funnels, heatmaps, session recording, attribution, and self-hosting. > > * PostHog: A strong choice for SaaS and product teams because analytics sits alongside session replay, feature flags, experiments, error tracking, surveys, data pipelines, and other product-development tools. > > Mixpanel, Amplitude, Heap, and Cromojo are better suited for SaaS and product analytics. GOOGLE ANALYTICS ALTERNATIVES COMPARISON | Tool | Key features | Minimum price | Best for | Free plan | | | | :| | | | Plausible Analytics | Privacy-friendly analytics, goals, custom events, Google Analytics import, funnels, journeys, revenue attribution | $9/month | Founders, blogs, SaaS websites, privacy-focused businesses | Trial | | Fathom Analytics | Cookie-free analytics, events, revenue tracking, UTM tracking, reports, API, bot filtering | $15/month | Small businesses, SaaS sites, agencies | 7-day trial | | Simple Analytics | No cookies, no personal data, campaign comparison, API, BI access | Free | Privacy-focused websites and small teams | ✓ | | Matomo Analytics | Events, ecommerce, funnels, heatmaps, session recording, attribution, self-hosting | Free self-hosted | Businesses needing deep analytics and data ownership | ✓ | | Umami Analytics | Lightweight analytics, events, API, MCP, funnels, heatmaps, session replay | Free | Developers, startups, self-hosted projects | ✓ | | Pirsch Analytics | Events, goals, session analysis, API/SDKs, funnels, segmentation, A/B testing | $6/month | Developers, SaaS founders, privacy-focused sites | 30-day trial | | GoatCounter | Open source, page views, referrers, browser/backend tracking, data export | Free | Personal sites, developers, small websites | ✓ | | PostHog | Product analytics, session replay, feature flags, experiments, error tracking, surveys | Free | SaaS founders and product teams | ✓ | | Mixpanel | Funnels, retention, cohorts, session replay, heatmaps, experiments, feature flags | Free | SaaS, mobile apps, product teams | ✓ | | Amplitude | Product analytics, retention, funnels, cohorts, session replay, experimentation, AI analysis | Free | Product-led SaaS and growth teams | ✓ | | Heap | Automatic event capture, funnels, journeys, retention, heatmaps, session replay | Free | Product teams that want automatic behavioral data | ✓ | | Microsoft Clarity | Session recordings, heatmaps, AI summaries, AI chat, behavior insights | Free | Founders wanting visitor behavior analysis | ✓ | | Cloudflare Web Analytics | Page views, visitors, performance metrics, filters, privacy-focused RUM | Free | Developers and lightweight websites | ✓ | | Cromojo | Analytics, revenue attribution, funnels, SEO indexing, monitoring, broken-link detection | Free | Solo founders, SEO-focused businesses, growing websites | ✓ | | Adobe Analytics | Advanced segmentation, journey analysis, attribution, governance, cross-channel analytics | Custom | Large enterprises and complex marketing teams | ✗ | 1. PLAUSIBLE ANALYTICS Plausible Analytics is one of the strongest choices for people who want to replace Google Analytics with something easier to understand. The main difference is the interface. Instead of forcing founders to dig through layers of reports, Plausible focuses on the metrics most website owners regularly need, including visitors, traffic sources, pages, campaigns, goals, and conversions. Plausible also focuses heavily on privacy. Its analytics does not rely on cookies or personal data for its standard website analytics, making it useful for businesses that want a simpler privacy-focused measurement setup. For SaaS founders, the platform goes beyond basic page views. Custom events, goals, segments, funnels, user journeys, and revenue attribution can help connect marketing traffic with actions that matter to the business. Google Analytics data can also be imported, which can make migration easier. Best for: SaaS websites, blogs, startups, agencies, and founders who want clean website analytics without the complexity of GA4. PRICING From $9/month, with higher plans adding more sites, team members, funnels, user journeys, revenue attribution, API access, and other features. 2. FATHOM ANALYTICS Fathom Analytics is designed around a simple question: what is happening on the website, and which traffic is actually converting? Its dashboard focuses on website traffic, referrals, pages, campaigns, events, and conversions rather than exposing users to an enormous collection of reports. Fathom is also built around privacy. Its website promotes analytics without cookies and says its platform does not require cookie banners for its standard tracking. It also includes automatic bot filtering, which can help keep traffic reports cleaner. Fathom Analytics For founders, the event and revenue tracking features are particularly useful. A SaaS business can measure actions such as sign-up clicks, demo requests, purchases, or other conversion events without building a complicated analytics reporting system. Best for: Founders, small businesses, agencies, SaaS websites, and companies that want simple privacy-focused website analytics. PRICING From $15/month for up to 100,000 monthly pageviews. Every plan includes 50 sites, event and revenue tracking, UTM tracking, API access, reports, and other core analytics features. 3. SIMPLE ANALYTICS Simple Analytics takes the minimal analytics approach even further. The platform provides a simple dashboard designed to answer basic traffic questions without requiring a complicated analytics configuration. It does not use cookies, consent banners, or personal data for its standard analytics setup. One useful feature for businesses comparing analytics systems is its ability to run alongside GA4. Simple Analytics provides campaign comparisons and can help businesses identify traffic that GA4 may not measure when visitors reject consent. Developers and technical teams can also use its API and BI access to move analytics data into existing reporting workflows. This makes it more useful for teams that want a simple front-end dashboard while retaining the ability to work with their own data. Best for: Privacy-conscious founders, small businesses, developers, blogs, and websites that need straightforward traffic analytics. PRICING Free plan available, while the self-serve paid plan starts at £20/month for 100,000 pageviews. 4. MATOMO ANALYTICS Matomo Analytics is one of the closest alternatives to Google Analytics for businesses that still need a broad set of analytics features. Unlike lightweight tools, Matomo supports detailed website analytics, event tracking, ecommerce analytics, funnels, user flows, heatmaps, session recordings, attribution, and other advanced capabilities. The biggest reason companies choose Matomo is data ownership. Matomo can be deployed on the company's own infrastructure, allowing technical teams to control where analytics data is stored. That makes it particularly interesting for businesses with strict data requirements or organizations that want a deeper analytics platform without handing all analytics infrastructure to a third-party cloud provider. Best for: Businesses moving away from GA4 but still needing detailed web analytics, ecommerce tracking, reporting, and data ownership. PRICING Free forever for the Community self-hosted version. Matomo Cloud and commercial self-hosted packages have paid plans based on traffic, users, and features. 5. UMAMI ANALYTICS Umami Analytics is an open-source analytics platform designed for developers who want lightweight website analytics without unnecessary complexity. Its hosted platform includes website analytics, event tracking, API access, and other features while maintaining a relatively simple interface. The self-hosting option is especially attractive to developers. A technical founder can deploy Umami on infrastructure they control instead of depending entirely on a hosted analytics provider. Its paid cloud plans add features such as more websites, more team members, longer data retention, API access, session replay, heatmaps, and other capabilities. Umami Best for: Developers, indie hackers, technical founders, open-source projects, and businesses that want simple analytics with self-hosting options. PRICING Free, with the Hobby plan supporting up to 100,000 events per month. The hosted Pro plan starts at $20/month. 6. PIRSCH ANALYTICS Pirsch Analytics is another privacy-focused analytics platform aimed at people who want more control over their analytics data. It provides website statistics, events, conversion goals, session analysis, URL shortening, APIs, SDKs, and Google Analytics import. The platform also supports GDPR compliance and data ownership. For developers, the API and SDK support can be useful when analytics needs to connect with an application rather than just a marketing website. The higher tier adds features such as funnels, segmentation, A/B testing, custom domains, teams, and white labeling, making it more suitable for agencies and growing SaaS teams. Best for: Developers, privacy-focused SaaS companies, agencies, and founders who want both simple analytics and developer integrations. PRICING From $6/month for 10,000 monthly pageviews. 7. GOATCOUNTER GoatCounter is one of the simplest options on this list. It is open source and designed specifically for people who do not need the enormous feature set of Google Analytics or Matomo. The interface focuses on essential website statistics while keeping the underlying system straightforward. Developers can integrate GoatCounter using JavaScript, an image-based tracker, backend middleware, or log-file parsing. This gives technical users several ways to collect data depending on how their website is built. GoatCounter also allows users to export their data and self-host the software. Its hosted service is currently offered free for reasonable public usage, while very high-volume users are encouraged to self-host. Best for: Developers, personal websites, open-source projects, blogs, and small websites that need basic traffic measurement. PRICING Free for reasonable public usage. Self-hosting is available for higher-volume or specialized deployments. 8. POSTHOG PostHog is one of the strongest Google Analytics alternatives for SaaS companies because it goes far beyond website traffic. Its platform combines product analytics with session replay, feature flags, experiments, error tracking, surveys, data pipelines, data warehouse capabilities, and AI-related analytics. This matters for SaaS founders because the important question is often not "How many people visited the pricing page?" but "What did those users do after signing up?" A product team can track onboarding, feature usage, activation, retention, experiments, and technical errors in the same ecosystem. This can reduce the need to combine several separate tools for product development and analytics. PostHog also has a generous free usage allowance, including 1 million analytics events per month, 5,000 session recordings, feature-flag requests, error tracking, surveys, and other monthly allowances. Best for: SaaS founders, developers, product managers, startups, and teams building web or mobile products. PRICING Free within the monthly usage allowances. PostHog then uses usage-based pricing for additional volume. 9. MIXPANEL Mixpanel is primarily a product analytics platform rather than a basic website traffic counter. Its strength is understanding what users do inside an application. Funnels can show where users drop off, retention reports can show whether they return, and behavioral analysis can help teams understand which features are actually being used. Mixpanel also includes session replay, heatmaps, experiments, feature flags, and AI capabilities. This makes it useful for SaaS products where product usage matters more than simple pageview reporting. For example, a SaaS team could compare users who completed onboarding with users who skipped it and then examine differences in feature adoption and retention. Best for: SaaS companies, mobile apps, product-led businesses, product managers, and growth teams. PRICING Free forever for up to 1 million events per month, with unlimited seats. Paid usage begins when higher limits and additional capabilities are required. 10. AMPLITUDE Amplitude is another major product analytics platform that is better suited to understanding user behavior inside digital products than simply counting website visits. Its analytics cover funnels, cohorts, retention, segmentation, paths, experiments, and product engagement. Amplitude's prebuilt Product Analytics views include product overview, onboarding, feature engagement, and retention. This is useful for SaaS teams because different parts of the funnel can be analyzed separately. A founder can identify where users drop during onboarding, compare behavior between acquisition channels, and measure whether a feature actually improves retention. Amplitude also includes session replay, experimentation, surveys, activation tools, and AI capabilities, depending on the plan. Best for: Product-led SaaS companies, mobile apps, growth teams, and companies with dedicated product analytics needs. PRICING Free, with up to 2 million events per month and unlimited seats. Paid plans add higher limits and additional capabilities. 11. HEAP Heap focuses on automatically capturing user interactions so product teams can analyze behavior without having to manually define every event before users interact with the product. This approach is useful when teams do not know in advance which user actions will become important. Instead of discovering an important interaction six months later and realizing it was never tracked, automatic capture can preserve a broader behavioral record. Heap supports funnels, journeys, retention, engagement analysis, dashboards, APIs, and other product analytics capabilities. Its platform also includes session replay and heatmaps through its experience analytics capabilities. The free plan supports up to 10,000 monthly sessions, while larger teams can move into paid plans with longer data history, additional analytics capabilities, and more advanced controls. Best for: Product teams, SaaS companies, UX teams, and businesses that want automatic behavioral data collection. PRICING Free for up to 10,000 monthly sessions. Paid plans use custom pricing. 12. MICROSOFT CLARITY Microsoft Clarity is different from many traditional Google Analytics alternatives because its biggest strength is visual behavior analysis. Instead of only showing numbers, Clarity lets teams watch session recordings and use heatmaps to understand where visitors click, scroll, and encounter problems. Its current platform also includes AI-powered summaries and AI chat capabilities that help summarize visitor behavior. For a founder, this can answer questions that a standard traffic dashboard cannot. If a landing page receives thousands of visitors but conversions remain low, session recordings and heatmaps can reveal whether visitors are ignoring the CTA, getting confused by the layout, or abandoning the page at a specific point. Clarity can therefore work particularly well alongside a primary analytics platform rather than necessarily replacing every analytics function. Best for: Founders, marketers, UX teams, ecommerce websites, and businesses that want to understand visitor behavior visually. PRICING Free forever, with unlimited heatmaps, websites, and team members. 13. CLOUDFLARE WEB ANALYTICS Cloudflare Web Analytics is one of the most interesting free Google Analytics alternatives for developers. It provides website analytics without requiring a Cloudflare proxy. A lightweight JavaScript beacon can collect pageview and visitor information, while Cloudflare also provides performance information based on real user monitoring. Privacy is another major focus. Cloudflare says Web Analytics does not collect or use visitors' personal data. For developers who already use Cloudflare, this makes setup particularly convenient. The platform can provide basic traffic and performance visibility without introducing another paid analytics product. It is not designed to replace every feature found in advanced platforms such as Matomo, PostHog, or Adobe Analytics. Its value is simplicity, privacy, performance data, and zero cost. Best for: Developers, personal projects, startups, static websites, and businesses that need basic traffic and performance analytics. PRICING Free. Cloudflare Web Analytics is available across Cloudflare plans. 14. CROMOJO Cromojo is an interesting option for founders who want analytics connected to SEO and website monitoring rather than analytics alone. Its analytics features include real-time analytics, custom events, conversion goals, funnels, visitor analytics, search analytics, and revenue attribution. What makes it different is that the platform also includes SEO indexing and monitoring features. Depending on the plan, users can monitor domains and pages, check broken links, submit URLs to search engines, monitor indexing, and receive indexing reports. That combination can be useful for solo founders managing their own website because traffic, search visibility, indexing, and website health can be monitored from one platform. For an SEO-driven SaaS or content business, this can reduce the number of separate tools required for basic website growth monitoring. Best for: Solo founders, SEO-focused SaaS businesses, content websites, ecommerce sites, and teams that want analytics plus indexing and website monitoring. PRICING Free plan available. Paid plans start at $19/month for 100,000 monthly impressions on monthly billing. 15. ADOBE ANALYTICS Adobe Analytics sits at the enterprise end of the Google Analytics alternatives market. This is not a lightweight tool designed for a founder who wants a simple traffic dashboard. Adobe Analytics is built for organizations that need advanced analytics across large customer journeys, marketing channels, websites, mobile experiences, and other digital touchpoints. Its capabilities include deep segmentation, journey analysis, advanced reporting and attribution, enterprise-level data collection, governance, and integration with the broader Adobe ecosystem. Adobe also offers Customer Journey Analytics and Product Analytics capabilities, allowing larger organizations to connect behavioral information across different systems and analyze customer journeys at a deeper level. For a global enterprise with multiple business units, marketing channels, customer databases, and strict data governance requirements, this level of control can make sense. For a small SaaS company, it is likely far more infrastructure than necessary. Best for: Large enterprises, global brands, complex marketing organizations, and businesses that need advanced cross-channel analytics and data governance. PRICING Custom pricing. Adobe does not publish a standard public price for Adobe Analytics. GOOGLE ANALYTICS ALTERNATIVES BY USE CASE The easiest way to choose an analytics tool is to start with what you need to measure. BEST FOR SMALL WEBSITES Plausible Analytics keeps website analytics simple, covering traffic sources, pages, campaigns, goals, and conversions without a crowded dashboard. BEST FOR PRIVACY Fathom, Simple Analytics, Plausible, and Pirsch are worth considering for privacy-focused and cookie-free analytics. BEST FREE OPTIONS Cloudflare Web Analytics, Microsoft Clarity, GoatCounter, Umami, and free tiers from PostHog, Mixpanel, Amplitude, and Heap cover everything from basic traffic to product analytics. BEST FOR SAAS PostHog, Mixpanel, Amplitude, and Heap focus on activation, retention, funnels, cohorts, feature usage, and user behavior. BEST FOR SELF-HOSTING Matomo, Umami, GoatCounter, and Pirsch give developers more control over analytics data and infrastructure. BEST FOR VISITOR BEHAVIOR Microsoft Clarity uses session recordings and heatmaps to show what visitors actually do on a page. BEST FOR ENTERPRISE Adobe Analytics is built for advanced segmentation, journey analysis, attribution, governance, and cross-channel measurement. BEST FOR SEO-FOCUSED WEBSITES Cromojo combines analytics with search insights, indexing checks, broken-link detection, and website monitoring. WHAT SHOULD YOU LOOK FOR IN A GOOGLE ANALYTICS ALTERNATIVE? Start with the question you need to answer. For blogs and marketing sites, traffic sources, page views, campaigns, and conversions may be enough. For SaaS products, you may need activation, retention, funnels, cohorts, feature adoption, and user journeys. Privacy and data ownership also matter. Matomo and Umami offer self-hosting options, while tools such as PostHog, Mixpanel, Amplitude, and Heap go deeper into product behavior. The best tool is not the one with the most reports. It is the one that gives your team the right data without unnecessary complexity. CONCLUSION There is no single Google Analytics alternative for every business. Plausible and Fathom fit simple website analytics. Matomo suits deeper analytics and data ownership. PostHog, Mixpanel, Amplitude, and Heap are built for product analytics, while Clarity and Cloudflare Web Analytics offer accessible ways to start measuring behavior and traffic. The key is simple: choose the tool based on the business question, not the number of features.
How Buttondown Reached $1M ARR and Became an AI-Recommended Newsletter Platform
Getting a SaaS product recommended by Claude, ChatGPT, Gemini or Perplexity is becoming an important part of product discovery. But AI recommendations do not appear just because a company simply adds a few keywords to its website. Buttondown is a useful example. The newsletter platform entered a crowded market, grew without a viral launch or major funding round, and eventually passed a $1M ARR run rate. In its September 2026 Indie Hackers case study, founder Justin Duke said that LLM-based acquisition had started increasing and wrote: “Claude loves us.” The interesting question is not only how Buttondown reached $1M ARR. It is how years of product positioning, public writing, customer support, word of mouth, searchable content, and product visibility created the information footprint that now helps AI systems understand and recommend the company. > QUICK SUMMARY > > * Solve one clear problem: Buttondown began in 2017 because Justin Duke wanted a simpler newsletter tool for his own use, rather than trying to compete with every email platform. > * Own a specific niche: It focused on Markdown-based newsletters, technical writers, developers, and independent publishers. > * Publish useful content: Founder-led writing and documentation built searchable information around the product. > * Turn customers into distribution: Good support and product experience drove recommendations and word of mouth. > * Build third-party visibility: Customer mentions, communities, reviews, and articles gave search engines and AI systems more evidence about the product. > * Optimize for AI discovery: Clear connections between product → audience → problem → use case help AI systems understand when Buttondown is relevant. > * Track AI recommendations: Duke says LLM-based acquisition is increasing and that Claude recommends Buttondown. > * Measure and improve: Use tools such as Lumirank.ai, Semrush, Profound, or OtterlyAI to track AI mentions, citations, competitors, and prompts. BUTTONDOWN: THE COMPANY BEHIND THE CASE STUDY Buttondown is a newsletter publishing platform designed around a simpler workflow than large marketing automation systems. Its positioning centers on newsletters, Markdown, technical publishing, APIs, automation, and a relatively minimal writing experience. The product was created by Justin Duke, a software engineer who worked at Stripe and Amazon before and during the early period of Buttondown. The idea came from his own frustration with newsletter software. He had been using TinyLetter and felt that existing alternatives were either too heavy or tried to take control of more of the publishing experience than he wanted. Buttondown's development started in April 2017, became functional enough to send emails by the end of May, and officially launched by the end of June 2017. Duke's December 2017 review says the product already had paying customers by July. The original product was intentionally narrow. Duke wanted a tool that could handle the basic newsletter workflow without forcing writers into a large marketing platform. That positioning remains important because Buttondown is now associated with a specific type of user rather than trying to compete for every email-marketing use case. The September 2026 Indie Hackers article reports that Buttondown now powers newsletters for tens of thousands of writers, from small personal newsletters to publications reaching hundreds of thousands of subscribers. The company is fully bootstrapped, profitable, and generating more than $83K per month, according to the case study. Absolutely. The content and writing style are kept the same, but the 15 points have been condensed into 10 points by combining closely related sections. The original ideas and terminology are preserved. Pasted text 1. START WITH A SPECIFIC USER AND CLEAR CATEGORY Buttondown did not begin by trying to serve every possible email marketer. The initial customer was much easier to understand: someone who wanted to publish a newsletter without dealing with the complexity and marketing-heavy design of larger platforms. Duke's advice is direct: build for a specific person, ideally yourself, and resist generalizing before there is a reason to do so. Buttondown's identity is relatively easy to describe: newsletter software for people who want a simple, technical, Markdown-friendly publishing workflow. That is more useful for search and AI discovery than broad positioning such as: > “An all-in-one marketing platform for modern businesses.” A product should have clear answers to: What does it do? Who is it for? What problem does it solve? What makes it different? When should someone choose it instead of an alternative? The connection becomes: Product → audience → problem → use case That relationship should remain consistent across the website, documentation, customer stories, directories, reviews, and third-party articles. 2. PUBLIC WRITING AND WORD OF MOUTH BUILD LONG-TERM DISCOVERY One of Buttondown's most important growth channels was writing. Duke says he wrote extensively in public about building the product, product decisions, and the less interesting parts of running a software business. This writing did not always convert through a measurable funnel, but it built trust with people who were likely to become customers. This is relevant to GEO because AI systems need information to build an understanding of a company. A product page can explain what a product does. But independent articles, technical posts, documentation, reviews, customer stories, and community discussions provide additional context. Buttondown also did not have one launch that suddenly changed the business. Duke explicitly says most growth came from a good product and customers telling their friends. A satisfied customer may: * Mention the product in a newsletter. * Recommend it in a community. * Write a comparison. * Publish a tutorial. * Link to the product. * Mention it when someone asks for an alternative. The company therefore gains information sources that it does not completely control. 3. CUSTOMER SUPPORT BECAME A MARKETING CHANNEL Duke identifies customer support as another major part of Buttondown's growth. When customers reported problems, a real person responded and tried to solve them quickly. Good support increases the chance that customers stay. It also increases the chance that customers recommend the product. That creates a simple loop: Good support → satisfied customers → recommendations → more discovery No AI visibility tool can manufacture this loop. GEO can increase the chances that existing reputation is discovered, but the underlying reputation still needs to exist. 4. THE PRODUCT ITSELF CAN CREATE VISIBILITY Buttondown has another advantage that many SaaS products do not have: its customers publish newsletters. Duke points out that Buttondown's emails carry the company's identity and that public newsletter archives create additional exposure. Every customer publishing through the platform can potentially introduce another person to Buttondown. This is a form of product-led distribution. The product is not simply used to create value. Its normal operation creates additional opportunities for discovery. Other SaaS products can look for similar mechanisms. For example: A form builder can create public forms. A website builder can create public sites. A documentation tool can create public documentation. A scheduling tool can create shareable booking pages. A design tool can create public projects. The strongest examples turn product usage into legitimate distribution without forcing promotional content into the customer's experience. 5. AI RECOMMENDATIONS ARE BECOMING ANOTHER DISCOVERY CHANNEL The most interesting part of the new Indie Hackers case study is the statement: > “LLM-based acquisition has bent the curve up a bit too. Claude loves us.” This indicates that AI assistants have become another discovery channel for Buttondown. The user may instead ask: > “What is the best newsletter platform for a developer who wants Markdown and an API?” The answer engine decides which products to mention. That changes the competition. Traditional SEO is largely about ranking pages for queries. AEO/GEO is about making sure the company, product, use case, evidence, and relevant sources are available to answer engines when they construct an answer. There is no single “AI ranking.” A brand needs to monitor multiple answer engines and multiple prompts. 6. BUILD CONTENT AROUND BUYER QUESTIONS AND CREATE PAGES THAT CAN BE CITED AEO starts with understanding the questions. For a newsletter SaaS, useful prompts might include: What is the best newsletter platform for developers? What are good alternatives to Substack for technical writers? Which newsletter tools support Markdown? What newsletter platforms offer an API? What is a simple alternative to Mailchimp for a small newsletter? The content strategy should answer those questions directly. Do not create a page simply because a keyword has search volume. Create a useful answer that explains: The problem → available options → differences → who each option is for → evidence → recommendation criteria. Being mentioned by AI is useful. Being cited as a source is even more useful. Useful citation targets include: Original research, Product documentation, Detailed comparison pages, Technical guides, Pricing explanations, Industry statistics, Customer case studies, Definitions and explanations Avoid creating pages that contain only promotional claims. Instead of: > “The fastest and best AI prospecting platform.” Publish: > “How to identify companies hiring SDRs, which data points to collect, and how to turn those signals into a prospecting workflow.” The second page contains information an AI system can use. 7. BUILD AN ENTITY, NOT JUST A WEBSITE A SaaS brand needs more than a homepage. AI systems may encounter information about a company through: Official website → documentation → reviews → directories → customer websites → communities → comparison articles → social profiles → founder content → news → third-party research The objective is consistency. If the company's website says it is an AI sales research platform, its documentation says the same thing, customers describe it that way, and third-party articles classify it similarly, an AI system has several independent signals connecting the company to that category. If every page uses a different description, the model has a harder classification problem. This is why entity consistency is an important part of GEO. 8. USE AI VISIBILITY TOOLS AND TEST ACTUAL AI RECOMMENDATIONS You cannot improve AI visibility if you do not know where the company currently appears. Lumirank.ai helps brands understand how they appear in AI search. It tracks how ChatGPT, Gemini, Perplexity, Copilot, and Google AI mention a brand, including visibility, sentiment, competitors, and cited sources. Other useful tools include: * Semrush AI Visibility tracks brand visibility across ChatGPT, Gemini, Perplexity, and Google AI, while providing competitor comparisons, prompt tracking, citations, and AI-readiness audits. * Profound focuses on answer-engine visibility, prompt research, citations, competitor analysis, AI crawler activity, and content recommendations. * OtterlyAI monitors brand mentions and citations across major AI search systems and provides prompt research and GEO optimization features. The workflow is simple: Run prompts → record recommendations → identify competitors → inspect citations → find content gaps → publish or improve content → run the prompts again. Do not assume that strong Google rankings mean strong AI visibility. Create a list of 20–50 questions potential customers might ask. Then test those questions across Claude, ChatGPT, Gemini, and Perplexity. Record: Was the brand mentioned? What position did it receive? How was the product described? Which competitors appeared? Which websites were cited? Was the description accurate? Which use cases triggered the recommendation? 9. FIND TRUSTED SOURCES AND DO NOT TRY TO MANIPULATE AI RECOMMENDATIONS Suppose Claude recommends a competitor whenever someone asks: > “What are the best AI sales tools for small SaaS companies?” Look at the sources behind those answers. Maybe the competitor is repeatedly mentioned in: * SaaS comparison articles * Review websites * Industry publications * Reddit discussions * Founder interviews * Software directories * Customer case studies Those sources provide a roadmap. The objective is not to copy the competitor's content. It is to understand what information ecosystem is influencing the category. GEO should not become a campaign of publishing hundreds of pages that mention a brand repeatedly. Instead, build information that deserves to be referenced. For example, a cybersecurity company could publish original research about phishing attacks. An analytics platform could publish benchmark data. An SEO tool could publish a study of search behavior. A developer tool could publish technical documentation and benchmarks. A SaaS company can also create detailed comparisons and customer case studies based on real data. Original information gives other websites and AI systems something concrete to reference. 10. BUTTONDOWN SHOWS WHY TIME MATTERS One of the strongest lessons from Buttondown is that many of its acquisition channels compound slowly. Duke says starting the company while he had a full-time salary gave him the financial stability to make patient decisions. He specifically connects patience with channels such as SEO and word of mouth. That is also true for GEO. A company cannot create years of customer recommendations, independent mentions, documentation, and useful content overnight. But it can start building those assets now. The process is: Clear positioning → useful product → useful content → customer satisfaction → recommendations → third-party references → stronger entity recognition → AI visibility Pasted text A PRACTICAL AEO/GEO WORKFLOW FOR SAAS A SaaS marketing team can turn the Buttondown example into a repeatable process. STEP 1: DEFINE THE CATEGORY Write one clear sentence explaining what the product does and who it serves. STEP 2: BUILD THE PROMPT LIST Collect questions customers might ask ChatGPT, Claude, Gemini, Perplexity, and Google AI. STEP 3: ESTABLISH THE BASELINE Run those prompts and record brand mentions, competitors, rankings, descriptions, and citations. STEP 4: ANALYZE COMPETITORS Find the products appearing most often and identify the sources cited around them. STEP 5: FIX THE WEBSITE Make product pages, documentation, comparison pages, pricing, use cases, and FAQs clear and factually consistent. STEP 6: PUBLISH USEFUL INFORMATION Create original research, technical guides, customer stories, comparisons, and pages that directly answer important buyer questions. STEP 7: BUILD THIRD-PARTY PRESENCE Earn relevant mentions through customers, communities, publications, directories, reviews, partnerships, and industry content. STEP 8: MONITOR AI VISIBILITY Use Lumirank.ai, Semrush, Profound, or OtterlyAI to monitor mentions, citations, competitors, and prompt-level changes. STEP 9: IMPROVE THE MISSING AREAS If competitors appear for a valuable prompt and your product does not, investigate why. The missing piece may be content, positioning, authority, citations, or simply a better explanation of the product. STEP 10: REPEAT AI search changes continuously. Run the same prompts regularly and compare the results over time. CONCLUSION Buttondown's $1M ARR story is not primarily about one marketing tactic. It is about building a product for a clearly defined user, keeping the product focused, supporting customers directly, publishing useful information, and allowing word of mouth to compound over years. The new part of the story is AI discovery. Justin Duke says LLM-based acquisition has increased and specifically notes that Claude recommends Buttondown. That outcome makes sense when viewed through an AEO/GEO lens. Buttondown has a clear product identity, years of public writing, customer recommendations, documentation, public archives, and third-party references. Those sources give search engines and AI systems enough context to connect the product with specific newsletter use cases. The lesson for SaaS companies is straightforward: Do not optimize only to rank a page. Build the information, reputation, and evidence needed for an AI system to confidently understand when your product is the right answer. Then measure whether that actually happens. That is where tools such as Lumirank.ai, Semrush, Profound, and OtterlyAI become useful: they turn AI visibility from something a company occasionally checks into something the marketing team can monitor, compare, and improve.
Three sites gamed AI search recommendations with 215,000 fake buying guides
Ask Perplexity for the best project estimation software and, depending on which source it pulls from, you'll get one of at least two different top-five lists, each cited to a research site that looks the same: named analysts, a GDPR badge, a claim of tens of thousands of independently verified software reviews. worldmetrics.org, recommends Float, Scoro, Teamwork.com, and Wrike. wifitalents.com, for the identical question,recommends Float, Scoro, Teamwork.com, Buildertrend, and Apropo. Neither site agrees with the other, and neither is actually independent. They're two of three domains that, according to a report published this week, generated 215,128 near-identical "best software" pages between them and now sit inside the citation graph Perplexity draws on to answer exactly this kind of question. The report comes from Trellner Research, an independent outfit that measures what AI systems actually cite when they answer questions. It queried Perplexity's sonar and sonar-pro models with 380 software-category questions ("best project estimation software," "top research data management platforms," and so on), asked for five ranked recommendations with sourced homepage domains, and logged every citation. The result: 7,534 citations spanning 2,055 distinct domains. It's a small, specific study, but the pattern it surfaces is not small at all. > SUMMARY: > > 1. The finding: Three domains, worldmetrics.org, gitnux.org, and wifitalents.com, published 215,128 machine-generated "best software" pages and now show up as cited sources in Perplexity's answers, more often than Gartner in some categories. > 2. The scale of the miss: 59.8% of all citations in the study point to domains ranked below #100,000 on Tranco's global traffic list; 23.4% aren't ranked in the top million at all. The median cited domain sits at rank 71,611. > 3. The tell: all three sites share the same two Cloudflare nameservers, the same page template and navigation, and were registered through NameCheap within a five-month window in 2023-2024, yet each credits a different set of three "named researchers" for identical rankings. > 4. It's not hypothetical harm: one citation for a research-data-management query resolved to a domain that no longer belongs to the real service and now redirects to a gambling site. > 5. A vendor's own marketing blog, Guideflow, ranked as the third most-cited domain overall, ahead of Gartner, despite not competing in most of the categories it was cited for. > 6. The report is careful about its limits: one prompt wording, one run per category, Perplexity only, no test of whether removing these sources would change the actual recommendations. Read the numbers as a real pattern worth watching, not proof the sources decided the outcome. WHAT THE THREE SITES ACTUALLY LOOK LIKE Open worldmetrics.org or gitnux.org and the pitch is confident: an "independent market research platform," 70,000+ software reviews across nearly a hundred categories, a five-step editorial process, named analysts with degrees from real institutions, and logos claiming citations from Microsoft, Forbes, Bloomberg, and Reuters. gitnux.org says it was founded in 2021. wifitalents.com runs the same pitch with the same structure: custom research from a few thousand euros, software advisory retainers, and a promise that "humans decide what gets published." None of that squares with what Trellner found underneath the branding. All three domains delegate DNS to the identical pair of Cloudflare nameservers, pam.ns.cloudflare.com and sean.ns.cloudflare.com. All three run the exact same page template with the exact same navigation bar: Services, Market Data, Software Advice, Editorial Process, Company. All three were registered through NameCheap between December 2023 and May 2024, a five-month window that doesn't line up with gitnux.org's own "founded in 2021" claim on its About page. The tell that's hardest to explain away is the bylines. Each page carries three named staff credits that never repeat across sites: Worldmetrics credits Kathryn Blake, Alexander Schmidt, and Victoria Marsh; Gitnux credits Diana Reeves, Helena Kowalczyk, and Olivia Thornton; WifiTalents credits Ryan Gallagher, Isabella Rossi, and Natasha Ivanova. Nine distinct named "researchers," for the identical project-estimation-software ranking, none of whom appear to have actually written anything, since the underlying page is a template. Trellner also found unrendered template variables surfacing in some bylines, like a "Within the next 26 days" placeholder that was clearly meant to be filled in by a script and never was. THE CITATION TABLE NOBODY WAS TRACKING Here's where it gets concrete. Across all 7,534 citations Trellner logged, the top of the table looks roughly like what you'd expect, mixed in with what you wouldn't: | Domain | Citations | Share | Tranco rank | | | : | : | : | | g2.com | 291 | 3.86% | 4,027 | | reddit.com | 261 | 3.46% | 105 | | guideflow.com | 194 | 2.57% | 177,039 | | gartner.com | 158 | 2.10% | 1,766 | | zapier.com | 82 | 1.09% | 2,919 | | wifitalents.com | 71 | 0.94% | 105,281 | | capterra.com | 68 | 0.90% | 6,387 | | linkedin.com | 67 | 0.89% | 18 | | worldmetrics.org | 60 | 0.80% | 104,737 | | gitnux.org | 50 | 0.66% | 42,759 | Three of the ten most-cited domains for software buying decisions are the manufactured network. Between them they out-cite Zapier, Capterra, and LinkedIn. And that top-10 list is the tame part: outside it, 59.8% of all citations point to domains ranked below #100,000 globally, 23.4% aren't in the Tranco top million at all, and the median cited domain sits at rank 71,611. Only 17.3% of citations concentrate in what you'd call the top ten "real" sources. The long tail isn't a rounding error, it's most of the answer. WHEN A MARKETING BLOG OUTRANKS GARTNER The other name worth sitting with is guideflow.com, sitting at #3 with 194 citations, ahead of Gartner's 158. Guideflow sells interactive product-demo software. It does not operate in most of the categories where Trellner found it cited, and its blog is, by the report's description, standard content-marketing material aimed at driving signups, not neutral comparison research. It still became "the third-largest evidence base" behind Perplexity's software recommendations, simply because it publishes a lot of content that happens to be structured the way these models like to extract from. That's the part that should worry anyone who has spent the last year hearing "just optimize for AI search" as the new SEO advice. It's true that AI answer engines reward clearly structured, frequently updated, citation-dense content; we wrote about the mechanics of that in our guide to Generative Engine Optimization. What this report adds is the uncomfortable flip side: that same reward function doesn't distinguish a neutral analyst from a vendor's own sales blog, or from three shell domains built specifically to look like one, provided the formatting checks the right boxes. THE BROKEN LINKS ARE THE PART THAT SHOULD HAVE CAUGHT SOMEONE Trellner also spot-checked whether the recommended vendor URLs actually resolved. 1.1% didn't. One example given in the report: a citation for "research data management platforms" pointed a reader toward dryad.co, presumably meant to be the well-known Dryad data repository, but the domain has since lapsed and now redirects to an Indonesian gambling site. Another recommendation, for Monte Carlo-style data tooling, pointed to a URL that now belongs to a Monegasque casino portal. Neither of these is a subtle SEO trick. They're stale links nobody bothered to re-verify, surfaced with full confidence as a citation inside an AI-generated answer that looks, at a glance, exactly as authoritative as every other line in the response. WHAT THE REPORT DOESN'T CLAIM To its credit, Trellner is explicit about where the evidence stops. The methodology is one prompt wording per category, one run, no repeat sampling, and it covers Perplexity's sonar models only, not ChatGPT, Gemini, or any other AI search product. The authors write plainly: "We have not shown that any of this changes the answers," and they didn't test the counterfactual of what Perplexity would recommend if these three domains were removed from its index entirely. It's entirely possible the underlying vendor rankings would look similar either way, since programmatic sites like these tend to scrape and remix the same public review data everyone else does. What the report does establish, carefully, is narrower and still significant: that a small, coordinated, unverifiable content network occupies real, measurable space inside the evidence base an AI search product presents as sourced fact, and that space is comparable to or larger than what long-established, actually-independent analyst firms occupy in the same answers. WHY THIS MATTERS PAST SOFTWARE LISTICLES The three sites in this report happen to sell "software advisory" and market research, which makes the story easy to dismiss as a B2B SaaS problem. It isn't. The same programmatic-content playbook, register a domain, adopt research-site branding, generate tens of thousands of templated pages, works for any category an AI search engine gets asked about: health supplements, financial products, legal services, hardware reviews. Software recommendations are just the category someone happened to audit first, with a public, checkable list of domains that anyone can verify by loading the pages. For founders and indie hackers, there are two takeaways worth carrying past the headline number. First, "an AI search engine recommended us" is not by itself a signal that you've earned real trust with a model. It might just mean your competitor's citation farm hasn't gotten around to your category yet, or already has, and is quietly outranking you with a page that took thirty seconds to generate. Second, if you're relying on ChatGPT, Perplexity, or Gemini traffic as a growth channel, it's worth actually checking who else gets cited alongside you for your category, the way Trellner did manually here. Run the query yourself, note every domain cited, and look up who's actually behind them. It costs five minutes and tells you whether you're competing against real alternatives or against a template with a different logo on it. None of this means AI search is untrustworthy across the board. It means the citation graph underneath it is exactly as gameable as the link graph underneath classic SEO was fifteen years ago, just with less scrutiny on it so far, because most people still assume "cited source" means "verified source." That gap is where 215,128 pages came from, and it's very unlikely to be the last network built to fill it. CONCLUSION AI citations can drive visibility, but being cited does not automatically mean a source is trustworthy. For SaaS brands, the focus should be on building accurate product information, credible reviews, community mentions, and trusted sources that AI systems can confidently reference. The key is not just getting cited, but understanding why you are being cited.