As long as I’ve been in marketing, people have warned against focusing on “vanity metrics,” or those flashy, high numbers that don’t translate to real results or profit. Fast forward a decade, I never expected traffic and search rank to be part of that conversation.
A few weeks back, I stumbled across a Reddit post that talked about the first measurement ever taken of Mount Everest. Rather than listing the exact measurement of 29,000 feet, the British Royal Geographical Society declared Mount Everest’s elevation to be 29,002 feet. It’s one of those facts that seems fake, but there’s good evidence that it’s true.
Enterprise marketing automation is how large organizations scale personalized marketing across multiple teams and channels without disrupting their data or workflow. If you’re evaluating platforms or trying to modernize a fragmented stack, this guide covers everything you need to make a confident decision.
If you’re evaluating answer engine optimization tools, here’s the short version: HubSpot AEO is an insight-to-execution platform that connects AI visibility data directly to your CRM and content workflows. Scrunch is a focused monitoring and benchmarking tool that excels at multi-engine tracking and competitive share-of-voice analysis.
As more buyers skip search entirely and go straight to ChatGPT, Gemini, or Perplexity for recommendations, marketers are realizing they need a new kind of tool — one that shows them how their brand appears in AI answers and what to do about it. That’s what both HubSpot AEO and Ahrefs Brand Radar are built for. But they take meaningfully different approaches, and the right choice depends almost entirely on what you need to do after you get the data.
There’s an irony at the heart of most failed CRM rollouts: the technology almost never lets you down. The deployment does. More than 60% of CRM failures trace back to people and process challenges, not the software, and most of those failures are preventable with the right plan.
Brand tracking tools help growth marketers gauge what people say about the brand, whether it’s good or bad, and why they choose competitors, all at scale. Brand monitoring also reveals AI share of voice and what you should improve to be cited more by LLMs.
If you’re comparing Scrunch versus Peec AI, you need to know your buyers are already forming opinions about your brand in answer engines like ChatGPT, Perplexity, and Google AI Mode before they ever reach your website.
In today’s digital world, customers want to access support quickly via their preferred communication channel. In response, more companies are looking to deliver seamless, personalized support experiences via omnichannel customer service models.
CRM projects don’t fail at the technical level — they fail because the people expected to use the system never bought in, the training was too generic to stick, and nobody owned adoption after go-live. The result is a CRM that costs money to maintain but doesn’t deliver the pipeline visibility or process alignment it was supposed to.
If your buyers are asking ChatGPT for vendor recommendations instead of scrolling through Google results, your brand’s visibility in AI search engines matters as much as your organic rankings … maybe more. That shift is what’s driving the rapid adoption of answer engine optimization as a discipline, and it’s why marketers are now evaluating dedicated tools to track, measure, and improve how AI platforms characterize their brand. The challenge isn’t awareness that AI is reshaping search; it’s choosing the right tool to address it.
For many, Semrush is the gold standard for tracking traditional SERP rankings, and the tool has recently added AI visibility features to its suite. However, there are plenty of Semrush AI visibility alternatives on the market worth exploring.
Here’s the question most teams are actually trying to answer: do you need a standalone tool to monitor how your brand shows up in AI search, or do you need something that connects that monitoring to the content and CRM workflows that let you act on it?
Profound and Semrush have both staked out positions in the answer engine optimization space, but they’re built for different teams with different goals. This post breaks down what each tool actually does, where they differ on engine coverage, prompt research, dashboards, and pricing, and how to decide which one fits your workflow.
SEO might feel like it’s under threat with the rise of AI. But the data tells a different story — SEO tools are still a must-have for marketers. In a HubSpot survey, 27% of marketers agreed that the biggest ROI channel of this year was the website, blog, and SEO.
AI search is no longer a niche experiment. According to HubSpot’s own research, 42% of buyers now use AI search as part of their evaluation process — and it’s the top predictor of purchase intent. Brands that show up in those answers earn the conversation before a prospect even clicks.
At some point after a CRM goes live, someone pulls a pipeline report and the numbers don’t add up. Marketing and sales end up using different definitions for the same term. A new integration breaks three sales pipeline reports that nobody knew shared the same data source.
Answer engine optimization (AEO) is the practice of creating content for AI like ChatGPT, Claude, and Gemini to reference in their outputs. AI-referred traffic is small but growing fast and has an outsized impact on conversions. Traffic from AEO makes up less than 1% of overall traffic but converts 3x-15x better than traditional search, according to November 2025 data from Microsoft Clarity. While the rest of the website world panics over declining traffic, savvy marketers like you are learning how to win high-intent traffic from AEO. Quality over quantity, right?
If you’re comparing HubSpot AEO vs Semrush AI Visibility Toolkit, I tested both for you as a marketing and AEO practitioner myself and will share my detailed review.
AI SEO tools have matured quickly. What started as a novelty layer atop existing keyword tools has become a full-fledged category, helping improve everything from research to AI search visibility.
There are an abundance of SEO tools for small businesses. But, with limited resources and budget, small businesses can face an added challenge: finding the right tech stack that does everything they need to maintain a competitive edge in search, without breaking the bank.
Scrunch and Ahrefs Brand Radar both track brand visibility in AI answers, but they solve different problems. Scrunch is purpose-built for AEO: it monitors AI citations, audits AI crawlability, and actively optimizes how AI bots experience your site. Ahrefs Brand Radar is an add-on to the broader Ahrefs SEO platform, built for teams that want AI visibility alongside traditional search data, backlinks, and web mentions in one place.
AI SEO tools for small businesses are essential to keep up with marketing trends and stay competitive. Yet most small businesses are trying to figure out which AI tools are actually worth their time and which ones are necessary, especially when there is an abundance of tools promising to solve every problem imaginable. The good news is that AI is available in just about every tool.
Roughly 58% of consumers now use AI answer engines in their product research each week — and that number is rising fast. As AI-powered answer engines like ChatGPT, Perplexity, and Google AI Overviews become primary discovery surfaces, content and SEO teams are asking the right question: is there a relationship between backlinks and AEO, or is it time to rethink the entire authority playbook?
There’s no denying it: Search behaviors have changed. As more queries are answered directly by AI Overviews, ChatGPT, and Perplexity, the shifts reshaping search are forcing marketers to rethink ranking and, just as crucially, learn how to write for AI search rather than just for a list of blue links.
You’ve probably noticed it yourself: buyers aren’t just Googling anymore. They’re asking ChatGPT, Gemini, and Perplexity questions and trusting whatever answers they get. If your brand isn’t named in those answers, well, you may not even be a consideration.
CRM data migration is the process of moving data, workflows, and assets from one CRM to another. It matters because your CRM is the operational backbone of your revenue team, and when the data inside it is wrong, every process built on top of it breaks too.
The best enterprise rank-tracking software goes far beyond checking positions for a handful of keywords. At scale, it means monitoring millions of data points across devices, locations, and search features, including AI Overviews, featured snippets, and local packs. Then that intelligence is fed into dashboards, CRM workflows, and executive reports that drive action across large organizations.
According to McKinsey, 50% of consumers now use AI-powered search, and more than 70% rely on it to ask questions and gather information. This shift in search behavior means SEO leaders must evolve. Modern workflows need to incorporate Answer Engine Optimization (AEO) strategies so brands can not only rank on page one of Google, but also gain visibility inside AI-generated answers across platforms like Google AI Overviews, ChatGPT, and Perplexity.
Wholesale businesses require more than a generic customer relationship management (CRM) system. A CRM for wholesalers must support account-specific pricing, large product catalogs, repeat orders, and sales workflows that integrate with inventory and fulfillment systems. When these processes are disconnected, quoting slows, errors increase, and revenue opportunities are harder to capture.
Growth experimentation is a structured approach to testing ideas across the full customer journey to discover what drives measurable business growth. Experiments improve channel-by-channel optimization as marketing teams push for measurable, repeatable growth under tight budgets.
Every content marketer seems to be asking the same question: Do semantic keywords still matter in SEO in 2026, especially now that AI engines influence traffic and buying decisions?
Recent data shows that 88% of marketers now use AI every day to guide their biggest decisions, and for good reason. Marketing automation has been shown to generate 80% more leads and drive 77% higher conversion rates. With global ad spend now topping $1 trillion, there’s simply too much data for even the most experienced teams to manage by hand anymore.
As any marketer or SEO knows, there’s a special satisfaction in seeing your hard work pay off in the form of snagging a top result on the SERPs. But in the age of answer engine optimization (AEO), search results don’t tell the full story. To gauge success, you need to learn how to track your brand’s presence in AI search — which introduces a new set of metrics, including mentions, citations, and share of voice.
AI search optimization is the practice of improving brands’ odds of being cited and mentioned by answer engines like ChatGPT, Gemini, and AI Overviews. The traffic it earns is small but high-intent. Across more than 1,200 publisher and news sites, visitors referred by AI tools signed up at roughly 11 times the rate of search visitors, according to a Microsoft Clarity study.
Scrunch vs Semrush comes down to one question: Do you need a dedicated AI visibility tool, or a full SEO suite that now tracks AI answers too? Scrunch is an AEO specialist built to monitor how your brand appears in AI-generated answers, while Semrush is a traditional SEO platform that added an AI Visibility Toolkit to a stack marketers already use for keyword research, rank tracking, and backlinks.
After years of working alongside CRM administrators, I’ve learned the single biggest difference between CRM platforms that drive revenue and ones that collect digital dust. The difference isn’t the software nor the budget, but the quality of the administration behind it.
If a brand is not visible in answer engines, it’s missing critical early-stage influence. According to McKinsey, 50% of consumers now use answer engines, and more than 70% rely on it to ask questions and gather information. That means a growing share of discovery occurs within AI tools and before users click through to websites.
As AI search reshapes how customers discover and evaluate brands, tools like Profound are gaining attention for helping marketers measure visibility within AI-generated answers. But, as budgets tighten, new AI visibility features emerge, and integration demands increase, many teams are actively seeking alternatives to Profound AI.
A CRM is like a teenager’s journal – full of sensitive information. But instead of school stories and secrets, it holds contact records, purchase history, support conversations, and for some, health information or payment data, too.
If you want to know how to get indexed by ChatGPT, I’ll show you, but first, I want to clarify: Other articles on this topic conflate “getting indexed by” with “showing up in” ChatGPT — and they are not the same thing. Getting indexed by ChatGPT means OpenAI’s search crawler discovered your page and stored it in OpenAI’s proprietary index (about which very little is publicly known). Showing up in ChatGPT means your content appeared in an answer, which can happen via that index or via a live web fetch triggered by a user’s query.
AI search interfaces are reshaping how content gets surfaced and cited. Pew Research data from 2025 found that around one in five Google searches produced an AI-generated summary, with 88% of those summaries citing three or more sources. Bain’s 2025 research found that roughly 80% of consumers rely on zero-click results in at least 40% of their searches.
Marketers are turning to AI-powered tools to scale relevance without increasing manual effort as inbox competition increases and performance expectations rise. AI email marketing tools are rapidly reshaping how teams execute and measure email campaigns. AI advances now support everything from subject line creation and personalization to send-time optimization and revenue attribution.
Schema markup for AEO helps answer engines understand a website. Schema is readable by AI crawlers because it’s added to a site’s HTML. It allows SEO professionals to add additional context and map entities without overwhelming the website’s front end or users. This additional context provided by schema reduces ambiguity and increases the likelihood that the web content can be accurately cited in AI-generated answers.
Keyword research for AEO can feel overwhelming because audiences are searching for almost everything in AI search, and queries are nuanced and personalized.
Customer relationship management (CRM) systems have become foundational to effective email marketing. For teams learning how to use a CRM for email marketing, the key is connecting contact data, segmentation, automation, and measurement into a single, cohesive workflow.
It seems like every brand is scrambling to get a piece of the pie in this new answer engine optimization (AEO) world. But what if you could get ahead of the curve by knowing the best on-page content formats for AI as verified by research? I pored over results from the new HubSpot State of AEO 2026 report and Wix Studio’s AI Search Lab research on most-cited content types to find out.
There’s a widening gap between what the market says about AI and what we actually hear from customers. The media, the VCs, the AI labs, and influencers have all talked about AI replacing humans, ripping out trusted software, and token-maxxing as ends worth pursuing. But the leaders running real businesses are increasingly asking the right questions. How do I make my people better with AI? Which systems can I trust? How can I measure the ROI of this spend? We hear these questions every day.
AI search behavior may be causing a dip in your traffic, but it’s also sending higher-quality leads your way. For marketers, that second part is a massive win. AI search was the number one predictor of purchase intent for CRM software buyers, according to HubSpot’s State of AEO 2026 report. And there are more findings from the report that every go-to-market team needs to know.
For years, the SEO playbook was straightforward: earn backlinks, climb rankings, capture clicks. But as AI reshapes how traditional SEO works, a different mechanism is determining which content gets seen — and it’s not backlinks. It’s citations. The role of citations in AEO is fundamentally different from link-building: instead of other publishers vouching for your page, AI answer engines are selecting your content as the direct source behind their generated answers.
A few weeks ago, I wrote about our vision for the agent era: agents should be able to run on HubSpot, and to run HubSpot. I want to go a level deeper on what “run HubSpot” actually means, and our latest step in bringing this vision to life.
Walk down a suburban street, and you might stumble across a following sign. It’s probably messy with poor formatting and inconsistent font size. Here’s one that I saw in Houston.
If you’re worried about what AI Overviews mean for SEO, let me remind you of the panic over featured snippets circa 2017. Remember how that turned out? At first, bloggers and SEOs bristled over these quick-glance summaries at the top of the Google SERPs, fearing they’d steal all our traffic. Eventually, however, we adapted and started optimizing content to get mentioned in them. I believe the same will be true of AI Overviews. I mean, it’s already happening: The internet is now filled with the latest advice on how to get cited in AI Overviews (including this article).
Growing up, the only “top 10” I cared about was MTV’s Total Request Live (TRL). When I started working, that became the top 10 results in the Google SERP. Now, my eyes are set even higher as we marketers explore how to rank in AI Overviews.
Google AI Overviews appear in Google Search results for a growing share of queries, and if your content isn’t structured to earn a citation, you’re losing visibility to competitors who’ve already adapted. Unfortunately, the challenge isn’t awareness. Most SEO leaders know AI Overviews exist. The challenge is execution: translating Google’s deliberately vague guidance into repeatable content workflows, measuring whether your AI website optimizations are actually earning citations, and proving business impact when traditional metrics like rank position and CTR no longer tell the full story. This playbook closes that gap.
I’ve spent the last year watching marketing teams scramble to understand why their organic traffic reports tell one story while their pipeline tells another. The missing link is almost always a need for AI search analytics tools.
The brand tracking dashboard says awareness is up. Social listening tools show steady mention volume. The PR platform logged a dozen media hits last quarter. But, none of those tools show how a brand shows up when a buyer asks ChatGPT, Perplexity, or Gemini for a recommendation.
Brand mentions aren’t a new concept, but answer engine optimization (AEO) is giving them a different weight. Brand mentions are any online reference to your brand, product, spokesperson, or company name; right now, they’re happening in more places than most teams can track.
Digital marketing optimization plays a major role in whether a marketing program grows or remains stagnant. Most teams are running campaigns, tracking metrics, and still scratching their heads, wondering why the pipeline isn’t moving. Honestly? The problem usually comes down to process, not effort.
In 2007, Coulter and Coulter showed two advertisements to two random groups of customers. Each advertised £10 discounts on flights to Turkey. One listed the tickets at £188. The other showed a higher price: £233.
Brand visibility determines whether your business gets found or gets passed over — in search results, on social feeds, and increasingly, in AI-generated answers. It’s one of the highest-leverage investments a marketing team can make, and also one of the most commonly mismanaged.
Product SEO is one of the highest-leveraged — and most overlooked — strategies in B2B and SaaS marketing. While most teams pour resources into top-of-funnel content, the pages that actually drive pipeline decisions, such as feature pages, comparison pages, and pricing pages, often go unoptimized and underperform.
For years, HubSpot invested in making our platform the best place for marketing, sales, and service teams to do their work. With AI, we’ve been building it to do the work for them – through agents that qualify leads, resolve tickets, save deals, and drive outcomes across the business. That’s why we call HubSpot an agentic customer platform.
As a content writer with over 7 years of SEO experience, I can confidently say that keyword clustering is a critical technique—even in a world where the SEO landscape has changed significantly.
You already track and analyze your SEO strategy — keyword rankings, organic traffic, SERP positions. But when a prospect asks ChatGPT, Perplexity, or Google AI Overviews a buying question and your brand doesn’t appear in the answer, traditional rank tracking can’t tell you that. AEO prompt tracking helps you measure brand visibility within AI-generated answers by monitoring whether (and how) your brand gets cited when real AI prompts are run across the engines your audience is actually using. For marketing leaders, SEO managers, and demand gen teams, it’s the measurement layer that closes the gap between “we publish great content” and “we can prove AI search drives pipeline.”
Every company’s competitors are showing up in AI-generated answers, but do marketers know which ones, for which queries, and why? That’s exactly what AEO competitor analysis is designed to tell teams.
When tracking share of voice for marketing teams, it’s often assumed to be a vanity metric — a number executives like to include in board decks but one that rarely influences strategy. In practice, that assumption doesn’t hold up.
I’ve spent considerable time testing free answer engine optimization tools across dozens of brand audits, and the verdict is clear: you don’t need a five-figure tech stack to get meaningful AEO data.
When I first started auditing content for answer engine visibility, I assumed the keyword research process was roughly the same as traditional SEO — just with a few tweaks. I was wrong.
Your competitors are adjusting pricing, launching new ad creative, publishing content that outranks yours, and showing up in AI answers you didn’t know existed — often all in the same week. Competitor monitoring tools exist to catch those moves early, but most teams end up with fragmented data scattered across platforms, and by the time they’ve pieced it together, the window to respond has closed.
This is part one of a three-part series on how HubSpot transformed with AI. Part two covers how we grow with Agent-first GTM. Part three is how we operate as an AI-first company.
This is part two of a three-part series on how HubSpot transformed with AI. Part one covers how we build with AI. Part three is how we operate as an AI-first company.
This is part three of a three-part series on how HubSpot transformed with AI. Part one covers how we build with AI. Part two covers how we grow with Agent-first GTM.
The AEO benefits that matter most to marketing leaders have shifted from theoretical to measurable. As AI-powered search engines like ChatGPT, Google AI Overviews, and Perplexity handle a growing share of how buyers discover brands, the rise of AI-powered search results increases brand visibility; the teams investing now are seeing real returns in conversion quality, pipeline influence, and long-term authority.
Your brand’s AI visibility score covers the part of the search landscape that traditional SEO rank tracking can’t see. Tracking it is becoming as essential as monitoring Google rankings — and a lot harder to pin down.
Search behavior has changed dramatically, and teams need to learn answer engine optimization (AEO) best practices to keep up. While traditional search engines still dominate, people increasingly turn to AI tools like ChatGPT to answer their questions. Heck, with 79% of those who already use AI for search believing it offers a better experience, even Google has introduced AI overviews to stay competitive.
A lot is going on in search today. Google still reigns supreme, but the competition and evolution coming from AI alternatives have many marketers wondering how to optimize for ChatGPT.
AI search engine citation tracking helps measure brand visibility and authority in AI-powered search results. As AI-powered search experiences reshape how people discover information, evaluate vendors, and build shortlists, visibility inside AI answers is no longer a vanity metric. If AI engines aren’t citing your brand, you’re missing influence at the exact moment buyers are forming opinions.
Generative AI is changing how people discover brands, products, and information. Because it disrupts the buyer journey, it requires new metrics, specifically GEO KPIs, that accurately reflect performance within these AI engines.
AEO metrics every marketer should track in 2026
Answer engine optimization (AEO) is a marketing strategy designed to help brands appear more consistently and accurately within AI-driven answer engines such as ChatGPT, Perplexity, and Copilot.
Research shows that 32% of buyers discover new B2B vendors using generative AI chatbots. This is why an answer engine optimization (AEO) strategy for B2B businesses is essential. AI-driven answer engines help buyers discover, evaluate, and shortlist vendors. The same research found that buyers start with an average of 7.6 potential vendors and narrow this to 3.5 before making their final decision.
GEO — or, as HubSpot refers to it, AEO — has found its place in the search landscape, and it’s reasonable to think that the future of generative engine optimization is guaranteed. According to Datos’s State of Search report, Q4-2025 saw some interesting changes. For the first time, AI tools had a consistent 1.31% to 1.34% of visits in the U.S. In previous quarters and reports, traffic to AI tools was growing. This stability in traffic suggests that AI search tools may have found their place in the wider search landscape.
While many are still skeptical, the global creator economy is expected to reach $1.18 trillion USD by 2032. And for minority creators and entrepreneurs from underrepresented groups, this moment is especially significant.
Marketers use AEO and GEO interchangeably, but there is a difference, and that’s what will be defined and explained in this article. In brief, AEO optimizes content for answer boxes and voice search results, while GEO targets AI chatbot citations and generated summaries.
Today, more and more buyers are beginning their journey with an AI-search. They may ask ChatGPT to compare products or use an AI-powered platform like Perplexity. Or, they're just Googling an offering and reading the AI Overview, all without clicking a link.
HubSpot realized that our buyers were moving from search engines to answer engines like ChatGPT, Gemini, and Perplexity — but we had no reliable way to measure AI visibility and understand whether our AEO plays were working.
If you’re asking yourself, “How can I measure AEO success?”, AEO rank trackers should be your next investment. They gauge your brand visibility in AI-generated answers, considering metrics like citations, mentions, share of voice, and sentiment.
By now, you know that everyone’s buzzing over answer engine optimization (AEO). So, just what is AEO in marketing? It’s a new way of ensuring your brand shows up in the places your prospects are using more and more: AI tools like Gemini, Perplexity, and ChatGPT. These AEO insights will catch you up on the most crucial information to get started now.
There’s a lot of conjecture out there about how to show up in ChatGPT results, but if you want advice from a practitioner who’s actually done it, keep reading.
Most marketing teams I talk to are doing genuinely good SEO, and yet when they open ChatGPT or Perplexity and type in the prompts their buyers are actually using, their brand is nowhere to be found. This is the exact problem the FSA Framework was built to solve.
An AEO strategy for SaaS won’t stray too far away from a good SEO strategy, but some tactics benefit AI search more than others, and it helps to know what these are. We all know that AI has shifted how brands earn visibility, and how visibility doesn’t equal clicks. But for SaaS, the way buyers conduct discovery and evaluation has changed disproportionately.
Two-thirds of marketers say that marketing has changed more in the past three years than in the past 50. Understanding Loop Marketing versus traditional marketing has become essential for marketers in 2026. The two frameworks differ fundamentally in how brands reach, engage, and retain customers in an AI-driven world.
A marketing forecast estimates future marketing results, such as leads, pipeline, and revenue, using historical data and conversion assumptions. Marketing forecasting connects planned activity to expected outcomes, helping teams understand what performance is likely to look like before campaigns are executed. This approach supports clearer planning, more predictable growth, and stronger alignment between marketing inputs and revenue targets.
Maybe you’ve opened ChatGPT a handful of times, gotten subpar results, and moved on. Maybe you’ve sat through an AI training or two and thought, “Cool, but how does this actually apply to my job?” Or maybe you’ve bookmarked a dozen AI tools you saw recommended on LinkedIn and haven’t tried a single one.
Search results used to be a doorway. You ranked, someone clicked, and they landed on your site. But today, that model is eroding faster than most marketing teams are equipped to move.
