For most teams, the answer to \u201cwhich prompts should I track?\u201d is this: track a representative sample of branded, unbranded, and competitor prompts across buyer stages, personas, and priority markets. Don\u2019t fall into the trap of tracking everything.<\/p>\n
<\/a><\/p>\n Remember: buyers don\u2019t query answer engines the way they query Google. A prompt often reads like a brief. Prompts often include details like team size, budget, location, and follow-up questions that all reshape the answer. Faced with that variation, marketers either track everything and drown in noise or fall back on branded queries that inflate visibility metrics.<\/p>\n This guide aims to help marketers build an effective prompt tracking set. The guide explains choosing and balancing prompts as well as scoring, maintaining, and turning visibility and citation findings into content decisions.<\/p>\n Table of Contents<\/strong><\/p>\n Which prompts should I track to reflect real buyer intent?<\/a> <\/a> <\/p>\n According to Responsive research<\/a>, 56% of tech buyers are turning to generative AI to build vendor shortlists. Answer engines increasingly drive which brands buyers consider before they ever visit a website.<\/p>\n Tracking prompts shows marketers whether their brand appears where buyers are asking, how it\u2019s described, and which competitors and sources are winning those answers instead.<\/p>\n Think about prompts in three broad categories: branded, unbranded, and competitor.<\/p>\n Branded prompts include the company or product name, such as \u201cWhat is [Brand]?\u201d or \u201cDoes [Brand] integrate with Salesforce?\u201d<\/p>\n Important note: In the same way that a brand will rank for its branded keyword in traditional SEO, answer engines should always mention the brand here, so presence isn\u2019t the metric. Branded prompts protect brand accuracy in AI answers. For branded prompts, the focus is sentiment and accuracy.<\/p>\n Unbranded prompts describe a problem or category without naming any company, such as \u201cbest project management software for agencies\u201d or \u201chow to reduce client onboarding time.\u201d Unbranded solution-seeking prompts measure discovery-stage visibility. Unbranded prompts usually reveal the biggest gaps and should make up the largest share of a prompt set.<\/p>\n In my experience, unbranded prompts deserve the same priority as unbranded keywords in traditional SEO. Search teams often judge search performance by branded demand, but search teams and stakeholders must understand that nonbranded search plays a critical role in performance as well.<\/p>\n Increasing brand visibility is mostly the job of PR, brand marketing, and wider visibility efforts. Search supports that work, but a search team\u2019s core role is to reach buyers who are still discovering their options and don\u2019t yet know which brands to consider. In AI search, unbranded prompts are where that discovery happens. Unbranded prompts are the clearest measure of whether answer engine optimization (AEO) is reaching new buyers, tying visibility as directly as possible to a search team\u2019s efforts.<\/p>\n Competitor prompts reveal where rivals appear during evaluation. Competitor prompts name one or more rivals, such as \u201cIs [Competitor] good for remote teams?\u201d or \u201calternatives to [Competitor].\u201d Answers to these prompts show which brands answer engines consider as the category\u2019s default options, and whether the brand appears as a credible alternative.<\/p>\n Pro Tip: If possible, track prompts for three to five direct competitors. Keep the focus on competitors who have solid marketing strategies and perform well online.<\/p>\n Ideally, marketers will track prompts that fall under all three categories of branded, unbranded, and competitor.<\/p>\n Using dedicated tools for prompt tracking, instead of manual checks, makes spotting trends more manageable for marketers. HubSpot\u2019s AEO tool<\/a> gives marketers one place to monitor prompts and track brand visibility across ChatGPT, Gemini, and Perplexity. Teams can compare how often competitors appear on the same prompts, review which sources answer engines cite, and turn findings into clear, prioritized recommendations.<\/p>\n For Marketing Hub Professional and Enterprise customers, AEO is included and uses CRM data to suggest prompts to track, rather than relying on generic industry templates. HubSpot\u2019s AEO tool is also available standalone for $50\/month (25 prompts included).<\/p>\n\n A balanced prompt mix can look slightly different for different businesses. One thing that stays the same is that a balanced prompt mix puts most tracking effort where visibility is uncertain, and buyers are still deciding. Search marketers can make the biggest difference in this uncertain visibility phase through prompt balancing.<\/p>\n Branded search should always show the brand anyway. Conversions on branded search should always be strong, as this is the type of search people make when they\u2019ve made their decision.<\/p>\n Paid search data from my boutique search marketing agency, forank<\/a>, shows the relationship between branded search and conversions clearly. My business partner, Leigh Buttrey<\/a>, runs paid advertising for a leading London catering company, and I manage the company\u2019s SEO and AEO efforts. Paid campaigns show exactly which searches drive conversions and provide insight into user behavior where keywords are concerned. This is data that SEO teams don\u2019t have, because Google doesn\u2019t provide conversion data for organic search. Because it\u2019s hard to know which keywords actually drive conversions, SEO can borrow from paid campaign data. A keyword that performs well for paid search will, in theory, perform well for organic SEO.<\/p>\n This year, our catering company\u2019s branded campaign generated 223 conversions at a 4.20% conversion rate. Branded search accounts for around 46% of all conversions from the company\u2019s search campaigns. The data tells a story about how customers come back and search for the business by name once they\u2019re ready to act. We see this pattern across many accounts. Vendor decisions are often made during the research phase, and the branded search is just the last step. That\u2019s why visibility in unbranded, research-stage prompts matters most.<\/p>\n Start with a ratio of queries split between unbranded, competitor, and branded. Get a feel for the data and whether it\u2019s suitable for the marketing campaigns running.<\/p>\n The unbranded, competitor, and branded categories are solid but broad. Next, make prompt tracking more useful by tracking prompts by persona or ideal client profile (ICP). Tracking by persona or ICP helps marketers ensure they\u2019re reaching every major decision-maker, which is especially important in complex B2B settings. For example, one marketing team may need stakeholder buy-in from a Chief Finance Officer (CFO), an Operations Lead, and the end user.<\/p>\n A CFO might ask for \u201csoftware that shortens month-end close,\u201d while an Operations Lead asks how to \u201cautomate invoice approvals.\u201d These audiences probably want the same product, and putting your company\u2019s product in front of both audiences increases the chances of your brand appearing in boardrooms when vendor decisions are being made. Aim to cover each priority persona.<\/p>\n Featured resource:\u00a0<\/span><\/strong>Inside the MEDDPICC Methodology<\/span><\/strong><\/a><\/p>\n Pro tip:<\/span><\/strong>\u00a0<\/span>Review the prompt mix regularly. Complete the first review after the first month. If branded prompts consistently return accurate, positive answers, consider moving those slots to unbranded or competitor prompts where there\u2019s more to learn.<\/span><\/p>\n <\/a> <\/p>\n Awareness prompts are broad and problem-focused. The buyer is trying to understand a challenge. They\u2019re not choosing a vendor. Answer engines tend to explain concepts and cite educational content, often without naming brands. Here\u2019s an example of Google\u2019s AI Overviews (AIO) for an awareness query:<\/p>\n\n Decision prompts are narrow and packed with constraints such as budget, integrations, team size, or contract terms. Answers usually name a few vendors, so these searches influence the buyer\u2019s shortlist. Here\u2019s an example of Google AIO for a decision query:<\/p>\n\n Any of the three tools mentioned in the AIO has a good chance of a website click or making the buyer shortlist.<\/p>\n Tracking prompts by buyer journey connects visibility to how buyers actually move toward a purchase. Buyer-journey prompt sets cover awareness, consideration, evaluation, and decision intent.<\/p>\n Most of the prompt set belongs in the consideration and evaluation stages, where shortlists form. Awareness prompts are worth a smaller share to check whether answer engines use the brand\u2019s content as a source, even when they don\u2019t name the brand.<\/p>\n According to 6sense\u2019s 2025 Buyer Experience Report<\/a>, B2B buyers evaluated an average of 5.1 vendors, up from 4.5 the year before. In previous years, each additional vendor on a shortlist added about two months to the buying cycle. This time, cycles actually shortened by just over a month. Buyers are comparing more options in less time, so every stage of research carries more comparison questions, and each one is a chance for a brand to make or miss the shortlist.<\/p>\n After an initial answer, buyers using answer engines will narrow down with follow-ups like:<\/p>\n \u201cWhich is cheapest?\u201d A brand that doesn\u2019t cover the details can appear in the first answer and disappear once the buyer adds constraints.<\/p>\n Since tracking tools \u200crun each prompt as a standalone question, marketers must write follow-ups as complete prompts with context. The same principles behind effective ChatGPT prompts<\/a>, including clear context, a specific role, and defined constraints, apply to writing prompts worth tracking.<\/p>\n For example, instead of tracking \u201cWhich is cheapest?\u201d track \u201cWhat\u2019s the most affordable project management tool for agencies with built-in time tracking?\u201d Add standalone follow-ups that reflect the most common next questions.<\/p>\n Pricing:<\/strong> \u201cWhich agency project management tools cost less than $15 per user per month?\u201d <\/p>\n Integrations: <\/strong>\u201cWhich project management tools for agencies integrate with HubSpot and QuickBooks?\u201d <\/p>\n Alternatives: <\/strong>\u201cWhat are the best alternatives to [Competitor] for small agencies?\u201d <\/p>\n Pro tip:<\/span><\/strong> <\/span>The best follow-up prompts come from questions sales teams hear repeatedly in discovery calls and demos, because those reflect the real objections buyers have before they commit.<\/p>\n If your prompt tracking tool allows, as HubSpot AEO<\/a> does, keep each root prompt and its follow-ups in the same tracking group<\/a>. Reporting then shows exactly where in the conversation the brand drops out. This can point to the specific content gap to fix, such as missing pricing information or undocumented integrations.<\/p>\n <\/a> <\/p>\n Search marketing efforts have the same goal as other teams in the company: they need to make money for the business. Therefore, the prompts to start tracking first should be tied to revenue.<\/p>\n Start by identifying prompts that drive clicks and revenue. I like to reverse-engineer which landing pages are being clicked. Landing pages receiving clicks from AEO are clearly working for the business. Landing pages that also convert are clearly working. Sometimes, landing pages have a clear angle, like a specific feature or audience. It\u2019s not an exact science, but marketing should be able to make some educated guesses on the type of prompts that are leading to the landing page\u2019s visibility.<\/p>\n Sales and customer service teams are a valuable source. The questions that sales teams hear repeatedly in discovery calls and demos, such as \u201cHow long does implementation take?\u201d, \u201cWill it work with our existing systems?\u201d, or \u201cWhat happens to our data if we switch?\u201d reflect the real concerns buyers need resolved before they commit. Buyers increasingly ask answer engines these same questions before they ever speak to a sales rep, so turning recurring objections into tracked prompts shows whether AI answers address those concerns, and whether they point buyers toward the brand or a competitor. Structured voice-of-the-customer methodologies<\/a>, such as reviewing call recordings or tagging common objections in support tickets, make it easier to spot which questions come up most often.<\/p>\n Featured resource:<\/span><\/strong> 51 AI Prompts for Sales Teams to Use [+Expert Tips]<\/a><\/p>\n Prompts are so varied and subjective that it\u2019s easy to become overwhelmed tracking many of them. Instead, start smaller, then build the prompts over time.<\/p>\n A starting set of 25 to 50 prompts is enough to spot patterns without creating noise. HubSpot AEO\u2019s<\/a> free trial covers 25 prompts across ChatGPT, Perplexity, and Gemini, a useful benchmark for a first set. Expand once the first month of data shows which topics need deeper coverage.<\/p>\n Pro tip:<\/span><\/strong> Before finalizing a first set, run a quick baseline. HubSpot\u2019s free AI Search Grader<\/a> is a one-time diagnostic that scores how ChatGPT, Gemini, and Perplexity characterize a brand across five dimensions: sentiment, presence quality, brand recognition, share of voice, and market position. Marketers can run it for their own brand, then for each key competitor using the same location and category, to compare how sentiment and share of voice differ across the market. Weak scores point to where tracking should start. Low presence quality suggests unbranded category prompts, while weak sentiment suggests branded prompts around the issues driving it.<\/p>\n Here\u2019s an example of what AI Search Grader says about HubSpot\u2019s opportunities:<\/p>\n\n AI Search Grader specifically shows \u201cGrowth Areas.\u201d These areas could inform decisions about marketing campaigns and prompts to target. Across two core answer engine platforms (OpenAI and Perplexity), enhanced\/advanced analytics are listed as a growth opportunity, and in Gemini, AI and automation capabilities are listed. There\u2019s some crossover between these features, so teams could create content that addresses these areas and therefore increase visibility. Tracking prompts related to these topics would then make sense.<\/p>\n Query fan-out is the process answer engines use to break a single prompt into multiple related searches behind the scenes. Google has described AI Mode and AIO working this way<\/a>: \u201cWhile responses are being generated, our advanced models identify more supporting web pages, allowing us to display a wider and more diverse set of helpful links associated with the response than with a classic web search, enabling new opportunities for exploration.”<\/p>\n A prompt like \u201cbest project management tool for a small agency with client billing\u201d might fan out into separate searches on agency project management tools, billing features, pricing, and reviews. The engine then combines what it finds into one answer.<\/p>\n\n Fan-out explains why tracking every phrasing of a question adds little value. Ten different wordings of the same question often trigger overlapping sub-queries and pull from many of the same sources. Clustering groups those variations by underlying intent, so one or two representative prompts can stand in for the whole cluster.<\/p>\n For example, \u201cbest project management tool for agencies\u201d and \u201ctop agency project management software\u201d belong in the same cluster. Clustering is best practice for three reasons.<\/p>\n More stable signals: <\/strong>AI answers vary from run to run, even for identical prompts. Reporting at the cluster level smooths out that variation and shows real trends. <\/p>\n Better use of tracking slots: <\/strong>Removing near-duplicate phrasings frees up space for prompts that cover new intents, personas, or competitors. <\/p>\n Clearer content decisions: <\/strong>Each cluster usually maps to one topic or page, so a visibility gap points to a specific piece of content to create or improve. <\/p>\n <\/a> <\/p>\n CRM data shows who buys, why they buy, and who the business loses deals to, making it one of the most reliable sources for prompt selection. Yet CRM data is often overlooked because most standalone AEO tools have no connection to a company\u2019s customer data. Without the link between AEO tool and CRM data, teams are effectively guessing at which prompts matter. The risk is that marketers end up falling back on generic category questions that may not reflect their real buyers.<\/p>\n For teams working out how to use CRM data to find prompts to track, the goal is to turn real customer patterns into prompts. Here\u2019s how to discover the prompts your marketing team should track based on data within the CRM.<\/p>\n Look for patterns in recent closed-won deals such as the industries, company sizes, regions, and use cases that appear most often. Each pattern becomes a set of unbranded prompts written from that buyer\u2019s perspective. If mid-sized healthcare companies make up a large share of new revenue, prompts like \u201cbest patient scheduling software for multi-location clinics\u201d deserve a place in the set.<\/p>\n Lost deals show where the brand is losing ground. Competitors named in closed-lost records belong in the competitor prompt set, and recurring loss reasons such as price, missing integrations, or ease of use point to evaluation prompts worth tracking.<\/p>\n Not every segment deserves equal coverage. Give more prompts to segments with the highest deal value, shortest sales cycles, or strongest retention, and fewer to segments that generate leads but rarely convert.<\/p>\n Pro tip:<\/span><\/strong> Revisit CRM-based prompts whenever the business enters a new market, launches a product, or sees a shift in who\u2019s buying. The prompt set should follow the customer base.<\/p>\n HubSpot\u2019s AEO tool<\/a> connects to CRM data from day one for Marketing Hub Professional and Enterprise customers. Prompt suggestions are based on that data, not generic industry templates, so marketers start with prompts tailored to their business and can add their own. As CRM data grows, suggestions stay aligned with how the customer base is changing.<\/p>\n Keywords and FAQs that already perform in traditional search are one of the fastest ways to build a prompt set, because they show proven demand and topics where the brand already has authority. The job is to turn short, keyword-style searches into the longer, context-rich questions buyers ask answer engines.<\/p>\n Google Search Console shows which queries already drive impressions and clicks. The most useful queries for prompt tracking are long-tail queries, which often reveal a problem or hint at who is searching, and question-style queries, which closely resemble how people phrase prompts. Exporting Google Search Console (GSC) data and sorting queries by word count is a quick way to surface them.<\/p>\n A keyword becomes a useful prompt when it includes details a real buyer would add, such as role, company size, constraint, or goal. Use CRM data to build the audience and understand the details.<\/p>\n Keep the core topic of the keyword intact, so prompt results can still be compared with organic performance for the same topic.<\/p>\n Featured resource:<\/span><\/strong> 70 AI prompt examples for marketers to use today<\/a><\/p>\n FAQs are already written as questions, which makes them easy to convert. Strong sources include on-site FAQ sections, People Also Ask results, support tickets, and pre-sales questions. Add buyer context to generic FAQs, and prioritize the questions tied to purchase decisions, such as pricing, integrations, and implementation, over purely definitional ones.<\/p>\n Teams that already organize content into topic clusters have a ready-made structure for prompts. Each pillar topic can become a prompt cluster, with the subtopics informing the individual prompts. This keeps prompt reporting aligned with content strategy, so a visibility gap in a cluster points straight to the pillar or supporting pages that need work.<\/p>\n Marketing Hub\u2019s SEO tools<\/a> make topic and prompt clustering easier for HubSpot users. They surface prioritized recommendations for optimizing site pages, suggest topics that build search authority, and integrate with Google Search Console so teams can see how queries perform, including impressions and click-through rate. That query and cluster data is a practical starting point for translating proven keywords into conversational prompts.<\/p>\n\n <\/a> <\/p>\n The right setup depends on the individual business because there are many variables, such as where buyers research, which markets drive revenue, and how much variation the team can manage in analysis and reporting.<\/p>\n According to HubSpot\u2019s 2026 State of AEO Report,<\/a> consumers\u2019 preferred platforms are ChatGPT and Google AI Overviews. These two platforms are a good place to start; however, every business should try to find out where their specific audiences are searching. For most businesses, there are three to four core platforms, including:<\/p>\n ChatGPT If teams want to discover exactly which platforms are driving traffic to a website, set up a report on Data Studio (formerly called Looker Studio) that pulls information from Google Analytics showing the source and the landing page visit.<\/p>\n GA4 specialist Kyle Rushton-McGregor taught me how to create a report in Data Studio<\/a>. Here\u2019s what my Data Studio report looks like:<\/p>\n\n I can see that ChatGPT is sending the most traffic through September, followed by Google. This indicates that AEO is working for my client, but it\u2019s important to consider that a brand will only get traffic from an AI source where it has brand visibility. GA4 can only show the click, not the missing opportunities.<\/p>\n Answer engines can return different brands, sources, and pricing depending on where a buyer is located. Tracking prompts by location<\/span> shows whether a brand appears in the markets that matter most.<\/p>\n Start with the markets that drive the most revenue or growth. Track the core prompt set in the primary market and a smaller, localized subset in secondary markets. Localized prompts should reflect how buyers there actually ask, including spelling, currency, and local regulations or competitors.<\/p>\n AI answers vary from run to run, even for identical prompts, and even on the same day. Collect data daily, which is where dedicated tools can help. Tools collecting data daily build a larger sample. Monthly reporting smooths out day-to-day variation and shows the actual trends. HubSpot AEO<\/span><\/strong><\/a> runs tracked prompts across ChatGPT, Perplexity, and Gemini daily<\/span><\/strong>, so marketers can review how brand visibility, sentiment, and citations change over time.<\/p>\n Important note:<\/span><\/strong>Teams should encourage delaying bigger decisions, such as rewriting pages or replacing prompts, for monthly or quarterly reviews. Patterns should be established over several weeks before implementing changes. It\u2019s also important not to react to every drop. Sometimes a decline is industry-wide, and the best response is to wait rather than take action.<\/p>\n HubSpot\u2019s free AI Search Sensor<\/a> tracks daily answer engine volatility across ChatGPT, Gemini, and Perplexity, along with weekly industry-level traffic, visibility, and citation trends. When a brand\u2019s own metrics drop, the volatility score helps show whether the engines shifted across the market or the change is more likely specific to the brand. That context is useful for stakeholder reporting too: it can explain a dip that affected the whole industry, or show that a brand held steady while the industry declined.<\/p>\n\n The image above shows an industry-wide visibility score in the financial services industry. Three major players in the industry dropped in September at the same time, pointing to a change in how answer engines sourced or generated responses, not a problem with any single brand\u2019s AEO strategy.<\/p>\n <\/a> <\/p>\n The prompts that prove business impact are the ones closest to revenue: prompts that lead to pages that convert, influence shortlists, or affect deals the business is already trying to win. Tracking these prompts gives marketers a way to connect AI visibility to outcomes leadership cares about, rather than reporting visibility for its own sake. The prompt types below make the strongest case for marketing\u2019s direct impact on business.<\/p>\n Prompts mapped to converting landing pages:<\/strong> Start with pages that already receive AI referral traffic and convert, then track the prompts most likely to surface them. When visibility rises for those prompts, and AI referral sessions to the same pages rise with it, marketers have a clear, page-level story to share with stakeholders. <\/p>\n Decision-stage prompts: <\/strong>Prompts about pricing, integrations, implementation, and alternatives sit closest to the purchase. A prompt like \u201cWhich project management tools for agencies integrate with QuickBooks?\u201d shows whether the brand makes the final shortlist, and gaps here have the most direct effect on pipeline. <\/p>\n Prompts for high-value segments: <\/strong>Prompts written for the industries, company sizes, and regions that generate the most revenue tie visibility directly to the business\u2019s most valuable customers. Improving visibility for \u201cbest project management software for architecture firms\u201d matters more if architecture firms make up a large share of closed-won deals. <\/p>\n Competitor prompts for lost-deal rivals: <\/strong>Tracking prompts about the competitors the business loses to most often shows whether the brand is gaining ground where it matters. Rising share of voice against those specific rivals is a meaningful business metric, not just a visibility one. <\/p>\n Branded accuracy prompts:<\/strong> Prompts about pricing, features, and plans protect revenue that\u2019s already close to closing. An answer engine that quotes outdated pricing or misses a key feature can cost a deal at the final step, so accuracy and sentiment on these prompts are worth reporting. <\/p>\n <\/a> <\/p>\n Ongoing reporting needs prompts that stay stable over time, because it\u2019s trends that help identify performance. Page-level fixes need prompts tied closely enough to specific pages that changes can be measured. The best prompts do both.<\/p>\n Core cluster prompts: <\/strong>Keep one or two representative prompts per priority cluster in the set permanently, such as \u201cbest project management tools for agencies with time tracking.\u201d Consistent wording over months is what makes trend reporting reliable. <\/p>\n Page-mapped prompts:<\/strong> Assign one to three prompts to each priority page, such as a pricing page, an integrations page, or an industry landing page. When a page is updated, visibility for its mapped prompts shows whether the fix worked. <\/p>\n Citation-gap prompts: <\/strong>Track prompts where answer engines cite competitors\u2019 pages or third-party sources but not the brand\u2019s own content. For example, \u201cWhich project management tools integrate with QuickBooks?\u201d might cite a competitor\u2019s integrations page, which points to a clear content action. <\/p>\n Accuracy prompts for key pages: <\/strong>Prompts like \u201c[Brand] pricing for small teams\u201d show whether answer engines reflect what the pricing or product pages actually say. Inaccurate answers often trace back to outdated, unclear, or missing information on the page itself. <\/p>\n Pro tip: <\/span><\/strong>When fixing pages with AI writing tools, AI brand voice training<\/a> helps keep updates consistent with the rest of the site.<\/p>\n <\/a> <\/p>\n AI prompt analytics<\/span><\/strong> show visibility. Proving impact means connecting visibility to what happens next.<\/span> <\/span>HubSpot AEO<\/span><\/strong><\/a> measures brand visibility, competitor mentions, sentiment, and citations from collected answers<\/span><\/strong>, and those metrics become more persuasive when paired with these signals.<\/span><\/p>\n AI referral traffic to mapped pages: <\/strong>Compare visibility trends for a prompt cluster with AI referral sessions to the pages that cluster supports. Rising visibility alongside rising sessions suggests the prompts are driving real visits. It isn\u2019t proof of cause, but a consistent pattern over several months is strong evidence. <\/p>\n Conversions from AI referrals: <\/strong>Track form fills, demo requests, or purchases from AI referral sources. Pages that receive AI traffic but don\u2019t convert point to a messaging or page experience problem, not a visibility one. <\/p>\n Self-reported attribution: <\/strong>Add ChatGPT or \u201cAI assistant\u201d as an option in \u201cHow did you hear about us?\u201d fields. AI-driven discovery often doesn\u2019t show up as a click, so self-reported data fills gaps analytics can\u2019t capture. <\/p>\n Sales feedback:<\/strong> Ask sales teams to note when prospects mention researching with AI tools or arrive with a shortlist already formed. Recurring mentions of specific competitors or features help confirm which prompts reflect real buying conversations. <\/p>\n HubSpot\u2019s guide to content performance<\/a> covers how to measure page-level results, which pairs well with prompt-level visibility data.<\/p>\n Pro tip:<\/span><\/strong> <\/span>Report impact at the cluster level, not prompt by prompt. Individual prompts fluctuate, but a cluster\u2019s visibility, traffic, and conversions moving together are a far more convincing story for stakeholders.<\/p>\n <\/a> <\/p>\n Prompt sets drift out of date as products, markets, competitors, and buyer language change. A quarterly review keeps <\/span>prompt management<\/span><\/strong> manageable and keeps the set aligned with the business without disrupting trend data.<\/span><\/p>\n Review performance by tier.<\/strong> Promote test prompts that revealed useful patterns, and retire prompts that haven\u2019t led to any action in three months. Prompts where the brand now performs consistently well can often move to a less frequent check. <\/p>\n Limit changes to about 20% of the set. <\/strong>Replacing too many prompts at once breaks trend lines and makes quarter-over-quarter reporting unreliable. Core cluster prompts should rarely change. <\/p>\n Add prompts for business changes. <\/strong>New product launches, new markets, newly named competitors in lost deals, and shifts in closed-won segments should all be reflected in new prompts. <\/p>\n Refresh language with sales and support. <\/strong>Ask sales and customer service teams which questions and objections have come up most in the past quarter. New recurring questions often belong in the set. <\/p>\n Treat reworded prompts as new prompts. <\/strong>Even small wording changes can change answers, so a reworded prompt shouldn\u2019t be compared directly with its old history. Note the change and start a fresh baseline. <\/p>\n Keep a change log.<\/strong> Record what was added, removed, or reworded each quarter and why. When results shift, the log helps separate real visibility changes from changes to the set itself. <\/p>\n Pro tip: <\/strong>Schedule the prompt review after quarterly business planning. New priorities, product launches, and target segments are already decided by then, so the prompt set can be updated to match.<\/p>\n <\/a> <\/p>\n HubSpot AEO<\/a> is available with Marketing Hub Professional and Enterprise or as a standalone subscription. Configuring prompt tracking<\/a> takes just a few minutes.<\/p>\n Confirm brand details. <\/strong>Navigate to Marketing > AEO<\/strong>, then enter the brand name, domain, and every brand name variation answer engines might use. <\/p>\n Review competitors. <\/strong>Edit HubSpot\u2019s suggestions down to three to five direct competitors, especially lost-deal rivals. Changes only apply to future data runs, so get this right at setup. <\/p>\n Define products and ICPs. <\/strong>Select the priority product lines and ideal customer profiles (ICPs). HubSpot uses these to generate suggested prompts. <\/p>\n Refine prompts. <\/strong>Edit suggestions to read like real buyer questions, generate more with AI, or add sales objections and follow-ups manually. For priority markets, write the location into the prompt. <\/p>\n Organize prompts into groups. <\/strong>For teams deciding how to organize prompts by product line and customer segment<\/strong>, create a group for each, so reporting shows visibility by product or audience. <\/p>\n Analyze and act.<\/strong> The dashboard covers brand visibility, sentiment, share of voice, and citations, while recommendations turn citation gaps into prioritized content actions. <\/p>\n <\/a> <\/p>\n Five to 10 prompts per priority topic, across three to five topics, gives a starting set of 25 to 50 prompts, which is enough to spot patterns without creating noise. Use one or two representative prompts per intent cluster rather than many rewordings of the same question.<\/p>\n Review the prompt set quarterly and replace as needed, but maintain as much as possible because marketing analysis needs trend data to be comparable. Add prompts sooner when the business launches a product, enters a new market, or starts losing deals to a new competitor.<\/p>\n ChatGPT, Google AI Overviews and AI Mode, Gemini, and Perplexity cover most AI search activity. HubSpot\u2019s State of AEO<\/a> found that consumers\u2019 preferred platforms are ChatGPT and Google AI Overviews. The right mix depends on the audience, so check AI referral traffic in analytics to see which engines actually send visitors.<\/p>\n No. Track the full core set in the markets that drive the most revenue or growth, and a smaller, localized subset in secondary markets.<\/p>\n
\n Which prompts should I track for awareness vs. decision?<\/a>
\n Which prompts should I track first, and how many should I start with?<\/a>
\n Which prompts should I track based on CRM-informed business context?<\/a>
\n Which prompt tracking engines, locations, and cadence should I use?<\/a>
\n Which prompts should I track to prove business impact?<\/a>
\n Which prompts should I track for ongoing reporting and page-level fixes?<\/a>
\n How to Tell Which Prompts Are Working<\/a>
\n How do I maintain my prompt set quarterly?<\/a>
\n How to Configure Prompt Tracking In HubSpot AEO<\/a>
\n Frequently Asked Questions About Prompt Tracking.<\/a>
\n Track fewer prompts, but track smarter.<\/a> <\/p>\nWhich prompts should I track to reflect real buyer intent?<\/h2>\n
How do branded, unbranded, and competitor prompts work?<\/h3>\n
Branded Prompts<\/h4>\n
Unbranded Prompts<\/h4>\n
Competitor Prompts<\/h4>\n
How should I balance my prompt mix?<\/h3>\n
Start with a type ratio.<\/h4>\n
Spread prompts across personas and products.<\/h4>\n
Which prompts should I track for awareness vs. decision?<\/h2>\n
How should I represent follow-up intent in a prompt set?<\/h3>\n
\n \u201cDoes it integrate with Slack?\u201d <\/p>\nWhich prompts should I track first, and how many should I start with?<\/h2>\n
Identify prompts that generate revenue.<\/h3>\n
Speak to sales.<\/h3>\n
Start with 25 to 50 prompts.<\/h3>\n
How do query fan-out and clustering help avoid tracking noise?<\/h3>\n
Which prompts should I track based on CRM-informed business context?<\/h2>\n
1. Start with closed-won deals.<\/h3>\n
2. Mine closed-lost reasons and competitors.<\/h3>\n
3. Weight toward high-value segments.<\/h3>\n
How do I translate keywords and FAQs into prompts?<\/h3>\n
Start with long-tail and question queries in Google Search Console.<\/h4>\n
Add the context a buyer would include.<\/h4>\n
Turn FAQs into full questions.<\/h4>\n
Map topic clusters to prompt clusters.<\/h4>\n
Which prompt tracking engines, locations, and cadence should I use?<\/h2>\n
Choose engines based on goals.<\/h3>\n
\n AI Mode and AI Overviews
\n Perplexity <\/p>\nFocus on priority markets.<\/h3>\n
Maintain a reporting schedule.<\/h3>\n
Which prompts should I track to prove business impact?<\/h2>\n
Which prompts should I track for ongoing reporting and page-level fixes?<\/h2>\n
How to Tell Which Prompts Are Working<\/h2>\n
How do I maintain my prompt set quarterly?<\/h2>\n
How to Configure Prompt Tracking In HubSpot AEO<\/h2>\n
Frequently Asked Questions About Prompt Tracking.<\/h2>\n
How many prompts should I track per topic to start?<\/h3>\n
How often should I update or replace prompts?<\/h3>\n
Which engines are the most important to track right now?<\/h3>\n
Do I need to track prompts for every market we sell into?<\/h3>\n