If AI-led campaigns keep producing form fills that sales rejects, the system may be succeeding at the wrong task. A thank-you page tells an ad platform that an action occurred. It does not tell the platform whether the lead was qualified, reachable, commercially relevant, or likely to become revenue.
Your first job is to connect those business outcomes to acquisition. Your second is to make the offer equally clear on the landing page, in the feed, across map profiles, and inside every creative asset. Do those two things before increasing spend, and automation has a much better signal to optimize.
Key takeaways: what to fix before spending more
Optimize toward business quality, not raw form volume. Define an accepted lead, return downstream statuses from the CRM, and keep diagnostic actions separate from primary conversion goals.
Make the offer unambiguous. A visitor and an automated system should both be able to identify what you sell, who it is for, why it matters, what action to take, and what happens next.
Measure each funnel stage on its own terms. Awareness, consideration, lead capture, qualification, opportunity creation, and revenue do not share one useful success metric.
Treat feeds, map listings, structured data, pages, and creative as one information system. Conflicting names, categories, locations, or conversion labels weaken both targeting and attribution.
Audit placements as well as campaigns. Automated campaigns can reach visual discovery surfaces that behave differently from conventional text search, so a blended click-through rate can hide what changed.
Teach the buying system what a qualified lead means
Begin in the CRM or lead management system, not in the bidding interface. Write down the point at which an inquiry becomes worth pursuing. That definition might depend on service fit, geography, budget, need, or another criterion your sales team already uses. The exact criteria are yours; the important part is that marketing, sales, the CRM, and the ad platform use the same definition.
I am thrilled to share the news of an exciting new partnership that is set to revolutionize the way we connect AI visibility data to tangible citation outcomes and impacts.
This collaboration promises to enhance the visibility of AI-generated insights and effectively translate them into actionable citations, thereby amplifying their real-world influence.
In a world where AI continues to drive change and innovation, ensuring that these contributions are recognized and used is crucial, and this partnership is a significant step in that direction.
In this report, I’m going to walk you through a comparison of conversion rates among the four leading AI chatbots: ChatGPT, Gemini, Claude, and Perplexity.
From May 2025 through April 2026, my research team conducted an in-depth study on AI conversion rates across various industries. We used anonymized data from more than 150 client companies, honing in on the most popular generative AI chatbots. Building on our previous analysis of ChatGPT conversion rates, we noted that most companies in our dataset had invested in generative engine optimization. The fascinating results of our study are presented below.
While all chatbot traffic converts at higher rates than traditional SEO, my study shows that ChatGPT and Perplexity typically have higher conversion rates compared to Gemini and Claude. This might be due to the greater user trust vested in ChatGPT and Perplexity’s recommendations.
Claude stands out in knowledge-driven and regulated industries. Its performance in Healthcare, Higher Education, and Industrial IoT indicates that professionals in these fields favor Claude for more detailed, analytical queries.
Industries such as Engineering, Software Development, and Transportation & Logistics exhibit relatively low conversion rates overall. This might suggest less dependence on AI tools or more specialized workflows not captured within this dataset.
B2B SaaS and Financial Services demonstrate moderate but closely clustered conversion rates across all models, likely reflecting significant but cautious AI adoption given potential compliance concerns and familiarity with AI limitations.
If you want a PDF copy of this report or wish to know more about our GEO services, reach out here.
First Page Sage Internal Research Study, February 2026, First Page Sage.
During a recent study, I discovered that Reddit stands out as the most-cited domain in AI-generated answers. In fact, it’s ahead of heavyweights like YouTube and LinkedIn, thanks to an analysis of 30 million sources conducted by Peec AI, a tool specializing in AI search analytics.
The findings: I’ve learned that Reddit claims the top spot across various AI platforms including ChatGPT, Google AI Mode, Gemini, Perplexity, and AI Overviews. Top contenders YouTube, LinkedIn, Wikipedia, and Forbes are right behind. Platforms like Yelp and G2 frequently appear when searching for recommendations.
As I delved deeper into the research, it became clear which domains the AI models tend to lean on:
ChatGPT values Wikipedia, Reddit, and editorial sites like Forbes.
Google shows preference for platforms such as Facebook and Yelp.
Perplexity favors Reddit, LinkedIn, and G2 for queries within the B2B realm.
Why we care: The insight that resonated with me was the importance of having authority beyond just our own websites. Brands that consistently feature on reputable third-party platforms have a better chance of being cited by AI.
Why these sources? It’s fascinating to see how AI systems are wired to prioritize both authority and authentic user input:
I’ve found that Reddit excels because it mirrors genuine user discussions.
YouTube shines in video citations, owing to their comprehensive transcripts and descriptions.
Wikipedia not only serves real-time data but also acts as a foundation for training datasets.
About the data: The analysis spanned 30 million sources, providing a comprehensive look at how often domains are directly cited in AI answers, effectively revealing what shapes these responses.
Have you ever wondered why most GPTs in businesses fail to be truly effective? It’s often because they are either too broad or haven’t been properly tested. Allow me to guide you through building focused, high-ROI GPTs that your team will not only adopt but use consistently every week.
The OpenAI GPT Store made waves in January 2024 with its launch, hosting over three million custom GPTs. But, if you ask teams how many they actively use, the answer tends to be disappointingly low, often zero or just one.
I’ve personally built and audited over a dozen custom GPTs for marketing, SEO, and sales teams. The pattern is consistent: only a select few are used daily, while the rest simply collect dust. Let me share with you a practical approach to crafting GPTs that your team will genuinely engage with—from identifying suitable use cases to structuring, testing, and launching them effectively.
If you’re eager to dive in, start with these foundational steps: Choose a task your team performs at least three times a week, typically taking over 15 minutes. Articulate this in a simple sentence: ‘This GPT helps [role] do [task] by [method].’
For a deeper understanding, I recommend checking out Marketing Research & Competitive Analysis or MARKETING, both highly ranked in the GPT Store’s Research & Analysis category. These projects showcase the build patterns I’ll cover here.
Now, let’s discuss what a business GPT truly entails. Unlike a generic AI assistant, a business GPT is a custom version of ChatGPT designed to handle one specific, recurring task for a particular role. Think of it like hiring a highly specialized worker for a job, rather than a generalist who does a little bit of everything.
B2B buyers start their journey long before they even search for us. I’ve learned that AI-powered Google Ads campaigns can ignite early demand and reward patience over time.
If I’m relying solely on brand and non-brand keywords in Google Ads, my growth becomes limited. A decline in performance isn’t due to the platform but the strategy behind it.
Discovering a brand doesn’t begin with a non-brand search. Buyers are researching on platforms like Reddit, ChatGPT, Facebook, LinkedIn, and YouTube. They watch demos, read testimonials, and become familiar long before actively searching for us.
For complex sales processes with lengthy customer journeys, this transformation is crucial, demanding a strategic shift. Here’s how I can make it effective in B2B.
AI-powered Campaigns: Your Growth Treasure
Over the years, Google has innovated with multi-channel, multi-asset campaigns like Performance Max and Demand Gen. These campaigns place my brand front and center as audiences research and evaluate options.
When my audience is ready to choose vendors, they’ve already built trust in my brand. They’ll search specifically for me because of the trust I’ve cultivated through consistent visibility.
A well-rounded Performance Max campaign includes diverse ad types, like image and video ads displaying demos or testimonials on YouTube. These ads also engage audiences across the web via the Display Network and retarget them as they continue their research. This process naturally leads to branded searches that ultimately convert.
Such campaigns are cost-effective, allowing me to leverage customer data alongside keywords as intelligent signals, not replacements. It’s about smarter keyword usage.
As AI Overviews and AI Mode transform Google’s search results pages, it’s time I reconsider my ad strategies to align with these changes.
I’m fond of the 4S framework: search, scroll, stream, and shop.
Adding “ask” captures how people now engage with AI tools. They consult ChatGPT or Gemini, search on Google, scroll through LinkedIn, stream videos on YouTube, and shop across numerous platforms. If my strategy focuses on only a couple of these behaviors, I’m missing the full growth opportunity.
Solely targeting keywords means missing the larger narrative. Brand keywords undoubtedly convert better, but how do people arrive at searching my brand? Consistent visibility ensures they notice my brand in their feeds.
Embrace Testing and Learn with Patience
This strategy requires time, especially in B2B settings with protracted sales cycles.
For example, it took almost a year to appreciate how Performance Max contributed to one of my life science client’s success, whose deals typically take months to finalize. There was a moment where our account manager nearly paused the campaign because initial data wasn’t promising.
Integrating sales data changed the perspective. As revenue figures rolled in, the campaign’s value became transparent.
If I can sync beyond MQLs with data like Proposal Sent, it keeps Google well-informed and offers reassurance until the sales data solidifies our insights.
Patience is key when providing the system quality data. I must remain steadfast and avoid quitting prematurely, accepting the complexity of B2B cycles.
An event might draw 100 people, some catch a webinar email later, and months pass before they search for us and request a proposal, eventually becoming customers. With long sales cycles, phenomena like this unfold subtly.
If testing funds are limited, I can designate 5% to 10% for AI-forward campaigns. Strategic testing without major commitments at peak times allows room to maneuver while the system adjusts.
Investing time in this strategy ensures sustainable growth. Those who master it gain an enduring competitive edge, unlike those focused on diminishing demand.
Have you ever wished for a tool that makes orchestrating AEO efforts a breeze? Let me introduce you to Profound Sheets, a game-changer that brings efficiency to new heights. Imagine a spreadsheet-like interface where every row acts as its own Agent run, each with its unique context. This innovative system allows me to process hundreds of inputs simultaneously, amplifying my marketing strategies beyond imagination.
By leveraging structured workflows, I’m able to accomplish what once took weeks in mere minutes. The time saved means more opportunities to focus on crafting creative strategies and optimizing performance. It’s like multiplying my marketing team’s capabilities overnight!
Hey there! I’m thrilled to share something exciting: Profound Agents now seamlessly connect with Vercel v0. This means I can generate and deploy stunning landing pages without writing a single line of code.
By leveraging my Profound AEO data as a solid foundation, deploying these pages has never been easier. It’s a game-changer for anyone looking to enhance their digital presence effectively and efficiently.
Your AI dashboard can look busy while the P&L remains unchanged. Faster drafts, more creative variants, rising AI visibility, and a lower apparent cost per task do not prove that AI created economic value.
If you need to defend an AI marketing budget, you need a credible answer to three questions: what changed compared with what would otherwise have happened, how that change became profit or cash savings, and what the change cost in full. The framework below gives you a practical way to answer them before a promising pilot becomes an expensive permanent line item.
Key takeaways
Classify every AI investment as an operational-efficiency bet, a marketing-performance bet, or a distribution-channel bet. Each requires different evidence.
Calculate ROI from verified economic benefit, not output volume, model usage, impressions, mentions, or hours theoretically saved.
Include implementation, data preparation, quality assurance, training, governance, measurement, and rework in the cost base.
Compare results with a credible counterfactual. A before-and-after improvement alone does not show that AI caused the change.
Keep released capacity separate from cash savings. Time saved has economic value only when you remove a cost or redeploy the capacity productively.
When a platform cannot provide adequate performance data, fund it as a capped learning experiment rather than presenting it as a proven acquisition channel.
Define the AI bet before you calculate its return
AI marketing is not one investment category. The label often hides three economically different bets. Combining them in one dashboard produces an attractive blended number that nobody can audit.
Operational-efficiency bets
An operational bet uses AI to reduce the resources needed for research, briefing, production, analysis, reporting, or quality control. Its first useful measures are cost per approved deliverable, cycle time, rework, throughput, and error rates.
The word approved matters. Producing twice as many drafts is not a productivity gain if editors reject more of them or senior staff spend the saved time correcting unsupported claims. Measure the complete path from request to usable output, including human review.
Marketing-performance bets
A performance bet uses AI to improve an existing marketing activity: audience selection, creative development, content optimization, lead qualification, conversion, or budget allocation. The economic question is not whether the AI produced more activity. It is whether the intervention created incremental qualified demand or contribution profit.
Pair the business outcome with a guardrail. If AI-generated landing pages increase initial conversions but attract poorly matched leads, conversion rate alone will overstate the return. Depending on your funnel, the guardrail may be qualification rate, sales acceptance, cancellation, return rate, retention, factual accuracy, or brand compliance.
Distribution-channel bets
A channel bet pays for access to an audience or invests in visibility inside an AI-mediated discovery environment. ChatGPT advertising and programs intended to improve a brand’s presence in AI answers belong here, even though one is paid distribution and the other may involve content, technical, and authority work.
Write a one-sentence investment claim before approving any of these bets: Because we will use AI to change a named process for a defined audience, a named business outcome should improve through a stated mechanism. If the team cannot complete that sentence without using words such as engagement, innovation, scale, or efficiency as substitutes for an outcome, the proposal is not ready for an ROI calculation.
Then record seven fields on an investment card:
The decision the measurement must support: scale, continue, redesign, or stop.
The exact AI intervention and the workflow or channel it changes.
The mechanism that should connect the intervention to value.
The eligible audience, campaign, account, content group, or business unit.
The baseline and the best available counterfactual.
One primary business outcome and the relevant quality guardrails.
The maximum cost, evidence standard, decision owner, and decision point.
This card prevents metric drift. A team should not begin with qualified pipeline as its goal, fail to influence pipeline, and later declare success because the model generated a large number of assets.
Build a cost and value ledger that survives scrutiny
The clean formula is simple:
AI marketing ROI = (verified economic benefit – fully loaded AI cost) / fully loaded AI cost x 100.
The difficult work sits inside the two inputs. Verified economic benefit should normally consist of incremental contribution profit and realized cash savings. Fully loaded cost should include every material resource required to produce, govern, measure, and maintain the result.
Count more than the software invoice
Your cost ledger may need the following entries:
Subscriptions, model usage, API charges, media, and platform fees.
Integration, workflow design, prompt development, and automation maintenance.
Data preparation, permissions, tagging, analytics configuration, and CRM work.
Employee and contractor time spent operating or supervising the workflow.
Editorial review, factual verification, brand review, security review, and legal or compliance review where applicable.
Training, documentation, adoption support, and process redesign.
Experiment design, holdout management, reporting, and analysis.
Rework caused by incorrect, inconsistent, duplicated, or unsuitable output.
Replacement costs for tools or services that the new system does not fully eliminate.
Use an internal labor-cost basis consistently. A billable agency rate, an employee’s loaded cost, and the opportunity value of an hour are different numbers. Switching among them to make a project look attractive turns the model into advocacy rather than measurement.
Separate profit, savings, and capacity
Incremental revenue is not incremental profit. Convert additional revenue into contribution profit by applying the relevant contribution margin and subtracting variable fulfillment costs that arise with the new business. Keep the measurement period consistent across the revenue, cost, and margin inputs.
Cash savings require an expense to disappear. A cancelled vendor contract, eliminated overtime, reduced external production spend, or a role that no longer needs to be added can create a realizable saving. A team finishing a task earlier while payroll remains unchanged creates capacity, not an immediate cash saving.
Capacity can still be valuable, but you need to show where it went. If marketers use released time to run additional experiments, improve sales enablement, or serve more accounts, measure the resulting throughput and economic outcome. If the time simply becomes slack, record the operational improvement without booking it as profit.
Avoid double counting. Suppose AI reduces editing time and the team uses that time to launch an additional campaign. If the campaign produces verified incremental contribution profit while payroll stays constant, credit that contribution profit. Do not also claim the same editing hours as a payroll saving.
Calculate the breakeven outcome before launch
A breakeven calculation gives the team a concrete hurdle before optimism enters the reporting:
Required incremental outcomes = fully loaded AI cost / contribution profit per incremental outcome.
An outcome might be a completed purchase, a retained customer, a qualified opportunity, or another event with defensible economic value. Match the event to the investment. A campaign intended to create qualified pipeline should not use raw leads as its breakeven unit merely because leads are easier to count.
If contribution varies widely, calculate more than one scenario using your own documented assumptions. Label those results as forecasts until observed outcomes replace them. The purpose is not to predict the future precisely. It is to expose what the investment must accomplish to pay for itself.
Use an evidence standard the channel can support
Attribution and incrementality answer different questions. Attribution assigns credit to a touchpoint under a chosen rule. Incrementality estimates what happened because of the marketing intervention and would not otherwise have occurred. ROI needs the second answer, even if attribution data helps you investigate the first.
Choose the strongest feasible design before the campaign begins. The following ladder runs roughly from stronger causal evidence to weaker directional evidence:
A randomized holdout in which eligible units are assigned to treatment and control.
A matched comparison using similar regions, accounts, audiences, or content groups, with known differences documented.
A staggered rollout that compares early and later groups across the same period.
An instrumented journey using permitted campaign parameters, dedicated destinations, CRM fields, offer paths, or customer-reported discovery.
An adjusted before-and-after comparison that explicitly accounts for other material changes.
Platform-reported attribution, AI visibility, impressions, mentions, citations, or production volume without a counterfactual.
Report what the design supports. A controlled test may justify a causal estimate. An instrumented path can show that a tracked interaction preceded a conversion, but it does not automatically show that the interaction caused the conversion. A visibility increase is evidence of increased presence, not evidence of revenue.
Before-and-after reporting is especially easy to misread. Pricing, promotions, seasonality, sales follow-up, product availability, competitor activity, media mix, and site changes can all move during the same period. Document those factors and use a concurrent comparison when feasible.
Measure AEO and GEO as a connected outcome chain
For AI search, answer engine optimization, and generative engine optimization, visibility belongs near the beginning of the outcome chain. Define a stable prompt set around your actual audience and buying questions. Record the model, date, conditions, brand mentions, citations, cited pages, and competitor presence. Sample consistently instead of treating one favorable response as a benchmark.
Next, connect visibility to behavior where observable: qualified referral sessions, engaged visits, branded demand, assisted leads, direct inquiries, sales conversations, and customer-reported discovery. Then connect those behaviors to qualified pipeline, purchases, retention, or contribution profit.
Do not assign revenue to an AI mention merely because a conversion occurred later. When the click trail is incomplete, present the visibility result, the observed business movement, and the uncertainty between them as separate facts. That is more useful than forcing an exact return from incomplete data.
Treat low-observability advertising as a learning purchase
When an advertising platform cannot provide the performance data needed for an incrementality analysis, cap the spend at an amount the business can afford to treat as experimentation. Write down the learning objective, the permitted instrumentation, the audience or placement being explored, and the evidence that would justify another round.
Where the format permits, use a dedicated landing path, campaign parameters, a distinct offer, CRM source fields, and a customer-reported discovery question. None of these creates a perfect counterfactual, but they can produce more decision-useful evidence than aggregate traffic and anecdotal sales feedback.
An AI pilot can improve production economics and still weaken the surrounding business model. This is particularly visible in agencies: automation reduces delivery effort, while clients expect the efficiency to lower their fees. SparkToro’s worldwide survey of agency owners put concern about AI as a potential threat at 53% in 2025, up from 44% in 2024.
Reporting only tokens consumed, assets produced, or hours removed reinforces the idea that the service is a commodity. The durable value sits in diagnosing the commercial problem, choosing the right intervention, creating defensible evidence, interpreting exceptions, and taking responsibility for the decision that follows.
Choose a pricing model that matches measurability
AI does not make every engagement suitable for performance pricing. Use the model that matches the amount of control and measurement available:
Use a fixed fee when the deliverable, quality standard, scope, and acceptance criteria are clear.
Use a retainer when the client is buying continuing strategy, experimentation, governance, and decision support rather than a predetermined volume of output.
Use time-based pricing for ambiguous discovery work where the necessary scope cannot yet be defined responsibly.
Use a performance component only when both parties agree on the eligible outcome, system of record, baseline, attribution or incrementality rule, measurement window, exclusions, data access, and payment limits.
Performance fees create disputes and potentially uncapped financial exposure when those terms are vague. Put the definitions, adjustment rules, caps, termination conditions, and audit rights in the contract, and have qualified counsel review material compensation changes.
Track contribution margin by account or service line: revenue minus direct labor, AI usage, contractors, and appropriately allocated delivery support. If efficiency improves, decide explicitly whether the gain will fund a lower price, higher quality, greater throughput, or a healthier margin. Assuming one workflow change will deliver all four at once usually hides an unpriced tradeoff.
The commercial pressure is not hypothetical. Some agency sales cycles have lengthened from 7-8 weeks to more than 12 weeks as buyers question what AI should do to price and value. Answer that question directly in proposals: disclose where automation supports delivery, define the human accountability that remains, and tie the fee to scope and economic responsibility rather than an inflated count of manual hours.
Include quality control and talent development in the model
Redesign junior work instead of deleting development. Have less-experienced marketers verify AI output against source material, document recurring failure modes, prepare experiment readouts, observe senior decision reviews, and own bounded tests under supervision. Include the supervision and training time in the investment ledger. A margin that depends on unrecorded senior rework is not a real margin.
Put every investment through a scale, continue, or stop gate
A pilot does not need perfect attribution, but it does need a precommitted decision process. At the decision point:
Scale when verified economic benefit exceeds the fully loaded cost, quality guardrails remain inside approved limits, and the evidence is strong enough for the amount of money at risk.
Continue as an experiment when the signal is promising, the uncertainty is material, and the next test has a realistic way to resolve that uncertainty.
Redesign when the mechanism appears plausible but adoption, data quality, workflow fit, or measurement prevented a fair test.
Stop when the benefit remains below the economic hurdle, guardrails fail, or the evidence gap cannot be closed at a proportionate cost.
Start with the largest AI-related line in your current marketing budget. Label it as an efficiency, performance, or channel bet. Rebuild its fully loaded cost, write down the counterfactual, and identify the strongest evidence you can obtain. If you cannot do those three things yet, move the spend into a capped experiment. Scale it only when the economic benefit and the quality of evidence can withstand the same scrutiny as any other marketing investment.
I’ve discovered that the most successful GEO and AEO strategies are deeply rooted in traditional SEO. It’s fascinating how these foundational principles seamlessly translate to AI visibility. Let me share why it’s crucial not to overlook these basics.
In our quest to harness the power of AI, many of us might feel tempted to skip straight to advanced strategies. However, without a solid SEO foundation, even the best AI-driven tactics can fall short. The rules that govern traditional SEO are critical to unlocking AI’s full potential in search visibility.
Consider this: AI systems thrive on structured data and clear content hierarchies. It’s precisely these elements that traditional SEO prioritizes, ensuring that our websites are not only user-friendly but also AI-ready. This is why every AI optimization journey should begin with tried-and-true SEO practices.
As someone who loves diving into the nuances of AI and SEO, I’ve seen firsthand how these two fields complement each other. Embracing the basics doesn’t merely prepare us for AI; it catapults our strategy into an era of smarter, more efficient digital marketing.
