A buyer can research a need, compare options, and act without leaving one ChatGPT conversation. That changes what it means to be visible: an organic recommendation and a paid placement can reach the same person, but they do different jobs.
In this on-demand webinar, Go Fish Digital and OpenAI discussed how to build the evidence that supports earned visibility, where ChatGPT ads fit, and what to measure when the path to a sale is hard to trace.
ChatGPT vs. ChatGPT Ads: Earned Answers & Paid Placements Are Separate #
Abhilash Edathil, who works on OpenAI’s monetization team, demonstrated a travel-planning conversation in which an ad appeared alongside, but apart from, the organic answer. He said the placement could draw on the conversation’s context and, where a user permits it, personalization. He was explicit that ads do not inform the answer. AJ of Go Fish Digital described an answer’s brand recommendation as earned visibility and a labeled ad as paid visibility; two different brands appearing in those positions is not a contradiction.
That distinction matters for planning. A paid test may put an offer in front of someone during a relevant conversation, but it does not buy inclusion in the model’s answer. Conversely, an earned mention is not an ad impression or proof that the buyer clicked through. Watch the travel-planning demonstration to see how the placement and answer appeared together.
Measure GEO As A Pattern, Not A Fixed Ranking #
AJ argued that a single prompt response is too variable to function like a traditional rank position. His suggested framework has three parts:
- Presence (is the brand mentioned or cited for a buyer question?)
- Representation (is that description accurate and current?),
- Competitiveness (how often does it appear relative to relevant rivals?).
To make that assessment useful, start with roughly 20–40 questions customers actually ask, run them repeatedly over a week or two, and keep the model and settings consistent. Look for recurring gaps instead of treating one answer as a verdict. Patrick Algrim added that a brand should examine the quality of a recommendation, not merely count mentions: the evidence available about its products and services shapes what the system can explain.
Fix The Evidence On Your Site Before Chasing Mentions #
When asked whether to prioritize a company website or off-site mentions, both Go Fish speakers started with the site. Algrim used a moving-company example: if a company offers cross-country moves but never says so on its site and has no corroborating evidence elsewhere, it should not assume an AI system will infer that service. Its own pages need to answer the questions a buyer would ask, specify what the company actually does, and stay consistent with external descriptions.
On why marketers should start with their own pages, Patrick Algrim said (33:44): So start with your website because you can control it. Make sure everything is, you know, again, factually true, connected to a cohesive story about your brand, product, service, what it is that you offer, and, and really just start there.
AJ added a technical first check: make sure the relevant pages are accessible to search crawlers rather than blocked by robots rules or a CDN. Reviews, PR, and other external evidence still matter, but they cannot rescue an unclear or contradictory account of the business.
Test Ads Against An Objective; Treat AI Referrals As Partial Data #
In the audience Q&A, Edathil said ChatGPT ads were shown to eligible adults in the Free and Go versions, not paid versions, and that availability by market and vertical was evolving. He pointed advertisers to OpenAI’s ads manager to check current eligibility and start a test. For measurement, he recommended choosing the desired outcome first, reach, traffic, or conversions, connecting conversion data through the available pixel or API when appropriate, and changing creative, bids, or budgets based on results. Those are his descriptions of the platform at the time of the webinar, not a guarantee of availability or performance for every advertiser.
AJ warned that AI influence can disappear from last-click reporting: a person may discover a brand in a chatbot, search for it separately, or paste a URL into a browser. GA4’s AI-assistant referrals therefore show only part of the journey. He suggested supplementing them with customer self-reporting and trends in branded and direct traffic, while keeping assistants separate in reports. Algrim recommended testing specific website or campaign changes over time rather than assigning every increase to a single source without evidence.
What To Do Next #
The practical takeaway is to choose a buyer problem and test a focused improvement—not to chase every new GEO tactic. A useful first pass is:
- Write down the buyer questions that matter and check recurring answers for presence, accuracy, and competitor comparisons under consistent settings.
- Audit the pages that should answer those questions: clarify services, eligibility, and differentiators, then check crawler access and conflicting off-site claims.
- If paid placement fits the objective and the account is eligible, run a limited ChatGPT ads test with defined outcomes and conversion measurement.
- Review AI referral data alongside branded demand, direct traffic, and customer-reported influence; document changes so later results have context.
Watch the full on-demand roundtable for the ad demonstration, the speakers’ GEO measurement discussion, and their answers to the audience’s attribution questions.
Join Us For Our Next Webinar! #
Google On What’s Next In AI Search + 5 Local Marketing Strategy Fixes
Join us as Caroline Dissaux, Business Development Lead for Search & Gemini at Google, and Bonnie White, Strategic Partnerships Manager at Adecco supporting Google on Google Business Profile partnerships, walk through what comes next in Google Search and its AI innovations. Krystal Taing, VP of Solutions at Uberall, covers all 5 local marketing strategy fixes that respond to those changes.