The Cleanest Brand in r/PPC's ChatGPT Ads Test Drew 0% CTR
Three U.S. media buyers compared notes on r/PPC this week after running ChatGPT Ads beta campaigns through April and early May 2026, and the click-through rates landed at 0%, 1.15%, and 2.4%. The cleanest brand drew zero. Format, creative, and category fit moved performance further than placement or bid, which puts the early ChatGPT Ads playbook closer to display than it is to search.
The three accounts, and the three datapoints
The r/PPC thread is short, specific, and unusually grounded for what these threads usually look like. Three operators (a DTC apparel brand, a B2B SaaS reselling productivity tools, and a mid-market home services lead-gen account) ran ChatGPT Ads beta inventory during April and early May 2026. Spend ranged from roughly $4,000 to $22,000 per account over four to six weeks. The CTRs they reported were 0% for the apparel brand, 1.15% for the lead-gen account, and 2.4% for the B2B SaaS reseller.
Those are tiny samples. A few thousand impressions on apparel can absolutely come back at 0% if the placement skewed toward queries with weak commercial intent, so I would not bet the agency on three datapoints. What is interesting is the shape. The brand most marketers would have picked to win (clean creative, strong product imagery, recognizable category) finished last. The reseller running text-forward, problem-framed ads about software stacks finished first.
OpenAI has said the beta is still in early testing, with an explicit goal of "learning and refining the experience for consumers before expanding it more broadly." That means buyers are essentially co-writing the auction logic with OpenAI right now, which is roughly the same window holdco trading desks are still locked out of.
Why the cleanest brand finished last
The apparel account, by the buyer's own description, ran the kind of creative that wins on Meta. Bright product imagery, low text density, a single offer, a two-line headline. ChatGPT ad units do not look anything like a Meta feed. The placement sits next to a long-running conversation a user is actively trying to complete. A photo-led ad reads, in that context, like noise the user has to step around.
The reseller's ad copy was almost the opposite. It read like a Reddit comment itself: "we tested three project management tools for a 12-person team and the cheapest one shipped first." It used the format of the surrounding conversation rather than fighting it. From what I have seen, the placement seems to reward ads that match the syntax of the surface they sit in, not the polish of a Meta thumbstop.
That idea is not entirely new. Native ads on publisher pages followed the same rule for a decade. ChatGPT Ads just made it relevant again for performance teams who had stopped thinking about format as a creative lever.
The 0% to 2.4% spread, against the beta-wide numbers
Similarweb data summarized in NoGood's beta breakdown puts the overall ChatGPT Ads CTR at roughly 0.68%, with the top quartile at 1%, the best-performing brands clustering near 1.57%, and an observed peak of 5.4%. Digiday reported a comparable 0.91% average across early advertisers when OpenAI flipped on cost-per-click pricing.
Plugged into that range, the three r/PPC datapoints map cleanly. 0% sits below the bottom of the distribution because the apparel account had small volume and likely concentrated in low-intent placements. 1.15% sits just above the top quartile. 2.4% lands between the top quartile and the best-performing brand cluster.
The takeaway I would draw is unglamorous. The public benchmarks are not lying. If you are budgeting against them, treat 1% as the median you should be at to call a campaign healthy, and treat 2% as the threshold where you start asking why you are not getting more inventory. The peak 5.4% number is not a goal for a 4 to 6 week test. It is a unicorn moment somebody is going to write a case study about.
Pricing is still moving while the data is forming
This is the bit that makes the r/PPC numbers more useful than they look on the surface. OpenAI started the beta with a $60 CPM and a $200,000 minimum commitment, which ALM Corp's reporting captured as the initial premium positioning. The platform has since switched to cost-per-click, with rates landing in the $3 to $5 range, and CPMs have fallen toward $25 in some cases. The total U.S. pilot generated $100 million in revenue over its first six weeks per the same source.
A 2.4% CTR at a $4 CPC works out to roughly $96 per thousand impressions of paid attention. That is the math of premium podcast inventory or a small-publisher native buy. A 0.91% CTR at the same CPC works out to about $36 per thousand impressions, which is much closer to social. The reason the same beta is being described as both "too expensive" and "weirdly cheap" depending on who you ask is that the CTR variance is doing all of the work, not the CPC.
If you are running a test budget right now, do not benchmark spend efficiency on cost-per-thousand. Benchmark it on CTR-adjusted cost-per-attention. That is the only number that survives the pricing model changing again, and OpenAI has already changed it twice. We covered the self-serve CPC window a few weeks ago, and the r/PPC numbers are the first practitioner-side datapoints I have seen that confirm what the trade reports were predicting.
A measurement layer that will not catch up before Q3
One advertiser in Winbuzzer's coverage of the early beta reported spending just 3% of a $250,000 budget over several weeks, because OpenAI's Ad Manager tool had a glitch that hid the campaign data. That is exactly the problem the three r/PPC operators were working around. They reconstructed CTR by hand because the platform UI was not giving them the report cleanly.
Anyone running a meaningful beta test should keep an offline log of impressions, clicks, and any post-click conversion data you can capture through your own pixel. Do not assume the dashboard is the source of truth. OpenAI's published documentation is still light on attribution windows and bidding logic, and the trade press is filling in those gaps from advertiser interviews rather than from official guidance.
This is the same playbook every new ad platform has run for the last ten years. Snap, TikTok, even Meta's first iteration of Advantage+ all had a 12 to 18 month window where the public benchmarks moved faster than the platform's own reporting. ChatGPT Ads is sitting inside that window right now. Anyone who waits for clean attribution before testing is going to be bidding against the case study that someone else got to publish first.
The setup I would actually run on a beta budget
Three things from this thread that I would act on this week:
1. Strip the Meta creative thinking out of your ChatGPT Ads brief. Build ad copy that reads like a comment inside the surrounding thread, not a thumbstop. From what I have seen, native to the surface beats polish on this placement, and the apparel result is the cleanest example of that pattern so far.
2. Test in a category where text-forward problem framing makes sense. B2B SaaS, professional services, lead-gen for considered purchases. Apparel and other visually driven categories are not where I would burn the first $10,000 of beta budget. Reseller copy that reads like a Reddit comment is doing the format work for you.
3. Treat the dashboard as a draft. Run a manual log of clicks and conversions in a spreadsheet. The platform reporting will lag what your account is actually doing, and you will need the offline number when you are talking to your CFO about renewal.
The interesting question is not whether ChatGPT Ads work. It is which brands are still cheap to acquire customers on this surface before someone publishes the case study that gets the CPCs bid up. From what I have seen in the trade press and now in this r/PPC thread, that window is probably six to eight months wide. After that, the cost-per-attention math starts to look a lot more like Meta in 2017.
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