Your A/B testing program has been quietly moving budget toward your worst ads.
GrowthSpree tracked 1,412 individual ad variants across 96 accounts and $14.2 million in spend, all the way through to closed-won revenue, then ran the correlations. Cost per SQL to pipeline: 0.71. Google Search CTR to pipeline: 0.18. Performance Max CTR: 0.07. LinkedIn sponsored content: 0.04. LinkedIn boosted posts: negative 0.02.
Read that last one twice. On boosted LinkedIn posts, a higher click-through rate had a slightly inverse relationship with pipeline.
Most teams will see those numbers, nod, and change nothing, because CTR is “directional.” Here’s why that read is the expensive one. CTR isn’t sitting passively in a dashboard. It’s the number your weekly test crowns a winner on, and the winner gets more money. In 43% of the A/B tests in that dataset, the higher-CTR ad produced fewer or more expensive SQLs than the version it beat. 56% of the ads that drove pipeline had relatively low click-through rates. They lost their tests. They got defunded.
This is a thermostat wired to the wrong room.
A thermostat in the hallway doesn’t just fail to heat the bedrooms. It runs the furnace on bad information, every cycle, forever, and reports back that everything is fine. GrowthSpree found 38% of ad spend parked in the bottom two pipeline quartiles. That’s not a measurement gap. That’s a machine doing exactly what you told it to do.
They also found 67% of high-CTR ads were what they call clickbait traps. Of course they were. You ran a weekly tournament where the prize went to whichever creative was best at earning a click, and creative teams are very good at winning the game you score them on.
The fix is slow and unglamorous, which is why it doesn’t happen: move the win condition down the funnel. Cost per SQL correlated at 0.71, roughly four times CTR’s relationship to pipeline. When teams in that dataset reallocated on pipeline instead of clicks, average cost per SQL improved 44%.
The objection is fair. SQLs take weeks, clicks take an hour, and you have a Monday meeting. Fine. Then stop calling the Monday number a decision.
Seth Godin landed the same idea from the other direction this week: “If you win an auction to get a click, you probably overpaid.” Every other bidder stopped. That’s why you won.
The thermostat isn’t broken. It’s measuring a room nobody sleeps in.
Worth Your Time
- ChatGPT referrals to B2B sites nearly quadrupled in a year (DemandGen Report / Demandbase): Monthly ChatGPT-referred visits hit 2.6 million in June 2026, up from roughly 645,000 a year earlier, while Perplexity declined and Gemini and Claude stayed flat. If you’re planning a multi-engine AEO push, the traffic says go deep on one before you spread across four.
- Top ABM performers are 3x more likely to have a documented AI roadmap (DemandGen Report / ForgeX): The headline is 59% versus 23%, but read the stat underneath it: half of all respondents, including 43% of top performers, still can’t quantify AI’s ROI. The roadmap is buying coordination, not proof, and it’s worth knowing which one you’re selling upstairs.
- Gamma hit $100M ARR with no sales team, and its CEO calls that a mistake (SaaStr): 50 employees, 600,000 paying subscribers, $167 average annual customer value. Grant Lee’s line is the one to steal: “Self-serve growth generates so much signal that it starts to feel like strategy.”
- The most helpful review isn’t from your most experienced user (Kellogg Insight): Across 26.8 million Steam reviews of 27,170 games, the most helpful positive reviews came from people with the most or the least experience, while the best negative ones came from the middle. Your review-solicitation emails almost certainly go to power users only, so you’re harvesting half the curve.
- Stop writing for your boss and start writing for the buyer (Product Marketing Alliance): Pushpay’s Stefan Gladbach builds the case on “Got Milk?”, a line that nearly died in internal review over grammar. Same failure mode as this week’s Take: the approval chain scores messaging on a metric the buyer never sees.
- LLM traffic converts differently, so stop landing it like paid search (Search Engine Land): The author’s own dataset puts LLM referral conversion at 20% and 61% above paid search, so treat that as one team’s proprietary read, not a benchmark. The transferable part is the mechanic: AI Mode queries run about three times longer, meaning the visitor lands with the decision mostly made.
- Specific numbers make your claims 42.9% more believable (HubSpot): Nicolas Guéguen asked 300 passersby for either “a little time” or “37 seconds,” and the oddly precise ask moved compliance from 63 people to 90. Worth a pass over your homepage for every number you rounded off to sound less fussy.
- Community pulse: the sharpest thread in r/ProductMarketing came from a B2B SaaS marketer asking why AI tools keep comparing their product against competitors their customers never consider. The models have the category right and the comparison set wrong, and nobody in the thread had a fix beyond publishing better comparison content.
Wondering where your growth engine is leaking? The Growth Gap Diagnostic is where I’d start. Or just hit reply and tell me what you’re stuck on.