• Growth Elements
  • Posts
  • Your Ad Experiments Are Noise: How to Test Google, LinkedIn, and Social Without Burning CAC in 2026

Your Ad Experiments Are Noise: How to Test Google, LinkedIn, and Social Without Burning CAC in 2026

Author

Chintankumar Maisuria
February 19, 2026

In partnership with

Read time: 3 minutes.

Welcome to the 195th edition of The Growth Elements Newsletter. Every Monday and sometimes on Thursday, I write an essay on growth metrics & experiments and business case studies.

Today’s piece is for 8,000+ founders, operators, and leaders from businesses such as Shopify, Google, Hubspot, Zoho, Freshworks, Servcorp, Zomato, Postman, Razorpay and Zoom.

Today’s The Growth Elements (TGE) is brought to you by:

Want to get the most out of ChatGPT?

ChatGPT is a superpower if you know how to use it correctly.

Discover how HubSpot's guide to AI can elevate both your productivity and creativity to get more things done.

Learn to automate tasks, enhance decision-making, and foster innovation with the power of AI.

Thank you for supporting our sponsors, who keep this newsletter free.

2026 paid reality​

  • Auctions are more expensive; AI automates bidding and targeting.​

  • Teams run many tiny “tests” across Google, LinkedIn, Meta, YouTube.

  • CTR/CPC look fine; pipeline, SQOs, CAC payback don’t.​​

  • Most ad “experiments” are just random spending with no decision logic.

[1] Give paid one primary job​

  • Capture: high‑intent search, branded, competitor, review retargeting.

  • Warm: ICP lists + visitors on LinkedIn/social.

  • Amplify: hero content and proof into target accounts.​

[2] Design tests per surface​

  1. Google/search: test intent clusters + offers (demo, diagnostic, ROI tool); success = SQOs, CAC, payback per cluster.

  2. LinkedIn: test narratives (problem POV vs feature vs proof) on named ICP lists; success = ICP reach, visits, SQOs from those accounts.

  3. Paid social/video: use mostly for remarketing + explanation; success = re‑engagement and opps from remarketed cohorts.

  4. Tier‑1 metrics everywhere: pipeline, SQOs, CAC, payback (click metrics are diagnostics only).​

[3] Key pointers for “real” experiments​

  • Budget: set a minimum monthly spend per surface (low‑thousands) or don’t call it a test.

  • Time: 4-8 weeks or until you hit X number of SQOs/opps; no 3‑day tests.

  • Decision rules (pre‑defined): what “scale”, “iterate”, and “kill” look like vs your CAC/payback baseline.​​

  • Maintain a simple experiment log: hypothesis, job, surface, budget, outcome.

[4] What not to do​

  • Don’t optimise for MQL volume while win‑rate and payback deteriorate.​​

  • Don’t run £200-500 trials on new channels and declare them “dead.”

  • Don’t treat AI‑black‑box campaigns as experiments if you can’t segment by intent or audience.

  • Don’t expect ads to fix broken product, ICP, or activation; they only scale what’s already there.​

That's it for today's article! I hope you found this essay insightful.

Wishing you a productive week ahead!

I always appreciate you reading.

Thanks,
Chintankumar Maisuria