Optimise B2B ads

B2B ad accounts flood you with data—impressions, clicks, view-throughs—yet only a few metrics move pipeline. I spent years chasing the wrong numbers until one quarter of heroic click-throughs produced zero revenue. The fix was a simple optimisation loop that starts with a clear testing hierarchy and ends with written learnings the whole team can see.

This chapter shows that loop. You will order tests by impact, measure against hard benchmarks, decide on winners with confidence and document insights so future campaigns start smarter. Follow the steps and your optimisation meetings will last ten minutes, not an afternoon.

Start with a testing hierarchy. Fix the biggest leaks first. Level one is tracking accuracy—ensure conversions fire correctly. Level two is audience fit. Level three is hook and creative. Level four is bid strategy. Skip levels and you risk polishing noise while hidden errors bleed budget.

Work top down. If tracking misfires, pause spend and repair tags before touching copy. When tracking is clean but cost per lead is high, audit audience filters. Only test creative once data and targeting hit baseline health.

Write the hierarchy in your playbook so new hires do not waste budget on headline tweaks when pixels are broken.

This ordered approach needs targets to aim at; benchmarks come next.

Benchmarks turn red flags into numbers. For mid-funnel LinkedIn campaigns I use four. Click-through rate above 0.6 %. Cost per lead below one-point-five times target customer acquisition cost. Landing-page conversion over ten %. Qualified-opportunity rate above twenty % of leads.

Set baselines from your past sixty days or industry studies if history is thin. Update quarterly. Market shifts can raise click prices or tighten conversion windows. Static goals become unfair and demoralising.

Display benchmarks beside live dashboards. Green, amber and red colour codes speed daily checks. When a metric flips to amber, consult the hierarchy to decide what to test first.

With numbers in place, you must know how to interpret them. Decision rules follow.

Adopt simple decision maths. Change one element at a time and wait for at least one hundred conversions or seven days, whichever is longer. Declare a winner only if the result beats control by fifteen % with ninety-five % confidence. Smaller gains often vanish on rollout.

If neither variant wins, keep the control and test a bigger variable, such as a new hook. When a metric fails the benchmark by over thirty %, return to audience research instead of micro-tweaking bids. Large gaps signal strategic mismatch, not tactical error.

Log each decision in a shared sheet: test name, hypothesis, volume, outcome and next action. This log stops you from retesting dead ideas six months later.

Recording is vital. The final section explains how to capture and share learnings.

Create a learning library in Notion or Google Sheets. One row per test. Columns include campaign tags, stage, asset link, result and a one-sentence takeaway. Tag rows with winning or losing to filter fast.

Set a fortnightly ritual. The campaign owner adds two lessons, good or bad. The growth team scans highlights in a ten-minute stand-up. Sharing failures saves more budget than celebrating wins alone.

Archive outdated tests after a year to keep the library actionable. Insights decay as platforms change formats or targeting rules.

With learnings documented you close the optimisation loop and start the next cycle from a higher base.

Effective ad optimisation follows a clear path. A testing hierarchy fixes the largest leaks first. Benchmarks reveal which number matters now. Decision rules prevent false wins, and a shared library turns isolated tests into lasting knowledge.

Run the loop fortnightly, record every outcome and watch costs fall while pipeline rises. Your budget now works like compound interest, each insight stacking on the last.