Paid, restructured around pipeline
Paid was optimising toward the cheapest lead, which meant the algorithm was actively hunting for the worst ones. We re-pointed the whole account at pipeline value and let it find fewer, better buyers.
Scaffolding. This case study is a template showing the structure that works. Rewrite it with a real engagement and real figures, then remove template: true from its frontmatter.
- 9% → 34%est
- MQL → SQL
- −41%est
- Cost per qualified lead
- 2.4×est
- Pipeline from same budget
What was broken
Every campaign was optimising to form fill. The bidding algorithms did exactly what they were told and went looking for the people most likely to submit a form — a population that overlaps only loosely with people likely to buy.
Cost per lead looked excellent. Cost per customer was quietly getting worse every quarter, and nobody could see it because the two numbers lived in different reports.
What I built
- Offline conversion imports. Qualified and closed-won events pushed back to the ad platforms, so bidding optimises against revenue instead of a form submission.
- A restructured account. Consolidated where the data was too thin to learn from, separated where intent genuinely differed. Fewer campaigns, more signal per campaign.
- Negative keyword and audience hygiene. A recurring pass rather than a one-off cleanup, because the waste regenerates.
- A single pipeline view. Spend, leads, qualification rate and pipeline in one place, so the tradeoff between volume and quality is visible while it is being made.
What changed
Lead volume fell. Qualified pipeline from the same budget went up meaningfully. This is the hardest result to communicate internally, because the metric most people had been watching for two years got worse on purpose.
If your paid team is rewarded on cost per lead, you are paying them to make this problem worse.