Case study
Out Adventures
6,618 leads on roughly $28k in Meta spend, with cost per lead cut from $7.47 to $3.64 as volume grew.

- Client
- Out Adventures
- Services
- Paid Social, Measurement and Analytics, AI Integration
- Audience
- Established
- Timeline
- Feb 2026 to present
The impact
6,618
Leads Generated
$3.64
Cost per Lead, Down from $7.47
4.34%
CTR, Up from 2.26%
The Challenge
Out Adventures runs small-group travel for gay and queer travelers, from Antarctica expeditions to European river cruises. Their growth engine is lead generation: travelers who raise a hand for a trip and enter a sales conversation with a real human.
The math of that model lives and dies on cost per lead. When we took over Meta advertising in February 2026, the job was simple to state and hard to do: more qualified leads, at a lower cost, from the same budget.
The Strategy
We treated the account as a system that should get smarter every month. Each month's published report fed the next month's plan: which audiences produced leads that converted, which creative was fatiguing, which trip categories deserved more budget.
Lead quality stayed in the definition from day one. We optimized against actual lead events, not the inflated multi-counted actions ad platforms like to report, so the numbers the client saw matched the names in their CRM.
What We Built
- Meta lead generation campaigns structured by trip category, so budget follows demand for specific adventures
- A creative testing cadence that retired fatigued ads before performance decayed
- Monthly optimization cycles documented in published reports, each one naming what changed and why
- Per-campaign health tracking so underperformers were caught inside the month, not after it
The Results
From February through early June 2026, the program generated 6,618 leads on roughly $28k in Meta spend. Cost per lead fell from $7.47 to $3.64 while monthly volume grew. Click-through rate climbed from 2.26% to 4.34% over the same period.
That is the opposite of how paid media usually ages. Costs fell as the system scaled because every month's learnings were applied to the next, and every month's numbers were published where the client could check them.
Our roles

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