This case study is about scaling lead generation through systematic work with creatives and campaign optimization. The recruitment agency's task was to increase the number of candidates (leads) for vacancies through the paid Meta Ads channel. Importantly, this wasn't about short-term growth, but about a system that makes it possible to predictably increase the number of applications without an uncontrolled rise in the cost per lead.
The recruitment niche has its own specifics: a low entry threshold for the user (it's easy to leave an application), but at the same time high competition for attention and trust. Most ads look the same, with typical promises and overloaded creatives. So there's a risk of banner blindness and unstable campaign performance.
Another challenge is the mistaken bet on “trendy” formats, especially video, which don't always meet the needs of this category.
Our solutions were built around systematic analytics and iterative testing.
Analytics stage
First, our team carried out a competitor analysis. We studied not only job offers but also the niche's visual approaches, message structure and how often creatives were refreshed. The insight we found: most competitors' creatives are overloaded with details, which slows down perception.
In parallel, we analyzed the audience. The main focus was on behavioral factors:
- what triggers people to apply (speed of getting hired, clear conditions with transparent figures)
- what the barriers are (distrust, the market's “gray” reputation)
This became the basis for forming creative hypotheses.
Production and testing
We prepared photo and video creatives, and of course the strategy included A/B testing from the start. We also created several creative groups with different approaches to presenting value:
- rational (conditions, salary, specifics)
- simplified (minimal text, focus on action)
Campaigns were launched with a traffic objective to quickly collect data and train the algorithm. This helped avoid restrictions at the start and reach a wider pool of users.
Optimization and scaling
Once data had accumulated, systematic optimization began:
- switching off ineffective creatives
- scaling combinations with the best CPL
- regular performance monitoring
- adding new ad sets to avoid audience fatigue
This approach lets us move to a rational, data-driven decision-making system.
Now let's move on to the campaign results. Here's what we got:
— Spend: 217,998.56 PLN
— Leads: 33,427
— Average cost per lead: 6.5 PLN
— Clicks: 247,833
— Average cost per click: 0.88 PLN
From an analytics perspective, the key result isn't just the volume of leads, but stable economics while scaling. The cost per lead stayed under control even as traffic grew, which shows the chosen strategy was valid.
Conclusions
Creatives: photos performed better than video. This confirms that the format should match the context of consumption, not trends.
Geography: targeting users who are actually located in Poland worked best. Local context directly affects conversion, so geotargeting needs to be taken into account.
Process: the biggest effect came not from any single hypothesis but from systematic work: analytics → testing → optimization → scaling.
