Targeted advertising in social networks for a reproductive medicine clinic
How to get 117 leads in three months and achieve a reduction in the cost of the application from $31.34 to $18.78
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Case: Targeted advertising in social networks for a reproductive medicine clinic
Input data
Client: reproductive medicine clinic.
Timing: 90 days.
Team: project, targetologist, designer, proofreader.
Problem: the clinic needed to consistently attract new patients but had low recognition of services and high competition in the niche.
Tasks:
launch advertising campaigns in Meta with clear audience segmentation and simple messages that emphasize the clinic's expertise;
ensure a stable flow of applications through the site, regularly testing creatives, audiences and placements to optimize the cost of the application.
List of works
1
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Niche analysis and selection of optimal campaigns. We researched the target audience and determined the most effective goal. We focused on Meta Ads, where the audience is most active on the topic of women's health and reproductive medicine.
2
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Development of creatives for key services. We created a series of banners and videos explaining the benefits: consultations with a reproductive specialist and gynecologist, free IVF under the NHSU, preventive check-ups, and mammological examinations. We made the messages as simple and understandable as possible.
3
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Formation of audiences and launch of the test. We set up a wide audience and segments by interests: women's health, pregnancy planning, IVF, gynecology, and mammology. Test campaigns allowed us to analyze user behavior and identify the most relevant groups.
4
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Placement optimization. We analyzed where users most often leave applications. We identified the most effective placements – Facebook Feed, Facebook Reels, Instagram Reels and Stories – and redistributed the main budget to them.
5
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Testing audiences and creatives. We conducted a series of A/B tests with different visuals, headlines, and audience segments. We identified the most productive combinations that provided the lowest cost per lead.
6
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Scaling after a successful test. After obtaining stable results, we increased the advertising budget. The campaigns continued to generate high-quality applications while maintaining the optimal cost of conversion.
Results
In three months, we built an effective patient acquisition system. In the first month of the test, we received 6 applications for $31.34. After optimizing creatives, audiences, and placements, in the second month, the number of conversions increased to 51, and the cost per lead decreased to $18.78. In the third stage, during scaling, we received 60 applications at an average cost of $22.66. The campaign provided 117 new calls from the site and demonstrated stable growth in efficiency.
The image shows the first month’s performance: 6 conversions for $31.34
The screenshot shows a decrease in cost to $18.78 and an increase in conversions to 51
The screenshot shows a stable flow of 60 applications with an average cost of $22.66
Results
In three months, we built an effective patient acquisition system. In the first month of the test, we received 6 applications for $31.34. After optimizing creatives, audiences, and placements, in the second month, the number of conversions increased to 51, and the cost per lead decreased to $18.78. In the third stage, during scaling, we received 60 applications at an average cost of $22.66. The campaign provided 117 new calls from the site and demonstrated stable growth in efficiency.
The image shows the first month’s performance: 6 conversions for $31.34
The screenshot shows a decrease in cost to $18.78 and an increase in conversions to 51
The screenshot shows a stable flow of 60 applications with an average cost of $22.66
Conclusions
A flexible testing strategy, high-quality creatives, and the right audience segments allowed us to establish a stable flow of applications for a reproductive medicine clinic. In 90 days, we attracted 117 potential patients and significantly reduced the cost of conversion. The campaign showed high efficiency in a competitive niche.
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