Marketplace platforms don’t struggle with ideas. They struggle with conversion systems.
When Inclumodels, a talent marketplace platform based in Hyderabad, approached AI Marketing Lab at the growth stage, the brand had presence and intent in the market—but acquisition wasn’t predictable. Traffic existed, but lead flow wasn’t consistent. The missing layer was a structured funnel that could convert attention into action, and action into qualified enquiries.
This case study covers how we built a performance engine focused on lead generation for marketplace platforms, using a segmented approach that prioritised both volume and quality—without losing control of the funnel.
Before the engagement, user acquisition was limited—not because demand was absent, but because the path from interest to submission was not engineered.
At the time of onboarding, we observed:
Inconsistent acquisition, with no repeatable funnel structure
Traffic not converting into potential customers with predictable frequency
No system to guide a prospect from first impression → intent → form submission
For marketplace businesses, this becomes a scaling barrier. When the acquisition funnel is unclear, lead flow depends on chance, not control. That makes monthly planning difficult and growth unstable.
The objective was direct and measurable:
Generate 150 qualified leads per month consistently.
Not inflated sign-ups. Not vanity traffic.
Qualified leads that could be followed up and converted into real marketplace demand.
To achieve this, we delivered:
Performance Marketing
Meta Ads as the primary growth driver platform
We intentionally focused on performance as the core lever, because the fastest path to predictability was funnel engineering through paid acquisition—supported by better message-to-audience match and optimisation at the submission point.
he strategy was built around one core principle:
Segment the funnel to scale both lead volume and lead quality.
Instead of relying on a single campaign and broad messaging, we engineered segmented acquisition funnels that improved:
Volume through scalable campaign structure
Quality through tighter message relevance
Conversion rate through lead form optimisation
This was executed as a Meta ads lead generation strategy tailored specifically to Hyderabad (hyperlocal)—so the funnel stayed aligned with real on-ground demand and realistic targeting.
We kept execution focused on three high-impact actions:
Multi-campaign scaling framework
We structured multiple campaigns and funnels to separate audiences by intent and response behavior, allowing scaling without collapsing performance consistency.
Creative testing across audience clusters
We tested creatives and messaging across different audience types to improve relevance and reduce mismatch—an essential factor in improving qualified inflow for marketplace platforms.
Lead form optimisation
Since leads were captured via Meta instant lead forms, we optimised form structure and flow to reduce friction and increase completion rate.
Together, this ensured conversion funnel optimisation from impression to submission—without depending on randomness.
The results exceeded the initial goal substantially.
₹10,000 total ad spend
3,000 Meta instant lead form submissions
90-day campaign duration
~1,000 leads per month average
Target was 150 per month (≈450 leads in 90 days)
Approximate CPL: ~₹3.3
Campaigns were hyperlocal (Hyderabad only), proving that focused segmentation and funnel control can outperform broad targeting—especially for marketplace-style businesses.
Inclumodels didn’t just reach a number.
It built a repeatable lead engine.
“AI Marketing Lab has really helped us grow with their performance marketing. We started seeing better leads and more visibility almost immediately. They understood our agency’s ‘you are the owner’ approach and made the whole process simple and effective.”
Marketplace growth is rarely about traffic alone. It’s about funnel precision.
By combining segmented campaign architecture, creative testing, and instant lead form optimisation, Inclumodels shifted from inconsistent acquisition to a scalable, predictable system. In performance marketing, structure determines scale—and this case is a clear example of that principle in action.
