The Challenge
The client relied on manual, subjective marketing spend allocations, creating significant inefficiencies across an annual budget exceeding $600 million. High data latency and fragmented integrations across 80+ ad networks severely restricted accurate, real-time campaign performance tracking. Without a scalable infrastructure to rapidly deploy regression models, the organization struggled to optimize cost-per-action and maximize overall ROI.
The Solution
Nineleaps engineered an end-to-end DevOps and MLOps framework to automatically aggregate high-volume marketing data across diverse global channels. The team established robust continuous deployment pipelines to seamlessly integrate iterative regression models and automate budget forecasting. This empirical, data-driven ecosystem eliminated human error and slashed data latency from weeks to hours, enabling highly scalable decision-making.
Services
- BI & Self Service Analytics
- Data Engineering
