Automated Paid Media Reporting System (API & Dashboard Integration)

Overview: The Automated Paid Media Reporting System was developed to replace manual reporting processes across multiple EMEA markets for global pharmaceutical and FMCG clients. Prior to its implementation, campaign reporting required significant manual data extraction and formatting across platforms, leading to inefficiencies, delays, and a higher risk of human error. The goal of this project was to create a scalable, automated solution that centralised performance data and enabled real-time visibility across campaigns.

Objective: The primary objective was to build an automated reporting infrastructure that could consolidate data from multiple paid media platforms into a single source of truth. This system needed to provide accurate, real-time performance insights across markets while reducing manual workload and improving reporting consistency. Key KPIs focused on time efficiency, data accuracy, and improved accessibility of performance data for faster decision-making.

Role & Responsibilities: As the lead on this initiative, I was responsible for designing and implementing the full reporting infrastructure. This included structuring the data pipeline, integrating platform APIs, building reporting frameworks, and ensuring outputs aligned with both internal team requirements and client expectations. I owned the end-to-end development process, from initial concept through to deployment and ongoing optimisation.

Approach & Tools Used: The solution was built using API integrations and custom scripts to extract data directly from platforms such as Google Ads, Meta, and TikTok. Data was processed and structured within Google Sheets, where automated workflows ensured consistent formatting and accuracy. This data then fed into dashboards designed to visualise key performance metrics, enabling clear and accessible reporting across multiple markets.

Challenges & Solutions: A key challenge was managing data consistency across different platforms, each with varying structures and reporting metrics. To address this, I standardised data formats and implemented transformation logic within the reporting layer to ensure alignment across channels. Additionally, handling large volumes of data required optimisation of data queries and processing methods to maintain performance and reliability.

Results & Impact: The automated reporting system reduced manual reporting time by over 10 hours per week, significantly improving team efficiency. It also enhanced data accuracy by removing manual handling and enabled real-time performance monitoring across EMEA markets. This allowed for faster optimisation decisions and improved responsiveness to performance trends, ultimately supporting stronger campaign outcomes.

Key Learnings & Reflections: This project strengthened my ability to design scalable data solutions that support performance marketing at scale. A key learning was the importance of building flexible and standardised data structures when working across multiple platforms and markets. In future iterations, further automation of data transformation layers could enhance scalability even further.