Challenge:

At McCann Health Engagement, the analytics team managed large volumes of data from multiple sources. Manual ETL processes and inconsistent data validation slowed reporting cycles and created governance challenges as the data ecosystem grew.

Solution:

Senan redesigned the ETL architecture using R for data ingestion and transformation and SQL within Amazon Redshift to automate processing and reporting workflows. He also implemented data validation checks and role-based access controls (RBAC) to ensure consistency, accuracy, and stronger data governance across reporting systems.

Impact:

The new system reduced data processing time by over 30%, significantly improving reporting efficiency and enabling teams to access insights faster. The improved infrastructure also strengthened data governance and reliability across the organization’s analytics workflows.

Challenge:

A client lacked a reliable framework to understand how different marketing channels contributed to customer acquisition. Their existing attribution approach was incomplete, making it difficult to optimize marketing spend across channels.

Solution:

Senan developed a Marketing Mix Modeling (MMM) framework using R and multiple linear regression to quantify the impact of different marketing channels on sales and acquisition outcomes. By integrating historical campaign data, media spend, and external variables, he identified the marketing levers that most influenced performance.

Impact:

The analysis enabled the client to reallocate marketing budget toward the most effective channels, resulting in a 15% increase in customer acquisition and more confident, data-driven marketing investment decisions.

Challenge:

At Horizon (Big), the strategy team relied heavily on the analytics team to run incrementality analyses, creating bottlenecks and slowing down test-and-learn experimentation.

Solution:

Senan built a self-service analytics platform using R Shiny that automated test and control market selection, causal impact analysis, and reporting. The tool integrated multiple data sources and generated automated reports using R Markdown, allowing strategy teams to run their own incrementality studies without requiring technical support.

Impact:

The solution dramatically increased the speed and frequency of experimentation, giving strategy teams greater autonomy and enabling faster optimization of media investments and campaign performance.