Challenge: At McCann Health Engagement, the analytics team managed vast amounts of data from multiple sources, requiring a more streamlined and automated approach to ETL processes for faster reporting and decision-making. As the volume and complexity of data grew, improving efficiency and data governance became a priority. Solution: Senan redesigned the ETL processes using R scripts for data wrangling and ingestion and SQL queries in Amazon Redshift to automate and optimize data transformation, processing, and reporting. He also implemented data validation checks to ensure consistency and accuracy across reporting systems. Additionally, he introduced role-based access controls (RBAC) to improve data security and governance. Impact: These enhancements led to a 30% reduction in data processing time, significantly improving reporting efficiency. Teams were able to access insights faster, allowing for more data-driven decision-making and improved campaign performance analysis.

Challenge: A client needed a clearer understanding of how different marketing channels contributed to customer acquisition, but their attribution model was incomplete and unreliable, making it difficult to optimize budget allocation. Solution: Senan conducted a Marketing Mix Modeling (MMM) analysis using R and Multiple Linear Regression to quantify the impact of various marketing channels on sales and customer acquisition. By integrating historical data, external factors, and media spend, he identified the most effective marketing levers and optimized budget distribution. Impact: The insights from the MMM analysis enabled the client to reallocate marketing spend more efficiently, leading to a 15% increase in customer acquisition and improved decision-making for future campaigns.

Challenge: At Horizon (Big), the strategy team relied on manual, ad-hoc processes to run incrementality analysis, often requiring significant support from the analytics team. This dependency created bottlenecks, slowing down decision-making and limiting the team’s ability to run test-and-learn experiments efficiently. Solution: Senan designed and built self-service analytics tools using R Shiny to automate test and control market selection and causal impact analysis. The tools integrated data from multiple sources, provided intuitive visualizations, and allowed the strategy team to conduct their own incrementality studies without requiring technical expertise. He also incorporated automated reporting with R Markdown to generate detailed analyses on demand. Impact: By streamlining the process, the strategy team gained greater autonomy, reducing their reliance on the analytics team and enabling faster insights. This led to more efficient test-and-learn cycles and improved decision-making on campaign effectiveness, ultimately optimizing media investments and business outcomes.