Turning Ad Impressions Into Measurable Impact
Every digital advertisement generates data: impressions, clicks, conversions, and user engagement signals. But which ads actually cause someone to act? This project builds a real-time system that captures advertising interactions as they happen and applies causal analysis to determine true incremental impact, transforming raw marketing data into automated, decision-ready insight.
The research was presented at the Graduate Poster Exhibition during the 2025 SPARK! (Showcase of Projects, Art, Research, and Knowledge). Developed within the Master of Science in Data Science program, the project was completed by Aryan Siddhabathula and Darshan Deepak Arugollu. Their work integrates web engineering, database architecture, causal inference, and machine learning into a unified, automated pipeline for advertising optimization.
Abstract: Real-Time Causal Ads Optimization – An Integrated Web-Based Data Collection and Automated Analysis Pipeline
Part 1: Data Collection and Ad Display Web Application
This phase of the project focuses on designing and implementing a web application that not only displays advertisements to users but also captures real-time interaction data, including impressions, clicks, and contextual details. Developed using Java and Spring Boot, the application logs each ad event alongside critical metadata such as session information, device type, and time of day, storing the information in a structured database.
By ensuring high-quality, real-time data capture, Part 1 provides the foundation for automated causal analysis in Part 2. This streamlined data pipeline enables the immediate availability of fresh insights, supporting a continuous feedback loop that informs ad targeting and budget allocation decisions as soon as new data is collected.
Part 2: Real-Time Causal Analysis and Automated Ads Optimization
Building on the data collection pipeline from Part 1, this phase integrates internal ad interaction logs with external datasets, such as the Criteo Attribution Dataset, to conduct real-time automated causal analysis for advertising optimization.
Using advanced causal inference and machine learning techniques, the system estimates advertisements’ incremental impact, or uplift, on key metrics such as conversions and user engagement, rather than relying on correlation-based approaches. The automated pipeline continuously ingests new data, recalibrates causal models, and generates actionable recommendations in near real time.
This approach enables marketers to dynamically refine targeting strategies, allocate budgets more effectively, and maximize return on investment through data-driven decision making.
Graduate Poster Exhibition at SPARK!
The Graduate Poster Exhibition celebrates the research and creative work of the graduate community, showcasing everything from prose and code to original research and artistic expression. As part of SPARK! (Showcase of Projects, Art, Research, and Knowledge), a reimagining of Research Week, the exhibition highlights the depth, range, and impact of graduate scholarship and invites the campus community to engage with ideas taking shape across disciplines.
Mastering Data: The Data Science Program
The Master of Science in Data Science at Rutgers University–Camden is an interdisciplinary program that equips students with skills in data analysis, machine learning, and statistical modeling. The curriculum integrates tools from mathematics, computer science, public policy, and prevention science, emphasizing practical applications across various industries. Core courses include Foundations of Data Science, Statistical Methods for Data Science, Data Visualization, and Applied Data Mining and Machine Learning. Students also engage in a thesis or capstone project, applying their knowledge to real-world problems.
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