Big Data & Real-Time Advertising: Research Project Outline

Introduction
In recent years, online advertising has evolved significantly—from displaying simple banner
ads on static web pages to deploying advanced, data-driven systems capable of delivering
personalized messages in real time. This transformation is largely due to the rise of Big
Data technologies and intelligent algorithms that analyze vast amounts of user information.
Businesses now prioritize relevance and precision over general exposure, aiming to serve
the right message to the right audience at the right time.
Major platforms such as Google Ads and Facebook Ads have revolutionized how
advertising works by integrating user data into their targeting mechanisms. Google Ads
focuses on users' search behavior, while Facebook leverages demographic data, interests,
and user interactions to customize the ad experience (Tuten & Solomon, 2018). These
platforms provide advertisers with tools to monitor campaign performance, set specific
objectives, and optimize results effectively.
One of the most advanced developments in this field is programmatic advertising, which
automates the entire process of selecting audiences, bidding on ad space, and displaying
ads. A key component of this system is Real-Time Bidding (RTB), where ad impressions
are bought and sold in real time through auctions as web pages load. This enables
advertisers to adjust their strategies on the fly and make data-driven decisions instantly (IAB,
n.d.).
However, the shift toward real-time, automated advertising presents new challenges. The
core problem lies in handling and analyzing large volumes of fast-moving data from multiple
sources. Advertisers must decide rapidly whom to target, which ad to show, and when to
show it. This requires robust infrastructure, including Hadoop, Apache Spark, and Kafka,
along with machine learning models capable of real-time processing (Chaffey, 2022).
Moreover, with increasing attention to user privacy and the ethical use of personal data,
companies must ensure that their practices comply with international regulations, such as
the General Data Protection Regulation (GDPR) enforced by the European Union
(European Commission, 2023). Balancing personalization and privacy has become a key
concern in digital advertising.
To guide this project, we propose the following research questions:
● How can Big Data technologies be used to improve the performance of online
advertising in real time?
● What methods are most effective for targeting users while respecting data privacy
laws?
● Which metrics best reflect the effectiveness of real-time advertising campaigns?
Some key metrics often used in digital advertising include:
● CTR (Click-Through Rate): Measures how often people who see an ad end up
clicking it.

● CPA (Cost Per Acquisition): Measures the cost of acquiring a customer through the
ad.
● ROAS (Return on Ad Spend): Evaluates how much revenue was earned for every
dollar spent on ads.
This project aims to investigate how Big Data technologies enhance the effectiveness of
online advertising in a real-time context. It will explore the systems and methods used for
user targeting, performance optimization, and ethical compliance. Ultimately, the goal is to
understand how modern advertising strategies can be both data-driven and responsible.
References:
● Google Ads Help Center. Retrieved from: https://support.google.com/google-ads/
● Facebook Business Help Center. Retrieved from:
https://www.facebook.com/business/help
● Tuten, T. L., & Solomon, M. R. (2018). Social Media Marketing (3rd ed.). SAGE
Publications.
● IAB (Interactive Advertising Bureau). Programmatic Advertising Overview. Retrieved
from: https://www.iab.com
● Chaffey, D. (2022). Digital Marketing: Strategy, Implementation and Practice (8th
ed.). Pearson Education.