Media & Entertainment

Digital Advertising Data Analytics

Turned billions of raw advertising data points into automated fraud detection and real-time campaign intelligence for a global AdTech company.

Overview

TatvaSoft developed a scalable analytics platform that processes billions of impressions, clicks, installs, actions, and campaign-spend data points to deliver timely, actionable insights. The solution automates extraction from multiple data sources, identifies fraudulent activity, calculates advertiser refunds, and monitors campaign performance. Stakeholders get transparent reporting on key metrics including ROAS, CPI, and CPA without any performance or cost bottlenecks.

Customer

A global AdTech company headquartered in Singapore delivering AI-powered advertising solutions that help mobile marketers optimize campaign performance and user acquisition.

Singapore

Challenge

Processing billions of advertising impressions, clicks, installs, and actions while maintaining acceptable query performance and data-processing costs.
Designing optimized BigQuery data-extraction strategies for high-concurrency reporting and operational requests.
Identifying non-human traffic, suspicious clicks, and complex advertising fraud patterns from large volumes of raw campaign data.
Accurately calculating advertiser refund amounts based on detected fraudulent activity and invalid engagement.
Replacing static spreadsheets with interactive dashboards for campaign health, spend distribution, fraud metrics, ROAS, CPI, and CPA monitoring.
Synchronizing data extraction, fraud analysis, reporting, and performance-summary generation through scheduled automated workflows.
Resolving data discrepancies and supporting customized reporting requirements without disrupting regular campaign operations.

Digital Advertising Data Analytics Case Study

Solution

Our team analysed the client’s advertising data operations, campaign-performance workflows, fraud-detection requirements, and reporting processes. Accordingly, TatvaSoft developed a scalable data management and campaign analytics framework that enabled the client to process high-volume advertising data, detect fraudulent activity, calculate advertiser refunds, automate recurring workflows, and monitor campaign performance through interactive dashboards. The solution used Google BigQuery, SQL, Python, Google Colab, Google Looker, and Jenkins to support fast data extraction, advanced analysis, automated processing, and transparent campaign reporting.

Below are the major capabilities delivered through the solution

BigQuery Data Orchestration

Optimized SQL queries extract detailed campaign metrics from large advertising datasets while maintaining query performance and controlling processing costs.
High-Volume Data Processing

The solution processes billions of impressions, clicks, installs, actions, and campaign transactions across a global mobile advertising ecosystem.
Campaign Performance Analytics

Campaign spend, conversions, ROAS, CPI, CPA, engagement quality, and other performance indicators are analysed to support data-driven optimization.
Automated Fraud Detection

Python-based analytical scripts identify non-human traffic, suspicious click patterns, invalid engagement, and other advertising fraud indicators.
Fraud Refund Calculation

Refund amounts owed to advertisers are automatically calculated based on detected fraudulent traffic and invalid campaign activity.
Interactive Performance Dashboards

Google Looker dashboards provide visibility into campaign health, spend distribution, fraud metrics, conversion performance, and ROAS.
Automated Data Pipelines

Jenkins schedules and manages data extraction, fraud analysis, report generation, and recurring performance-summary workflows.
Fraud & Performance Reporting

The platform automatically generates recurring fraud reports and campaign-performance summaries, reducing dependence on manual reporting.

Expertise

Data Warehouse & Analytics

Google BigQuery
SQL
High-Volume Data Processing
Query Optimization
Campaign Data Analysis

Scripting, Visualization & Automation

Python
Google Colab
Google Looker
Jenkins
Fraud Detection
Refund Calculation
Automated Data Pipelines and Performance Dashboards

Result

TatvaSoft successfully delivered a scalable data analytics and campaign support framework for the media and entertainment domain, enabling the client to process high-volume advertising data, automate fraud detection, calculate advertiser refunds, and monitor campaign performance through centralized dashboards. The solution connected BigQuery, Python-based analysis, Google Looker, and Jenkins-driven automation to reduce manual effort, improve financial accuracy, and provide greater transparency across advertising operations. The platform benefited the client by:

Automating complex campaign data extraction, processing, fraud analysis, refund calculation, and recurring report generation.
Reducing manual operational effort through scheduled data pipelines, automated monitoring, and error-alert mechanisms.
Improving the detection of non-human traffic, suspicious clicks, and invalid engagement across large advertising datasets.
Ensuring more accurate advertiser refunds and protecting the integrity of campaign spending and financial reporting.
Providing real-time visibility into campaign KPIs, spend distribution, fraud metrics, ROAS, CPI, and CPA through interactive dashboards.
Enabling a more secure, transparent, and high-performing advertising environment that can scale with the client’s global advertiser base.

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