Tealium partnered with a leading telecoms provider to modernize its European marketing operations. The goal was to build a unified, AI-ready data infrastructure for advanced marketing automation, personalization, attribution, and fraud prevention. This initiative aimed to boost campaign efficacy, deepen customer insights and reduce fraudulent/declined orders through integrated analytics and machine learning.
1. The Challenge
The Telecoms provider identified several critical challenges that necessitated a strategic overhaul of their data and marketing stack:
- Fragmented data collection and disparate sources across websites, applications, and backend systems, leading to inconsistent analytics and operational silos.
- Limitations in marketing automation and advanced attribution due to non-standardized tagging and tracking frameworks.
- Inability to rapidly test, implement, and scale AI-driven marketing and personalization use cases.
- Operational and reputational risk impacts caused by fraudulent and declined orders, not only draining resources but also causing inaccurate measurement in campaign and marketing effectiveness.
- The need for a scalable data platform enabling real-time integration of multiple data streams, both for marketing and fraud detection purposes.
2. The Solution
To address these interconnected challenges, the Telecoms provider partnered with Tealium to execute a structured, phased transformation:
Data Foundation Setup
- Deployed a standardized data layer across all digital touchpoints, ensuring robust and consistent data collection.
- Integrated backend sources, including CDW and CRM systems, connected into a centralized customer data hub, enabling unified profiles to be accessible for both marketing and fraud analysis.
Vendor Data Source Migration
- Migrated all existing analytics and marketing tags to a new unified system using standardized rulesets, reducing complexity and data variance in a hybrid client-server architecture.
- The harmonized structure improved both measurement accuracy for attribution and data quality for fraud modeling.
System Setup
- Implemented Tealium’s AIStream for unified, real-time data flow.
- Connected to key analytics and optimization platforms, such as Adobe Analytics and Optimizely.
- Linked offline order outcomes (accepted/declined) with the central CDP, making this data available for AI and machine learning use cases, including fraud detection.
Use Case Implementation
- Established a prioritized roadmap for AI and CDP-driven initiatives – including real-time cross-device identity mapping, consent management, and real-time segmentation.
- Leveraged predictive modeling and score-based audience exclusions with AIStream and in-house machine learning systems to:
- Improve purchase propensity predictions for marketing efficiency.
- Identify, prevent, and exclude fraudulent orders based on behavioral and transactional patterns.
- Integrated operational processes for rapid deployment and iteration of both proof-of-concept and scaled use cases, combining marketing optimization with real-time fraud monitoring and measurement.
Organizational Enablement
- Delivered comprehensive training and certification, ensuring organization-wide upskilling and adoption of the new tools and processes.
3. The Results
The initiative delivered measurable benefits spanning marketing performance, fraud prevention, and operational maturity:
Efficiency Improvements
- Strategic exclusion of low-value users (predicted by purchase probability scoring models) reduced cost per visit (CPV) by 16–28% and cost per order (CPO) by 12–28%.
- The single-source-of-truth data infrastructure improved the accuracy of attribution and the quality of actionable insights, resulting in better resource allocation for campaigns.
Fraud Prevention Gains
- Predictive audience exclusion and real-time scoring models led to a demonstrable reduction in declined and fraudulent orders, validating the effectiveness of AI-powered fraud prevention in the data stack.
- Integrating offline order outcomes into modeling further improved fraud detection accuracy and reduced false positives, helping to safeguard legitimate sales and streamline the customer journey.
Operational Excellence
- Organization-wide upskilling created a common language and culture of data-driven innovation.
- Improved onboarding processes for new vendors and tools enhanced the agility of both marketing and risk operations.
Overall Summary
By establishing an AI-ready, centralized data foundation in partnership with Tealium, this Telecoms provider realized transformational gains in both digital marketing and fraud prevention.
The project unified data across silos, elevated their analytical and automation capabilities, and empowered both marketing and risk teams to act on real-time insights.
Lessons learned included the value of cross-functional collaboration, the importance of rigorous model tuning and monitoring, and the critical role of continuous organizational learning in delivering sustainable, data-driven transformation.
