Synthetic Data in Digital Marketing: Current Trends and Applications

Data has become the lifeblood of modern marketing, yet privacy regulations, yet as global data production accelerates—estimated to surpass 180 zettabytes by 2025—the accessibility and reliability of data have become significant challenges. Increasingly stringent privacy regulations, coupled with the adoption of privacy-enhancing technologies, are constraining marketers’ ability to derive actionable insights from available datasets. These constraints underline the necessity for innovative approaches to maintain analytical robustness while respecting data protection frameworks.

Challenges in Data Collection and Utilization

The regulatory landscape surrounding data usage is defined by frameworks such as the European Union’s ePrivacy Directive and the General Data Protection Regulation (GDPR). These frameworks mandate explicit consent for data collection and processing activities, introducing significant limitations for online marketers. Concurrently, public awareness of privacy rights has led to widespread adoption of ad blockers, intelligent tracking prevention technologies, and privacy-focused web browsers. For instance, browsers such as Safari and Firefox integrate tracking prevention mechanisms that significantly diminish the availability of user data for analysis.

Empirical data underscores the scale of the problem. A global brand reported a 70% decrease in consent rates following the implementation of a compliant consent banner devoid of manipulative design elements, highlighting the operational impact of adherence to ethical data practices. This phenomenon has been characterized as the “data crocodile,” symbolizing the growing disparity between data volume and actionable data availability. The reliance on traditional third-party cookies exacerbates this issue, as these mechanisms are increasingly rendered ineffective by regulatory and technical constraints.

Synthetic Data as an Analytical Paradigm

Synthetic data emerges as a viable solution to the aforementioned challenges. Defined as data generated through advanced algorithms to replicate the statistical properties of real-world datasets, synthetic data enables robust analysis without compromising individual privacy. It offers the dual advantages of maintaining compliance with data protection laws and addressing the analytical requirements of online marketing.

The technology is bifurcated into two primary categories:

1. Partially synthetic data, where a subset of variables is replaced with synthetic values, retaining some original data points. This approach presents a moderate risk of re-identification.
2. Fully synthetic data, wherein all variables are algorithmically generated, providing enhanced privacy assurances but requiring rigorous validation to ensure data utility and security.

Synthetic data is increasingly recognized by regulatory authorities as a privacy-enhancing technology. Entities such as the UK’s Information Commissioner’s Office (ICO) and the European Data Protection Supervisor (EDPS) have endorsed its potential, framing it as a pivotal tool for aligning analytical practices with legal mandates.

Practical Application: JENTIS’ Synthetic Users

JENTIS exemplifies the application of synthetic data through its Synthetic User feature, which leverages machine learning models to address data scarcity caused by non-consented data loss. The process commences with the collection of data from consenting users, which is utilized to identify predictive variables These variables form the basis for generating Synthetic User profiles. This methodology allows for the simulation of non-consenting user behavior, yielding datasets that approximate the statistical characteristics of the complete user base.

The system incorporates robust privacy safeguards. Non-consented data is processed in a minimized and time-bound manner, with all such data being deleted within 24 hours post-synthesis. Furthermore, JENTIS prioritizes first-party data collection, ensuring that organizations retain control over data processing. This approach facilitates compliance with data protection frameworks while mitigating dependency on third-party analytics providers.

Benefits of synthetic data in marketing analytics

The integration of synthetic data into marketing analytics confers several advantages:

• Regulatory Compliance: By decoupling datasets from identifiable individuals, synthetic data aligns with the GDPR and analogous regulations, enabling ethical analytics.
• Data Scalability: Synthetic data generation mitigates the challenges of limited sample sizes, producing robust datasets that support comprehensive trend analysis and scenario modeling.
• Extended Data Retention: Unlike personally identifiable information, synthetic data can be stored over extended periods, facilitating longitudinal studies and strategic forecasting.
• Campaign Optimization: Synthetic datasets support rigorous A/B testing, predictive modeling, and segmentation analysis, enhancing campaign precision and return on investment (ROI).

Challenges and limitations

Despite its utility, synthetic data is not devoid of challenges. The fidelity of synthetic datasets is contingent upon the quality and representativeness of the original data used for synthesis. Inadequate or biased input data can propagate inaccuracies within synthetic outputs, undermining their reliability. Additionally, while fully synthetic data minimizes re-identification risks, these risks are not entirely eliminated, necessitating continuous evaluation of synthesis algorithms.

Conclusion

Synthetic data represents a transformative innovation in the domain of online marketing. It reconciles the competing imperatives of data-driven decision-making and rigorous privacy compliance, offering a scalable and ethical alternative to traditional data collection methodologies. By addressing the limitations imposed by privacy regulations and technological barriers, synthetic data enables marketers to sustain analytical efficacy while respecting user autonomy.

The future trajectory of synthetic data will likely be shaped by advancements in algorithmic design and regulatory clarity. As the technology matures, it is poised to play a central role in the evolution of marketing analytics, facilitating informed decision-making and fostering trust between businesses and consumers.

Credits:

This post is a reproduction of an article originally published on OneTrust DataGuidance. All rights remain with the original author, Mira Suleimenova, Director of Compliance and Legal at JENTIS GmbH, and platform. For the full article, visit this link.