Data is the lifeline for digital advertising. As the digital landscape continues to change, so too will data. Whether it’s evolving data privacy regulations or third-party cookie deprecation, access to high-quality data—data that’s clean, well structured and free of bias—is a challenge for marketing organizations.
As data becomes increasingly difficult to collect, store and activate, digital marketing leaders need solutions that maintain effectiveness while protecting their customers’ privacy. Enter: Synthetic data.
Synthetic data is a class of generative AI that can optimize scarce data, mitigate bias or preserve data privacy. Synthetic data sets are artificially generated (i.e., not obtained from direct observations of the real world) to retain the statistical and behavioral aspects of real data sets without compromising the privacy of those individuals from which the data was collected.
While marketers may be tempted to dismiss synthetic data as “fake data,” it can actually be quite powerful. Synthetic data can be used to generate data sets that would otherwise be impractical because of collection limitations or regulatory restrictions, making it both available and applicable to various marketing objectives.
We will likely see a wider embrace of generative AI technologies like synthetic data in targeted advertising within the next two to five years. Marketers need to prepare for the day synthetic data will become a norm in advertising right now.
First and foremost, synthetic data is a potentially viable solution to the common privacy challenges of sharing data across partners, including for the purpose of targeted digital advertising campaigns.
While the technology to anonymize data sets via synthetic data is still relatively immature, synthetic data in theory could protect personally identifiable information (PII) of a company’s customers such as social security numbers, phone numbers, email addresses and sensitive data like race and gender.
Today, the process behind the anonymization and sharing of real first-party data can be time-intensive and expensive. Synthetic data, on the other hand, can be created based on the original “real data” set to form a synthetic one that no longer contains any of the original PII or sensitive data information.
For instance, a large U.S. insurance company used synthetic data to anonymize complex transactional data sets that contain PII by extracting the data sets’ statistical information and complex relationships. The new synthetic data set no longer contained any original information and could not be traced back to the individuals, ensuring regulatory compliance while preserving the statistical attributes and trends of consumer behavior. The company could then share the data with third parties securely for behavioral analysis of account transactions in three days rather than six months. In advertising, this could help to create segments for specific targeted ads or building unique customer journeys.
Similar examples will only continue to emerge to protect consumer privacy: By 2025, Gartner expects synthetic data to reduce personal customer data collection in a way that avoids 70% of privacy violation sanctions.
The use of AI and machine learning (ML) is on the rise in marketing, and one such technology supported by synthetic data is the deepfake, a type of synthetic media that replaces existing videos or audio with synthetically generated images or audio.
