Synthetic Data

The core problem synthetic data solves is the tension between needing large amounts of high-quality training data and the practical constraints of privacy, cost, and availability. Historically, marketers relied on real customer data to build predictive models—but real data comes with privacy risks, regulatory compliance headaches, and often isn't diverse or complete enough for robust AI training.

Think of synthetic data like a movie set: instead of filming in a real city, you build a realistic replica that captures all the essential characteristics. An AI model trained on this replica performs just as well on real scenarios, but you've eliminated privacy exposure and gained complete control over the data's properties.

In practice, this shows up in marketing tools in several ways. A CDP (customer data platform) might generate synthetic customer profiles to test segmentation logic before applying it to real audiences. An email marketing platform could use synthetic data to train personalization models without exposing actual customer behavior. A demand generation tool might create synthetic account lists to validate targeting criteria before spending budget on real prospects.

The technical process involves training a generative AI model on real data, then having it produce new records that statistically resemble the original without copying it. The result is data that's diverse, privacy-safe, and often more balanced than real-world datasets (which are frequently skewed toward your best customers).

For CMOs evaluating AI tools, synthetic data capability matters because it reduces implementation friction. You can pilot AI solutions faster, test more scenarios, and comply with privacy regulations (GDPR, CCPA) more easily. It also levels the playing field for smaller companies that lack massive first-party data advantages.