Synthetic data might be the next big thing in privacy-first advertising
Martech leaders in Asia Pacific are already using synthetic data to train AI, test campaigns, and future-proof analytics
As privacy crackdowns intensify, marketers are being forced to rethink their data strategies.
The latest solution grabbing attention? Synthetic data.
This artificially generated, privacy-safe alternative is now being explored by a growing number of agencies and martech companies—particularly in Asia Pacific—as a way to fuel innovation without compromising user trust.
Forrester recently named synthetic data one of its top 10 emerging technologies for 2025. And it’s not just a future-forward theory. From campaign testing to AI modeling, real-world marketing teams are already putting it to use. This article explores how synthetic data works, why it matters now, and what it means for the future of advertising.
Short on time?
Here’s a table of contents for quick access:
- What is synthetic data and how does it work?
- Why marketers are turning to synthetic data now
- Real-world use cases from agencies in APAC
- Strategic tips for marketers exploring synthetic data
Patrecia Meliana
What is synthetic data and how does it work?
Synthetic data is information created by machines—not collected from real people—but designed to behave like the real thing.
Think of it as a digital twin of actual customer data, only without the privacy baggage. Unlike anonymized data (which can still be traced back to someone), synthetic data is built from scratch and contains no real-world identities.
For marketers, this means you can run A/B tests, simulate user journeys, or train AI models without needing to collect consented user data. Whether you’re modeling how customers move through a funnel or testing creative performance across demographics, synthetic data gives you realistic results—while staying fully privacy-compliant.
It can take many forms: structured spreadsheets, simulated browsing behavior, even AI-generated images or audio. The big win? You get accurate insights and testing power without the legal and ethical headaches of using personal data.
Why marketers are turning to synthetic data now
According to Forrester, 42% of surveyed firms are already using synthetic data as a privacy-preserving tool. But as digital marketing faces more regulation—from GDPR to China's PIPL—the technology’s strategic value is gaining ground.
AppsFlyer VP of Product Inna Weiner says the appeal is simple: “Anonymised data is fragile, but synthetic data doesn’t trace back to anyone. It’s made from scratch.” Her team has helped brands like Coca-Cola and Nike use synthetic data to prototype products and train algorithms without needing to tap into sensitive datasets.
The urgency is clear: as traditional data pipelines dry up, synthetic data offers a rare chance to preserve analytics power while staying compliant.
Real-world use cases from agencies in APAC
Agencies in the region aren’t waiting to see if synthetic data will catch on—they’re already scaling it.
At Publicis Groupe, teams in Japan and across Asia Pacific are using synthetic data for virtual focus group testing and creative validation. “We’re testing creative with a full virtual focus group comprised of data-backed personas,” said Laurent Thevenet, Head of Creative Technology, Asia Pacific. “It guides our decisions in more informed and impactful ways.”
Kuhan Kumar, CEO of Malaysia’s Digital Symphony, sees it as a game-changer. His team uses synthetic data to simulate conversion curves and predict campaign performance, particularly in data-poor markets.
“It’s how we break the tradeoff between innovation and privacy,” Kumar said. “You get full modeling power, and zero privacy baggage.”
Strategic tips for marketers exploring synthetic data
If you’re considering synthetic data, here are four strategic takeaways:
1. It’s not just a compliance tool—it’s an innovation driver.
Use it to test creative, model audience behavior, or simulate market entry strategies in places where real data is limited.
2. Treat it like a living system, not a one-time file.
Weiner warns that synthetic data must be regenerated frequently to account for seasonality or behavioral shifts. It’s a dynamic asset that requires active upkeep.
3. Start small and focused.
Pick one pain point—like media testing, audience expansion, or message personalization—and run controlled experiments. Don’t try to overhaul your data strategy overnight.
4. Beware of ‘perfect’ data.
Too-clean datasets can lead to brittle AI models. As Thevenet notes, the goal isn’t perfection—it’s realism. Include edge cases and anomalies to build more resilient systems.
Synthetic data may sound like a futuristic concept, but for an increasing number of marketers, it’s already part of their everyday stack.
As user-level tracking becomes harder to justify and harder to implement, the ability to simulate without surveillance could define the next wave of responsible, scalable advertising. Agencies that master it early will have a serious edge in the privacy-first economy.
