For decades, understanding audiences has relied on observing real people and real behaviour. That’s the foundation for high-quality data, providing the trusted evidence organisations use to make confident decisions.
Today, AI is creating new ways to build on that foundation. Models not only analyse data, but also generate realistic language, simulate conversations and reproduce complex behavioural patterns.
That opens up new possibilities: exploring ideas faster, testing scenarios at scale and generating data that reflects real-world behaviour without exposing real individuals.
In today’s media landscape, where organisations need faster answers, stronger privacy and greater confidence in decision-making, that matters more than ever.
That is where synthetic data comes in.
What is synthetic data?
Synthetic data is artificially generated data designed to reproduce the patterns and behaviours found in real-world data.
For some, it sounds like science fiction. For others, risky or less trustworthy than “real” data. But that misunderstands what synthetic data is.
Think of it like a weather forecast that creates patterns from historical data to predict what comes next. And we make real decisions – umbrella vs sunglasses – based on it every day.
Synthetic data works in a similar way. Grounded in real-world data, it creates a realistic representation of behaviour – without relying on identifiable individuals.
It can replicate:
- Audience behaviour
- Viewing and consumption patterns
- Customer journeys
- Motivations and opinions
- Survey responses
How it works
Synthetic data starts with real data from real people.
AI models learn from datasets – such as panels or survey responses – by identifying relationships, behaviours and patterns. Once trained, they generate entirely new data that behaves like the original, without revealing the identity of anyone in the original dataset.
In simple terms:
1
Real data teaches the model
2
The model learns patterns and behaviours
3
New synthetic data is generated from that learning
The result is realistic, scalable and privacy-safe data – ready for research, modelling, analytics and decision-making.
Where synthetic data makes a real difference
Faster insights
Teams can test ideas, simulate scenarios and explore hypotheses more quickly than traditional research allows.
A media owner planning a new content format, for example, no longer needs to wait weeks for focus group results – synthetic audiences can indicate likely reactions in a fraction of the time.
Greater privacy
Synthetic datasets do not contain real individuals, which reduces privacy and compliance risks when handling sensitive information.
For businesses operating across multiple markets with varying data regulation, synthetic data offers a way to work with realistic audience behaviour without touching personally identifiable information.
Access to hard-to-reach audiences
Synthetic data can simulate niche or difficult-to-recruit groups, making it easier to study behaviours that are otherwise hard to capture.
Young men – one of the hardest groups to recruit through traditional panels – can be modelled and queried on demand.
Better testing
Organisations can use synthetic data to:
- Test / refine: Test and refine content, ads, concepts and messaging before putting them in front of real audiences or investing in research, production or media.
- Explore: Simulate audience segments and explore “what if” scenarios.
For example, a marketer could simulate how different audience segments might respond to a new campaign at an early stage, using synthetic audiences to refine the concept or messaging before testing it with real people or making any investment.
This makes synthetic data particularly valuable as an early-stage research tool: it can help organisations explore what might work, narrow down options and identify the questions worth taking to real audiences.
At Fifty5Blue, we are applying these capabilities as part of our Digital Twins solution, where synthetic personas are built by combining AI with our own panel and TGI consumer data. These can be queried like real people to explore reactions, motivations and preferences.
What makes it particularly powerful is its flexibility. Clients can plug in their own first-party data, making Digital Twins data agnostic and adaptable.
The result is faster learning, greater agility and more informed decision-making.
A new way to make better decisions
Speed or depth? Scale or quality? Innovation or privacy?
Synthetic data helps overcome those trade-offs.
But its value depends on one thing: the quality of the data behind it. That is why high-quality, people-based data remains critical.
The strongest synthetic data starts with the strongest real-world data. At Fifty5Blue, our synthetic models are built on decades of people-based audience data across multiple markets, combining trusted panels with AI and data science to generate outputs that reflect how audiences behave.
Because in the end, synthetic data is about helping us understand people better – giving organisations the confidence to make smarter decisions, faster.