Curious how synthetic data is changing the way on how to measure brand performance? Have you wondered how robust the synthetic sampling approach is?
Download our definitive guide to synthetic data boosting in brand health tracking.
Grounded in empirical evidence from our R&D journey in the past year, where we have tried and tested with a number of statistical and Gen AI methods. It will give you guidance on key considerations if you are considering applying synthetic data boosting to your brand health trackers, or reach out to one of your Kantar representatives to find out more.
Cynthia Vega, Head of Digital Analytics at Kantar and co-author of the report Synthetic Data: Beyond the Hype, breaks down what synthetic data really is (and isn’t), dispels common myths, and explains why human input is still essential for high-quality outcomes.
A few questions to get you started on the topic of synthetic data:
What is synthetic data?
Synthetic data is artificially generated information that mimics real-world data.
For instance, based on surveying 1,000 real people from a population, algorithms can create additional data that looks and behaves like it came from actual consumers, and even gets to increased statistical precision within sub-groups of the population.
Another form of synthetic data is digital twins, where a model is trained on actual attitudes and behaviours from a real respondent and then used for subsequent predictions and insights – with permissions, privacy and regulatory safeguards built in.
Why is synthetic data relevant in marketing?
The world moves quickly. CMOs and insights teams need to move at lightning speed to innovate and stay competitive. But getting real, high-quality data from enough people can be slow, expensive and sometimes impossible for niche audiences.
With increasing privacy regulations and consumer concerns about data collection, synthetic data offers a way to speed up research and get richer insights without needing to collect more personal information.
AI and machine learning advancements, especially generative AI, have dramatically improved the quality and capabilities of synthetic data generation. Forbes predicts that synthetic data could become a $2.34bn industry by 2030, making it an essential tool for staying competitive in a data-driven landscape.
How does synthetic data work in marketing and insights?
Imagine a brand tracking survey where you have limited responses from a crucial but hard-to-reach demographic – something like Gen Z buyers of luxury cars in Germany, say. Instead of spending time and money trying to recruit more of these specific individuals, we use our high-quality, fraud-proof human panel data to train a synthetic data model.
This model then generates new rows of synthetic respondents that accurately reflect the characteristics and preferences of that under-represented subgroup. This boosts the statistical power for that audience segment, allowing marketers to make more confident decisions about how to target and engage them, without compromising the overall sample quality.