The changing face of modern market research
The market research industry is in the midst of a profound transformation, driven by the rapid introduction of machine learning and artificial intelligence (AI) into workflows, and in general becoming increasingly present, powerful and more sophisticated.
At the forefront of this transformation is the use of synthetic data as a research tool.
What is synthetic data?
In relatively simple terms, when we refer to synthetic data, we’re talking about computer-generated information designed to replicate the values, patterns, relationships, and other statistical properties of real data. For market research purposes, when used well, it can serve as a substitute or augmentation for actual collected data.
Synthetic data is data which has been created using algorithms, simulations, and other generative AI techniques in order to replicate the characteristics, patterns, and relationships found in original and authentic datasets collated from human responses.
Seed data is one of the foundational elements of synthetic data. Seed data refers to the capturing of high-quality, real-world responses that act as the blueprint for AI models to learn from, and it assists with providing synthetic outputs that are anchored in authentic human behavior.
How is synthetic data used in market research?
There are currently two predominant ways that synthetic, or augmented, data is used in market research:
Synthetic boosting
Synthetic boosting refers to the ability to increase niche audience quotas by topping up a target group with synthetic responses. Augmenting niche or underpowered groups with synthetic respondents means that small segments are more likely to be stable enough for robust analysis. Do be aware, though, that boosting is not a substitute for missing segments, as it requires seed data for learnable patterns.
Synthetic enrichment
This refers to the ability to enrich existing research with additional synthetic responses to new questions. It combines actual consumer responses with modeled data, creating a more complete, actionable picture of your target audience without additional surveys.
What are the benefits of using synthetic data?
As Cint’s Phil Ahad has stated, synthetic data is meant to augment—not replace—high quality, real data and is most powerful when applied with careful oversight and clear reporting.
Synthetic data is meant to augment—not replace—high quality, real data and is most powerful when applied with careful oversight and clear reporting.
Researchers find themselves under pressure to move faster, reach hard-to-find audiences, and extract additional value from every commissioned study. Synthetic augmentation allows for that without turning every single need into yet another expensive and potentially lengthy cycle of fieldwork. Any augmentation of this kind is based on real data provided by real people.
When the processes behind creating and deploying synthetic data have been validated and proven reliable, it has the capacity to enable fast, cost-effective, and privacy-safe augmentation to traditional survey and behavioral datasets.
It is crucial to remember that real people must remain the anchor of research; synthetic methods are on hand to extend their footprint in smart, validated ways, and should not be seen as a straight up replacement for real respondent groups.
Read Cint’s report on synthetic data
Almost 200 market researchers and insights professionals from Cint’s customer base were invited to participate in a research project. They were asked to respond to a survey regarding their usage of AI and synthetic data, and their outlook on those shifts in the Restech landscape.
The resulting report explored sentiments around the use of synthetic data in modern market research.
Head here to download our report today.
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