Synthetic Data can’t think like real consumers yet…

Synthetic data has gained popularity in market research, promising to create "real-like" consumer behaviour patterns without the need for actual survey responses. However, while synthetic data has its place, over-reliance on it for making critical business decisions can lead to costly mistakes.

What is synthetic data?

Synthetic data is artificially generated information that mimics real-world data using statistical models, AI, or predefined probability distributions. It is often used to test survey platforms, validate analytics tools, and ensure workflows function as intended before collecting real consumer insights.

Synthetic data is a tool – not a truth!

The very name "synthetic" means that it is not natural, which itself indicates its limitations. In the domain of market research, synthetic data is often treated as a reliable substitute for real-world insights, but this can be misleading.

Probable use-cases of synthetic data in market research

Many articles have been written on how it can be applied in the MR domain:

• Concept / Product testing: Gauging the appeal of new product ideas across different customer segments without needing extensive field research.
• Predicting future consumer behaviour: Anticipating how different customer segments might respond to new offerings or changes in the market.
• Boosting survey sample sizes: Generating additional responses to balance demographic representation. However, a more accurate alternative is using projections or weighting techniques on real data, ensuring the insights remain grounded in actual consumer responses.
• Testing survey logic and platform functionality before launching real surveys.
• Creating test data for dashboards, analytics models, and AI training to avoid privacy concerns.
• Simulating various scenarios for academic research or initial hypothesis-building.

?Major limitations of synthetic data

However, AI-driven synthetic data is ultimately just randomization with sophisticated formulas. The generated data is only as good as the assumptions behind it, meaning:

• It does not capture actual consumer sentiment, emotions, or evolving preferences.
• It assumes past trends remain valid, ignoring market shifts.
• It lacks the unpredictability of real consumer behaviour, which is crucial for insights.
• It can never fully capture nuances that exist in real data based on gender, age, region, or other demographic profiles.

Why using synthetic data for decision-making can be a blunder?

1.????? Accuracy: If synthetic data is generated using past data, it inherently assumes that past market conditions still hold true, which is rarely the case in dynamic industries.

2.????? Lack of behavioural complexity: Consumer decisions are often influenced by external factors such as economic conditions, trends, and sentiment-all of which synthetic data struggles to replicate.

3.????? No guarantee of data integrity: Unlike real surveys where logic checks and screening questions filter out "dirty" or “unqualified” respondents, synthetic data can create inconsistent or illogical responses if not carefully modelled.

4.????? Impact on decision-making: Businesses might misinterpret synthetic data as "real" insights and base marketing campaigns, pricing strategies, or product launches on fabricated trends that do not reflect actual consumer demand.

The reality: synthetic data is just a fancy Random Data Generator

Many survey platforms include a Random Data Generator (RDG) feature, which produces test responses for surveys. At its core, this is nothing but synthetic data. However, RDGs and AI-generated synthetic datasets cannot replace actual human responses, making them unsuitable for decision-making that carries financial risks.

Can synthetic data replace real survey participants?

We’re not there yet. While synthetic data is faster to generate than gathering real survey responses, the trade-off is a decrease in the quality and reliability of the data.

To make synthetic data more reliable, it needs to feel more human. Instead of creating average answers, it should reflect real consumer behaviour - including strong opinions, emotional decisions, and unexpected choices. People don’t always pick products logically; they follow trends, trust brands, or make impulse buys. Adding a mix of extreme responses, unexpected patterns can make synthetic data more lifelike and useful for market research.

While synthetic data can assist in hypothesis generation, testing platforms, and filling sample gaps, it should not replace real consumer insights for critical decision-making. The best approach is to use synthetic data where it adds value-such as testing surveys or models-but ensure real-world data remains the foundation of any major marketing or strategic decision.

In market research, synthetic data is a tool-not a truth....