Synthetic Data for Market Research: Is It Really All It’s Made Up to Be?

Synthetic data may lead to confident decisions based on artificial truths.

To answer this question, I approached the use of synthetic data through first principles.

First principles thinking improves problem solving and decision-making by stripping away assumptions and focusing on fundamental truths.

Instead of following tradition or jumping on trends, first principles thinking asks: What do we know is absolutely true? From that foundation, we rebuild our understanding from the ground up to make better, more informed decisions.

In market research, applying first principles to synthetic data means asking:

• What is synthetic data, where does it come from, and what value does it provide?

• Can artificial data replace the real voice of the customer in informing business decisions?

• What problem is synthetic data actually designed to solve?

Thinking this way helps clarify the key questions and highlights important considerations surrounding the use of synthetic data.

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Synthetic data for market research pros and cons

The promise of synthetic data is speed, cost efficiency, and easy access. It helps market researchers reduce the time and cost of traditional surveys by quickly generating modeled responses.

But are the trade-offs worth it, gaining speed at the expense of trust?

To answer these questions from a first principles perspective, we have to ask: Where did synthetic data come from, and how did it get here?

The use of synthetic data originated in computer vision, where AI systems were taught how to "see," and in machine learning, where they were developed to predict and replicate behavior based on patterns in large datasets. These early applications focused on tasks like object recognition, navigation, or diagnostics in domains with defined outcomes.

In such applications, synthetic data is commonly generated to augment, train, or fine-tune models in situations where real-world data is limited in availability, constrained by privacy concerns, or prohibitively expensive to obtain.

Ground truths

These are domains with clear right answers. Outputs can be objectively measured and compared, making it possible to establish ground truths using generally accepted validation methods. For example, data in fields like computer vision and image recognition are more stable and objective than data related to fluctuating consumer preferences, such as taste in music, which can be influenced by varying factors like mood or occasion.

The risks to consumer and survey research

Artificially generated (synthetic) data often fails to capture the nuance, irrationality, and contradiction of real human behavior. While it can mimic statistical patterns, they are based on models, not lived experience, emotion, or context.

Synthetic data tends to miss cultural and emotional subtleties, struggle with unpredictable or contradictory behavior, and oversimplify responses by smoothing out the natural variability found in real people. As a result, it may omit edge cases, outliers, or unexpected attitudes that are crucial in market research, behavioral prediction, and innovation discovery.

Bringing it back to first principles, what foundational truths do we know about consumer insights and survey research?

Ultimately, it comes down to this: Can you trust the data in light of these core truths?

1. Consumer behavior is fluid, not fixed. Preferences and perceptions shift constantly, making static, point-in-time trained synthetic models quickly outdated.

2. Emotion and irrationality often drive real-world decisions. People don’t always act logically, and models trained on statistical norms can’t simulate irrational or emotionally driven behavior.

3. Context shapes response. Answers vary by mood, timing, and social environment, factors that synthetic systems struggle to replicate authentically.

4. Survey research is a tool for discovery. It reveals what we didn’t know to look for. Synthetic data can only reflect patterns from the training set; it cannot surface the unexpected.

5. There’s often too little high-quality training data to begin with. Market research studies vary widely in topic, question format, sample size, and often can’t include proprietary client data, making it difficult to generate the consistency needed to reliably train synthetic models.

6. Survey fraud and bad data are real, and synthetic systems can amplify them. When flawed responses are used for training, synthetic data outputs risk reinforcing the very noise researchers aim to avoid.

   
   

Conclusion

When viewed through the lens of first principles, the promise of synthetic data in market research begins to unravel. While it may offer speed and cost advantages, these benefits come at the potential cost of accuracy, authenticity, and trust. Clients often invest millions in developing products, launching services, and executing in-market strategies based on research findings. Can they afford to base those decisions on data that may be simulated, smoothed, or out of sync with real consumer sentiment?

The foundational truths of consumer feedback, such as fluid behavior, emotional complexity, contextual nuance, and the value of real human responses, cannot be easily replicated by probabilistic models trained on limited, often imperfect data.

Synthetic data may have a role to play in structured, technical domains with clear right answers. But market research insights thrive on nuance, contradiction, discovery, and human unpredictability. If we are in the business of understanding people, not just simulating them, we cannot afford to trade real insight for artificial shortcuts.

Let's talk. Book a complimentary strategy session here or contact me at rsilvestre@strategence-us.com.