Putting Synthetic Data to Work: Balancing Effort, Trust and Impact

Data's Role in Market Research: Same As it Ever Was

In market and marketing research, data has always been more than numbers—it is a reflection of context, people, and the real world. Historically, data has been tethered to the ground, derived from the field, focus groups or studies that captured the nuances of human behavior.

From the advent of Nielsen panels tracking household habits to the narratives emerging from qualitative focus groups, the labor of collecting and interpreting data has been deeply rooted in lived experiences. This labor has not only ensured the quality of insights but also reinforced trust in the outcomes –the data came from somewhere.

 

However, as synthetic data enters the scene, this tether is loosening. Synthetic data, created through artificial intelligence to mimic real-world patterns and structures, offers speed, scale and innovation—but it also demands a new level of scrutiny. The foundation of market research has always relied on effort (the work of data collection and interpretation), impact (the decisions that data drives) and confidence (trust in the data’s connection to reality). Synthetic data introduces an element of uncertainty—a lack of clarity about its validity and fidelity to real-world conditions—that must be addressed to avoid false assumptions or shallow insights.

While uncertainty has long been a factor in market research, synthetic data requires us to embrace this uncertainty as a tool for exploration rather than something to eliminate. By doing so, we can expand the horizons of market research while remaining grounded in reality. As we explore synthetic data’s potential, we must weigh these constructs carefully, ensuring it complements rather than replaces real-world insights.

What Is Synthetic Data?

Synthetic data refers to artificially generated information designed to replicate the patterns, structures and properties of real-world data. Unlike data gathered directly from the field, synthetic data is created through algorithms and models. It can fill gaps, simulate scenarios and enable experimentation in ways that real-world data cannot easily achieve. However, synthetic data is only as good as the inputs and assumptions it is built upon, which makes validation and grounding critical.

To understand its role, consider synthetic data as a flight simulator. It mimics the mechanics of flight, providing a controlled environment to test and train. A pilot can practice takeoffs, landings and emergency scenarios without risk—but the simulator cannot fully replicate the unpredictability of real turbulence, random events or human passenger behavior. Similarly, synthetic data offers a controlled space to experiment and predict outcomes but lacks the messy, nuanced realities of real-world interactions and complex human behavior.

As we move deeper into the age of data and artificial intelligence, synthetic data is transforming market research. It is particularly valuable for testing new concepts without historical benchmarks, modeling customer behaviors during early product development or exploring underserved market segments. However, it must remain a tool to augment real-world data, not a shortcut to bypass it. Trust in synthetic data depends on its alignment with reality and its role within the broader goals of effort, impact and confidence.

Potential Pitfalls and Challenges

While synthetic data offers significant benefits, its application is not without risks. Common challenges include:

• Bias Amplification: Synthetic data may inherit or exacerbate biases present in the original training data.
• Overconfidence in Outputs: Without grounding in real-world data, synthetic insights may lack nuance or fail to resonate authentically with target audiences.
• Loss of Real-World Context: Synthetic simulations may overlook the cultural and emotional complexities inherent in human behavior, failing to change as people and culture evolve.
• Ethical Concerns: Misuse of synthetic data without transparency can erode trust and misguide strategic decisions.

A Practical Guide to Synthetic Data

As these potential pitfalls demonstrate, synthetic data is only effective when used thoughtfully and strategically. It requires a balancing of the effort needed to generate and validate the data, the impact of the decisions it informs and the confidence placed in its outputs. Here’s how we at Material approach synthetic data, drawing on these principles and our experience in developing and delivering impactful client solutions.

Use Case	Best Practices	Impact	Blind Spots / Challenges	Effort	Confidence
Filling Data Gaps	Augment datasets to better represent underserved or underrepresented segments.	High	Risk of perpetuating biases from the source data; must validate outputs.	Medium	Medium
Stress-Testing Responses	Simulate responses to product launches or campaign strategies across synthetic customer profiles.	High	May not capture emotional depth or cultural nuance of real-world reactions.	Medium	Medium
Predictive Analytics	Use synthetic data to model customer behaviors and predict outcomes in new demographics or markets.	High	Synthetic predictions may oversimplify complex variables in real-world scenarios.	Medium	Low
Testing New Concepts	Explore untapped markets or early-stage ideas using synthetic personas and scenarios.	Moderate	May miss latent needs or unique behaviors not captured in synthetic data.	Low	Medium
Scaling Research Efforts	Use synthetic data to conduct large-scale A/B testing or exploratory research.	Low	Risk of prioritizing efficiency over depth, leading to shallow insights.	Low	High

Looking Ahead

Synthetic data is a transformative force in market research, offering scalability, efficiency, and opportunities for innovation. To harness its potential while avoiding its thorniest problems, businesses must strike a balance between effort, impact and confidence. The key is recognizing that synthetic data is not a substitute for real-world data, nor an idealized "holy grail." Instead, it should be understood and used as a complement—most effective when its limitations are acknowledged and accounted for.

Avoiding overconfidence requires more than recognizing synthetic data’s limitations; it demands a mindset shift. Instead of viewing its imperfections as flaws, we can embrace them as opportunities for exploration. Synthetic data’s inherent uncertainty makes it a powerful tool for stress-testing, surfacing edge cases and uncovering new possibilities that real-world data alone (that is inherently retrospective) might overlook. This shift—from seeking precision and exactness to valuing uncertainty as a catalyst for discovery—opens new avenues for innovation.

In this era of potentials and possibilities, market research thrives not just on finding answers but on stimulating exploration and expanding understanding. By leveraging synthetic data thoughtfully, businesses can remain agile, forward-thinking and grounded in the essential truths of real-world insights.