Synthetic data is not new to market research, and the healthcare insights industry is starting to get friendly with the idea of conversational AI and digital twins. At the forefront of this transformation is synthetic data—an innovative tool that is redefining how market research is conducted across industries. But what exactly is all the buzz about?
Synthetic data is like a best friend to traditional market research—a supportive companion that doesn’t overshadow but enhances the insight-gathering process. It’s a game-changer for pharma and healthcare brands seeking new ways to deepen their understanding of patients, HCPs, payors, and market sentiments.
Types of Synthetic Data
The diversity within synthetic data is vast, with four main types emerging as particularly useful in market research:
- Persona-Based: Imagine fictional profiles that mirror real data patterns—great for hypothesis development and internal training.
- Boosted Samples: Synthesize respondents to fill gaps, balancing representation across hard-to-reach groups.
- Pure Silicon Samples: These fully synthetic datasets mirror population characteristics, ideal for internal research where privacy is crucial.
- Digital Twins: Digital twins model individual behaviors over time, assisting in behavioral forecasting and market simulations.
Use Cases for Synthetic Data
Synthetic data can shine in numerous areas, amplifying market research’s effectiveness in ways traditional methods may fall short:
- Hypothesis Generation: Ideal for testing the waters on new drug formulations or devices before diving into costly primary data collection.
- Early-Stage Testing: Streamlining initial concept tests without the baggage of extensive primary data collection.
- Dataset Augmentation: When privacy or ethics limit access to patient data, synthetic data steps in, enhancing epidemiological study contexts.
- Reducing Respondent Burden: Particularly in sensitive areas like mental health, synthetic data lessens the load on actual patient involvement, enabling comprehensive insights with fewer ethical complications.
Navigating the Challenges of Synthetic Data
Like any innovative tool, synthetic data is not without its skeptics and challenges. Synthetic data’s simulations may lack real-world unpredictability. However, integrating clinical, demographic, and lifestyle variables offers a more holistic simulation, capturing the essence of patient behaviors. Historical confirmation bias may be another issue, but periodical updates to models align them with the latest medical advancements, minimizing bias and ensuring relevance. By incorporating real-time data inputs—from electronic health records to wearable devices—synthetic data models can become timely and responsive to emerging health trends.
And while synthetic data may fall short of predicting new disease outbreaks, it simulates various scenarios, allowing the healthcare industry to prepare for the unexpected.
The Final Verdict: Pragmatism Meets Idealism
Synthetic data is no cure-all; rather, it’s a potent augmentation to traditional primary research. When used judiciously, it complements primary data rather than replacing it, filling in the gaps, and speeding up the research process. It invites a pragmatic approach where market researchers can confidently navigate complexities and make informed decisions.
Pragmatism vs. Idealism: Synthetic Data in Market Research
Get your copy of our cheat sheet to learn about common issues, training needs, and specific examples for each type of synthetic data.
As the industry marches toward the future, synthetic data offers a balanced path forward, empowering researchers to leverage the full potential of emerging technologies while maintaining a human touch. The revolution is here—will you join the ride?
Author
Dan Wasserman
Chief Operating Officer and Head of AI Solutions
Dan Wasserman is KJT’s COO, leading the integration of generative AI into client solutions to drive innovation as part of Sparq Intelligence. With expertise in compliance, operations, and analytics, he transforms complex data into actionable insights. Dan values collaboration with KJT’s teams to help clients design better products and services.
