Synthetic data represents one of the most important innovations for the field of marketing research, offering a number of significant advantages.
First, they provide invaluable privacy protection by eliminating the need to use consumers' personally identifiable information, making them an ideal choice for studies that address intimate or taboo questions.
Furthermore, synthetic data is highly cost-effective compared to collecting real data, making it accessible even to researchers with limited budgets. Its ability to simulate various scenarios and data sets adds a new dimension to analysis, enriching the understanding of consumer behavior patterns.
However, it is important to keep in mind that synthetic data also has limitations that at this point should be approached with caution. Synthetic data is created using algorithms and statistical models that generate artificial information that mimics the characteristics of real data, but sometimes cannot fully capture the complexity of real data. Especially in highly specialized environments or niche markets with unique characteristics. Additionally, the algorithms used to generate synthetic data can introduce bias if not handled correctly, which can distort analysis results.
Future perspectives of synthetic data in market research.
However, these advances are expected to improve the accuracy and realism of synthetic data, opening new opportunities for its application in a wide range of marketing research contexts. In addition, a greater focus on ethical considerations is expected, guaranteeing transparency and responsibility in its use, as well as the development of specific regulatory frameworks to guarantee ethical and responsible practices.
To a large extent, if properly generated and validated, synthetic data has the potential to be a reliable and valuable tool for market research and analysis, serving as a perfect additional layer to deepen and enhance analysis.
Common use of synthetic data is simply a matter of time and experience. Continuous improvement thanks to large-scale tests, which are currently underway, makes them an increasingly attractive option for researchers and companies.
February, 2024
