Shopper data is crucial for CPG businesses to understand buying behavior, brand perception, and refine retail strategies. Traditional methods like surveys and field studies struggle with high demand, emerging trends, data gaps, and regulations. They often miss hard-to-reach demographics, require significant investment, and yield limited insights.
Synthetic data—artificially generated to mimic real-world data—offers a scalable, privacy-compliant solution. It simulates scenarios, providing insights for product bundling, placement, and e-commerce strategies. By leveraging synthetic data, enterprises can streamline research, cut costs, and gain actionable insights without the limitations of traditional methods.
Problem statement
Our client, a CPG giant with multiple brands operating in 200+ markets, had limited survey capabilities owing to cost-intensive traditional research methods and challenges in accessing zones that lack direct shopper insights. As a result, they could capture brand perception insights of only ~30-40% of their entire consumer ecosystem, leaving them blindsided on how the remaining group perceived and reacted to their products. They sought to understand brand perception metrics like brand awareness in non-surveyed zones to uncover more meaningful shopper trends. They hoped to leverage developments in GenAI to extrapolate historic data to achieve this.
The client required a solution that would unlock insights from non-surveyed or under-surveyed zones to make more informed decisions in prioritizing brand investments, shaping market growth strategies and more.
Impact
- $4.1M saved by reducing dependencies on traditional surveys and other expensive 3P data providers
- 70% accelerated time to insights through faster access to shopper perception data without waiting for periodic survey cycles
- Gained insights into previously underexplored zones within emerging markets
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