In the ongoing search to increase efficiencies, improve outcomes, and lower costs using AI, the quality and uniqueness of a brand's data have become the key determiner of competitive advantage. According to our latest Most Valuable Global Brands 2024 report, brands perceived as meaningful, different, and innovative by consumers double their chances of growth compared to brands lacking these qualities (BrandZ, 2024).
Indeed, one of the most powerful ways to achieve Meaningful Difference is by utilizing proprietary datasets to power unique AI capabilities for accurate and rapid product research, testing, and trialing at a highly competitive cost point. Today, we are beginning to see products reach markets where the research, testing, and trialing are conducted exclusively using generative AI fueled by expansive, refined proprietary datasets.
The foundation: quality data at scale powers AI insights
Accurate and reliable data forms the foundation for training AI models to deliver dependable insights. These datasets include:
- Behavioral data capturing real-world customer interactions
- Attitudinal insights from surveys and customer feedback
- Cross-sectional data combining multiple data streams
- Historical trend data spanning years and decades
- Proprietary advertising performance metrics
Generic AI models offer basic capabilities, but they simply cannot rival systems trained on decades of refined, industry-specific data from market research, consumer interactions, and other key data points.
Using unique datasets to build inimitable AI insights
When multiple proprietary data sources combine, they create inimitable AI-powered insights. These AI solutions and others go far beyond generic applications by drawing on unique combinations of behavioral patterns, consumer attitudes, and performance metrics.
The richness of the proprietary data allows AI models to spot intricate patterns and produce detailed insights tailored to industries, products, and customer segments.
Our AI solutions showcase how distinctive datasets drive superior performance. ConceptEvaluate AI, for example, predicts market success through our extensive innovation database of approximately 39,000 concepts tested globally, incorporating nearly 6 million consumer evaluations. By analyzing this wealth of historical concept testing data, brands can rapidly assess new products and innovations at scale and identify winning concepts earlier in the development cycle.
For advertising effectiveness, LINK AI processes 35 million human responses from 250,000 ad tests accumulated over three decades. This vast database of real consumer reactions enables brands to evaluate creative content with unprecedented speed and precision. Google, for instance, tested 11,000 ads in less than a month at a scale and speed that traditional methods cannot compete with.
Proprietary data and AI-powered insights in practice
The synthesis of extensive proprietary datasets with advanced AI models is opening up new opportunities for product development and innovation. The case studies below highlight how brands achieve faster time-to-market and greater success by applying AI insights backed by decades of consumer data.
Accelerating product innovation through AI-powered testing
When Iceland Foods aimed to pioneer AI-assisted ready meals, they needed swift, reliable concept testing for their new wellness range. Using ConceptEvaluate AI, they quickly identified the most promising products and energy-focused benefits. The AI-powered evaluation helped Iceland pinpoint which concepts would resonate strongest with consumers and maintain brand differentiation. The insights guided Iceland's product development strategy and accelerated product innovation while reducing market risk, leading to the planned launch of their first AI-assisted ready meals in early 2025.
Scaling creative effectiveness across global markets
Genomma Lab Internacional needed to evaluate vast quantities of advertising content across multiple markets while maintaining brand consistency and commercial effectiveness. LINK AI provided a centralized testing solution across the Latin American market. The model's predictions aligned with real-world sales improvements and market share, giving Genomma Lab the confidence to fully adopt AI-powered testing. This allowed them to evaluate more creative assets at lower costs while uncovering valuable patterns for developing targeted communications across different brands and audiences.
These use cases illustrate how AI models built on rich proprietary datasets offer more than just efficiency gains: they provide deeper insights that improve decision-making and strengthen brand performance.
Winning the AI advantage with proprietary data
Brands possess extensive proprietary data across creative assets, product concepts, consumer interactions, and market performance metrics. Combined with AI models and backed by robust data governance, these unique datasets can drive fast and precise decision making as highlighted above. Indeed, the competitive edge gained through AI solutions trained on proprietary data creates lasting advantages too. Looking ahead, organizations that treat their proprietary data as a core asset will lead in AI-powered innovation.