The fusion of GenAI, proprietary data, and industry expertise is reworking how brands uncover actionable insights, fuel innovation, and carve out decisive competitive advantages.
According to our recent Global Trends Report, 57% of marketing professionals now consider AI-driven insights essential for understanding consumer behavior, with 62% expecting to increase their investment in artificial intelligence technologies to stay competitive (Kantar, 2024).
Today, LLMs are widely accessible. The true competitive advantage lies in leveraging them with proprietary data and industry knowledge including behavioral analytics, attitudinal surveys, and advertising metrics for enhanced insights.
Yet, generic GenAI applications aren’t equipped to tackle industry-specific challenges. For example, they may have difficulty interpreting the complex relationships between brand equity metrics and financial performance or identifying subtle patterns in consumer behaviours driving brand growth. Those familiar with brand and sales data understand that there is often a lag. While basic solutions may miss strong connections, a trained machine would consider the category's purchase cycles and link the analysis to reveal the true relationship.
By bridging Open AI capabilities with domain-specific knowledge, brands have the access to solutions fluent in data analysis and industry expertise. Subject matter experts lead in refining GenAI by curating datasets, defining use cases, prompt engineering, validating outputs, and refining feedback. Prompt engineering, in particular, is emerging as a key area of expertise that can significantly enhance the performance and relevance of AI outputs.
1. Innovate with greater confidence and scale: AI-powered tools enable rapid evaluation of product concepts, reducing risk and accelerating time-to-market for new offerings.
2. Evaluate and optimize creative impact in real-time: Advanced AI solutions enable quick and accurate assessment of creative effectiveness across channels to optimise campaign impact.
3. Optimise marketing investments with predictive power: AI-driven analysis of the complex relationships between media investments and brand performance paves the way for smarter media spend, outcome simulations, and ROI predictions.
4. Deliver hyper-personalised customer experiences: By combining AI capabilities with deep customer insights, brands can meet individual preferences and needs to create exceptionally rewarding experiences that foster brand loyalty.
5. Create unique competitive advantages: By combining widely available AI applications with proprietary data and expertise, brands can develop insights and solutions that competitors can’t easily replicate.
These benefits translate into tangible competitive advantages across various sectors. A prime example is how leading consumer goods companies are leveraging AI to optimise their brand portfolios. For instance, a global beverage brand leveraged Kantar’s LINK AI database of 250,000 ads and 35 million interactions to optimise its ad campaign during a major sporting event. By rapidly assessing creative effectiveness across various channels in real-time, the brand fine-tuned its messaging to enhance engagement and brand recall. These AI-driven insights enabled the brand to outperform competitors and maximise its marketing ROI during a key sales period.
Consider the following steps to begin harnessing GenAI enhanced insights for your brand:
1. Infrastructure Assessment: Evaluate and prepare your technology infrastructure to support AI integration, ensuring it can handle data processing and application deployment.
2. Data Preparation: Curate diverse, clean datasets representing various industry scenarios that align with your objectives.
3. AI Application Selection: Choose a GenAI application based on your use case, dataset size, training data, and performance metrics.
4. Infusing Domain Knowledge: Enhance the GenAI application with proprietary data and expert insights via prompt engineering and retrieval-augmented generation. This step is where your organisation's value proposition comes into play and differentiates your AI-driven insights from generic AI ones.
5. Iterative Testing: Continuously evaluate against industry benchmarks and refine based on feedback. 6. Ethical Safeguards: Ensure adherence to industry standards and regulations, maintaining data privacy and fairness in AI-driven decision-making.
According to our recent Global Trends Report, 57% of marketing professionals now consider AI-driven insights essential for understanding consumer behavior, with 62% expecting to increase their investment in artificial intelligence technologies to stay competitive (Kantar, 2024).
Today, LLMs are widely accessible. The true competitive advantage lies in leveraging them with proprietary data and industry knowledge including behavioral analytics, attitudinal surveys, and advertising metrics for enhanced insights.
How industry-specific knowledge delivers differentiation
Generative AI (GenAI) utilises large language models (LLMs) to analyse vast datasets and generate human-like text, insights, and solutions that support data-driven decision-making.Yet, generic GenAI applications aren’t equipped to tackle industry-specific challenges. For example, they may have difficulty interpreting the complex relationships between brand equity metrics and financial performance or identifying subtle patterns in consumer behaviours driving brand growth. Those familiar with brand and sales data understand that there is often a lag. While basic solutions may miss strong connections, a trained machine would consider the category's purchase cycles and link the analysis to reveal the true relationship.
By bridging Open AI capabilities with domain-specific knowledge, brands have the access to solutions fluent in data analysis and industry expertise. Subject matter experts lead in refining GenAI by curating datasets, defining use cases, prompt engineering, validating outputs, and refining feedback. Prompt engineering, in particular, is emerging as a key area of expertise that can significantly enhance the performance and relevance of AI outputs.
The competitive advantage of GenAI enhanced insights
The integration of GenAI with domain-specific knowledge and data yields several benefits that directly impact brand growth and consumer engagement:1. Innovate with greater confidence and scale: AI-powered tools enable rapid evaluation of product concepts, reducing risk and accelerating time-to-market for new offerings.
2. Evaluate and optimize creative impact in real-time: Advanced AI solutions enable quick and accurate assessment of creative effectiveness across channels to optimise campaign impact.
3. Optimise marketing investments with predictive power: AI-driven analysis of the complex relationships between media investments and brand performance paves the way for smarter media spend, outcome simulations, and ROI predictions.
4. Deliver hyper-personalised customer experiences: By combining AI capabilities with deep customer insights, brands can meet individual preferences and needs to create exceptionally rewarding experiences that foster brand loyalty.
5. Create unique competitive advantages: By combining widely available AI applications with proprietary data and expertise, brands can develop insights and solutions that competitors can’t easily replicate.
These benefits translate into tangible competitive advantages across various sectors. A prime example is how leading consumer goods companies are leveraging AI to optimise their brand portfolios. For instance, a global beverage brand leveraged Kantar’s LINK AI database of 250,000 ads and 35 million interactions to optimise its ad campaign during a major sporting event. By rapidly assessing creative effectiveness across various channels in real-time, the brand fine-tuned its messaging to enhance engagement and brand recall. These AI-driven insights enabled the brand to outperform competitors and maximise its marketing ROI during a key sales period.
A strategic approach for combining GenAI and proprietary data
Organisations need to combine GenAI with proprietary data strategically. Balancing automation with human expertise is key and subject experts are central to interpreting and verifying complex GenAI outputs. This collaborative human and GenAI approach ensures insights are data-driven, contextually relevant, and actionable.Consider the following steps to begin harnessing GenAI enhanced insights for your brand:
1. Infrastructure Assessment: Evaluate and prepare your technology infrastructure to support AI integration, ensuring it can handle data processing and application deployment.
2. Data Preparation: Curate diverse, clean datasets representing various industry scenarios that align with your objectives.
3. AI Application Selection: Choose a GenAI application based on your use case, dataset size, training data, and performance metrics.
4. Infusing Domain Knowledge: Enhance the GenAI application with proprietary data and expert insights via prompt engineering and retrieval-augmented generation. This step is where your organisation's value proposition comes into play and differentiates your AI-driven insights from generic AI ones.
5. Iterative Testing: Continuously evaluate against industry benchmarks and refine based on feedback. 6. Ethical Safeguards: Ensure adherence to industry standards and regulations, maintaining data privacy and fairness in AI-driven decision-making.
Unparalleled competitive edge and brand growth
It’s now commonplace to leverage GenAI for insights. But it's the strategic integration of proprietary data and industry expertise that’s delivering the market-leading insights fuelling innovation and unparalleled competitive edge. This is the deciding factor in who leads and who follows in the AI-driven future of market research and brand growth.
Learn more about how Kantar’s AI-powered solutions can support your brand growth ambitions here.
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