How ITC Uses AI to Transform Consumer Research and Drive Smarter Decisions

How ITC is Fine-Tuning Its Consumer Research Practices Using AI

ITC is transforming its market research approach by integrating Artificial Intelligence (AI) to boost efficiency and speed up data processing. AI tools now analyse large datasets, identify emerging trends, and streamline new product development. This shift enables ITC to make more informed, quicker decisions and reshapes how it examines data and evaluates marketing strategies.

Highlights

• AI streamlines analysis of consumer behavior, strategy assessment, and performance measurement, overcoming the limits of traditional methods.
• ITC has built a secure internal platform to apply AI technologies like Natural Language Processing (NLP) and Machine Learning (ML), allowing researchers to build impactful client products while controlling experimentation.
• Key uses include category exploration with public data, sentiment analysis of customer care interactions, and trend tracking via social media, all enhancing decision-making quality.

The Evolution of AI in Market Research

ITC’s market research traditionally followed three phases:

• Exploration: Investigating issues such as sales drops or brand challenges in specific regions, understanding target groups, and spotting trends.
• Evaluation: Testing new concepts, products, packaging, and marketing strategies to assess consumer reactions and optimize offerings.
• Performance Measurement: Monitoring brand health, marketing spends, and retail data to track market success.

Each phase involved defining objectives, designing research tools, collecting and analysing data, and preparing reports—a process that was time-consuming and resource-intensive.

AI as a Game-Changer

During the webinar "The Future is AI", Vara Prasad, Vice President of Consumer Insights and Analytics at ITC, highlighted key benefits of AI:

• Improved Efficiency: AI simultaneously processes qualitative transcripts and quantitative data, enabling smoother, more conversational data collection and less manual effort.
• Faster Data Processing: AI and ML handle large volumes of data rapidly, making previously time-heavy analysis more manageable.

Tracing AI’s Development

• 1950s: The foundation with the Turing Test.
• 1980s: Models began using large datasets to identify patterns and predict outcomes.
• 2010s: Machine learning evolved to mimic brain functions, including unsupervised learning techniques.
• 2020s: Emergence of generative AI capable of creating original content based on historical data and domain knowledge.

This progress is driven by massive increases in computing power, which has doubled approximately every six months since 2010.

Current and Upcoming AI Capabilities

AI is advancing from narrow applications like customer service bots toward more sophisticated forms capable of complex reasoning and personalized interactions. Within five years, ITC expects to enter the super-intelligence phase, where AI significantly drives innovation and hyper-personalization.

Current AI tools relevant for market research include:

• Facial recognition
• Speech and text analytics
• Natural Language Processing (NLP)
• Image and video analysis
• Deep learning
• Conversational AI solutions

These tools help researchers analyze varied data formats and solve specific business challenges.

ITC’s AI Strategy in Market Research

To avoid overwhelming researchers with too many AI options, ITC developed a secure internal platform. This setup allows product developers and solution teams to build client-facing tools within a controlled experimentation environment.

ITC’s AI approach includes:

• Computational AI: Using NLP and ML for agile, predictive solutions in routine market research tasks.
• Generative AI: Customizing general generative AI models with fresh consumer data and ITC’s domain knowledge, similar to training a new expert team member.
• Synthetic Data: Employing ML techniques like Generative Adversarial Networks (GANs) to create synthetic datasets that mirror real data, enhancing insights when real data is incomplete or sensitive.

AI Use Cases at ITC

• Category Exploration: Instead of traditional immersions, ITC analyses public data sources such as Google searches, social media posts, videos, and images. This helps identify what consumers buy, when, why, and who they are, providing a precise research starting point.
• Customer Care Data: AI converts thousands of daily customer call audio files into text for sentiment analysis, uncovering feedback, brand mentions, pain points, and product insights that were previously inaccessible.
• Trend Tracking: Public social conversations around topics like protein or gut health are continuously monitored. Trends are classified as emerging, mainstream, or declining, guiding strategic decisions.
• New Product Development (NPD): AI analyzes trends and market data to generate product concepts and packaging designs. This supports deciding whether a trend is actionable and integrates AI into regular NPD planning.
• Sales Performance and Anomaly Detection: AI reviews large-scale sales data across platforms, spots anomalies (e.g., sudden sales drops), investigates causes, and predicts future trends. This shifts reporting from static dashboards to predictive and prescriptive insights.

The AI-Powered Future of Market Research

ITC’s experience shows AI is more than just a tool—it reshapes how data is analysed, marketing challenges are assessed, and performance is tracked. This results in faster, sharper, and more dynamic decision-making.

For professionals interested in expanding their AI knowledge in marketing and product development, exploring specialized AI courses can be valuable. You can find relevant AI courses for marketing specialists and product developers here.