Why traditional brand trackers are failing – and how AI can save them

Brand tracking, as it stands, fails to deliver. It’s expensive, reactive, and largely disconnected from the nuanced reality of consumer sentiment. But AI is poised to transform these archaic tools into dynamic systems capable of delivering immediate, precise, and predictive insights. By integrating AI into brand tracking tools, Chief Marketing Officers (CMOs) can finally turn brand tracking into a powerful engine of growth. Below, we outline three stages that show how AI can enhance, complement, and revolutionize brand intelligence: crawl, walk, and run.

Crawl: AI-optimized surveys

Traditional brand tracking systems often rely heavily on static surveys that, while foundational, don’t capture the full spectrum of consumer sentiment and brand perception. They’re slow to adapt and tend to focus on generic metrics that fail to reveal the deeper motivations and behaviors of consumers. This is where the entry level ‘crawl’ stage comes in – using generative AI to enhance the design of surveys, ensuring it’s not just data-driven but dynamic and adaptive, incorporating pattern recognition AI to capture nuanced responses.

AI’s ability to process large volumes of historical data, customer interactions, and market signals means it can pinpoint the attributes that genuinely matter to your audience. Instead of guessing which features or sentiments drive loyalty, AI highlights the hidden motivators often missed in traditional surveys, using tone-of-voice analysis to deepen understanding of consumer attitudes. AI refines surveys so that every question counts, turning them into efficient and surgically precise tools that lead to clearer, more actionable insights. Moreover, AI algorithms continuously update these surveys in response to emerging patterns, making them future-proof and agile.

Walk: real-time sentiment analysis and trend detection

The next level – ‘walk’ – uses AI to go beyond static data collection, enhancing brand tracking by incorporating real-time sentiment analysis, pattern recognition, and tone-of-voice detection. This stage enhances traditional surveys with dynamic tools like natural language processing (NLP) and contextual analysis that provide instant feedback, amplifying brands’ sensing capabilities, and offering continuous insight into the underlying drivers of brand performance.

Traditional trackers rely on batch data collection, which can’t keep up with the rapid evolution of consumer sentiment. AI, on the other hand, continuously scans online conversations, reviews, and social media to capture an evolving picture of what customers feel and why they feel that way. This shift isn’t just about speed; it’s about depth. By leveraging tools like cultural and economic trend analyzers alongside competitive analysis algorithms, AI not only identifies patterns in consumer feedback but contextualizes them, showing which cultural, economic, or competitive factors are influencing behavior.

This approach empowers CMOs to respond with agility. Instead of waiting for reports weeks after an event or campaign, they gain immediate access to actionable insights. It’s no longer about understanding yesterday’s consumer sentiment; it’s about anticipating tomorrow’s shifts and acting on them before the competition does.

Run: building an AI-driven data lab

For those ready to take full advantage of AI’s potential, the ‘run’ stage offers the opportunity to build an AI-driven data lab – an ecosystem where data isn’t just collected but continuously analyzed, contextualized, and optimized in real-time.

This lab ingests diverse data sources, looking beyond surveys to wider social sentiment as well as macroeconomic indicators and sales performance. The result is a centralized system that provides a comprehensive view of brand health, competitor positioning, and emerging market trends – all in one live interface. With predictive analytics models and anomaly detection algorithms, CMOs gain predictive power, anticipating consumer behavior shifts and adjusting strategies proactively.

An AI-driven data lab doesn’t just track a brand’s past or present; it prepares CMOs for the future, turning static reports into strategic insights that offer a real-time pulse of the market. For example, in our work with Unilever, we developed an AI-powered engine – Delphi AI – that fast-tracks product innovation and keeps the company at the forefront of consumer goods. A scalable and sustainable Microsoft Azure-hosted capability supports analytics across various data sources, generating rich product insights and recommendations.

The AI imperative

For CMOs today, traditional brand tracking systems are no longer enough. AI offers a radical alternative – one that transforms brand tracking from an expensive, reactive process into a proactive, real-time intelligence system that drives growth. The journey from crawl to run provides a clear pathway: optimize your current tools, enhance them with real-time capabilities, and finally, build a comprehensive AI ecosystem that delivers deep, predictive insights.

The question is no longer whether AI will transform brand intelligence; it’s whether CMOs will seize the opportunity to future-proof their strategies. For those ready to evolve, AI isn’t just a tool – it’s a strategic necessity.