Manasa Rajan, co-founder & Ceo, Jupiter Meta, on the changing phase of consumer intelligence for brands

There is a quiet crisis in how brands understand their consumers.

Every year, Indian enterprises spend hundreds of crores on market research, surveys, focus groups, tracking studies, in pursuit of a single, elusive thing: the truth about what consumers want. And yet, the insights that come back are often months old by the time they reach a decision-maker. They are shaped by respondents who game the system, skewed by sampling that mistakes familiarity for representation, and diluted by the sheer noise of a data ecosystem overrun by bots and incentivised responses.

The problem is not a lack of data. It is a lack of trustworthy data - gathered fast enough to matter.

The Broken Promise of Traditional Market Research

India’s market research and insights industry reached $3.2 billion in FY2024, growing at 12.6% year-on-year, according to the Market Research Society of India, and is projected to touch $3.8 billion in FY2026. India is now the world’s third-largest market research economy. The money is real. The ambition is real. The problem is that the underlying quality of the data that this industry runs on is increasingly not.

For decades, market research operated on a simple premise: ask enough people, aggregate their answers, and call it consumer behaviour. The methodology was imperfect but serviceable in a slower economy. Today, it is neither.

Consider the timelines. A typical brand health study takes six to ten weeks from briefing to final report. By the time the findings land in a strategy deck, the market has moved. A competitor has launched. A cultural moment has passed. The consumer who was surveyed three months ago is no longer the same consumer.

Then there is the reliability question. The rise of panel fatigue, where the same hyper-responsive respondents fill survey after survey, has made much of the industry’s data structurally biased. Add to this the infiltration of AI-generated responses and bot traffic into online panels, and the integrity of the underlying data becomes genuinely alarming.

Research published in peer-reviewed journals found that usable responses from online surveys have declined from 75% to just 10% in recent years due to survey fraud. A separate industry analysis by Research Defender found that 31% of raw survey responses contain some form of fraud. Brands are making multi-crore media and product decisions on a foundation that is shakier than most would like to admit.

Fragmentation compounds the problem further. Consumer insights sit in silos - CRM data here, social listening there, a dipstick survey somewhere else - with no coherent layer to synthesise them into a single view of consumer behaviour. Market analysis, as it is currently practiced, is largely a rearview mirror exercise dressed up as foresight.

A New Expectation Is Taking Shape

The brands winning today are not waiting for quarterly reports. They are operating with continuous feedback loops, real-time market intelligence that informs not just strategy, but execution. The shift is structural: from periodic research to always-on consumer intelligence.

This is not simply a technology upgrade. It represents a fundamentally different relationship between brands and insight. Customer feedback is no longer an input to annual planning; it is an operational signal. Consumer behaviour is no longer described retrospectively; it is tracked, modelled, and increasingly - anticipated.

The infrastructure to support this kind of intelligence has, until recently, been out of reach for most enterprises. Building proprietary panels takes years. Cleaning and verifying data is expensive. And the analytical capability required to translate raw consumer signals into predictive insight is rare.

What Verified Human Data Changes

At the centre of this shift is a problem that rarely gets named directly: you cannot build reliable consumer intelligence on unverified data.

This is where the conversation about market research needs to evolve. The industry’s response to data quality problems has largely been procedural, more screening questions, more attention checks, more manual auditing. These are patches, not solutions.

A more durable answer lies in identity verification at the point of data collection. When you know, with cryptographic certainty, that the person responding to a survey is a real, unique human being - not a bot, not a duplicate, not a professional survey-taker - the entire downstream chain of analysis becomes more trustworthy. Market analysis built on verified human data is not just more accurate; it is a different category of intelligence.

Zero-knowledge KYC (ZK-KYC) technology makes this possible without sacrificing consumer privacy. Using cryptographic proofs, a respondent’s identity can be verified without exposing their underlying personal data, the system confirms “this is a real, unique

individual” without ever revealing who they are. This is a meaningful distinction in an era where data privacy regulation is tightening globally and consumer trust is a competitive asset.

Jupiter Meta and Hercules: Building the Next Layer of Consumer Intelligence

Jupiter Meta was built on exactly this premise. Over five years, the company has constructed SuperJ - a verified consumer network of over 20 million users, onboarded through ZK-KYC - as the data foundation for a new kind of consumer intelligence platform.

Hercules, Jupiter Meta’s autonomous consumer intelligence platform, sits on top of this infrastructure. It is designed to do what traditional market research cannot: deliver real-time consumer insights, synthesise customer feedback across touchpoints, and surface predictive signals, all from a base of verified, privacy-compliant human data.

For enterprise brands, this translates into something concrete. Instead of waiting months for a brand health report, a team can track shifts in brand perception week over week. Instead of running a one-time market analysis before a product launch, they can run continuous pulse checks against verified consumer cohorts. Instead of relying on historical data to model consumer behaviour, they can work with live signals.

The clients Jupiter Meta works with - HUL, ICICI, Asian Paints, Amul, Greenply, Air India, and Kantar, across FMCG, banking, retail, and media - use Hercules not as a replacement for research, but as an upgrade to the intelligence infrastructure that research was always trying to build. These are organisations that run at scale, where the cost of acting on bad data is significant, and the value of acting on good data faster is compounding.

The Competitive Advantage Is Temporal

There is a version of this conversation that focuses on accuracy, better data, better models, better outputs. That conversation is important. But the more immediate competitive advantage that real-time consumer intelligence confers is temporal.

In a category like FMCG, the difference between identifying a consumer behaviour shift in week two versus week twelve is not just analytical, it is commercial. In financial services, the ability to track customer feedback in near-real time after a product change can determine whether an issue becomes a churn event or a recoverable moment.

Speed and trust, together, are what make consumer intelligence actionable. Without speed, even accurate insights arrive too late. Without trust in the data, even fast insights lead to confident mistakes.

What the Industry Needs to Confront

The market research industry is not going away. India’s analytics segment - the fastest-growing part of the industry - grew 17% last year, driven by AI and machine learning adoption across retail, finance, and media. The direction of travel is clear. But the industry is being asked to do something it was not originally designed for: operate at the speed and complexity of modern consumer behaviour, while maintaining the rigour that enterprise decisions require.

The platforms that will define the next decade of consumer intelligence are those that resolve this tension, not by choosing speed over rigour or vice versa, but by building the infrastructure that makes both possible simultaneously. Verified human data, real-time collection, and AI-powered synthesis are not incremental improvements to the existing model. They are the foundation of a different model entirely.

For brands serious about understanding their consumers - not as a periodic exercise, but as a continuous organisational capability - the question is no longer whether to make this shift. It is how quickly they can.

Note to readers: This article is part of Mint’s paid consumer connect Initiative. Mint assumes no editorial involvement or responsibility for errors, omissions, or content accuracy.

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