Why static market research models are evolving into decision intelligence systems

By Vishal Ranjan, Founder & CEO of Claight

The market research industry built its business on annual reports and stable forecasts. A series of global shocks has made that model untenable, and the firms that survive the next decade will look nothing like those that dominated the last one.

There is a particular kind of meeting that research professionals have quietly stopped scheduling. The one where a beautifully formatted, 90-page syndicated report is walked through slide by slide, forecast by forecast. Not because the data is wrong. But because by the time it arrives, the decisions it was meant to inform have already been taken.

Market research, for most of its existence, was built on a foundational assumption: that the world would more or less hold still while you studied it. Annual refresh cycles, stable CAGRs, quarterly check-ins that felt adequate because markets moved at a pace that allowed for reflection. It was a sensible model for a sensible era. But that era is gone.

The Year the Models Broke
The pandemic did not merely disrupt market research. It exposed it. Forecasts assembled in January 2020 were functionally useless by March. Consumer goods companies watched planning assumptions collapse in real time.

Research firms found themselves doing something the trade had rarely demanded before: updating outlooks not quarterly, not monthly, but every single day.

What came next ensured there would be no return to the old rhythm. Russia’s invasion of Ukraine in February 2022 drove sunflower oil prices sharply higher. Ukraine supplies close to half the world’s exports, and India is its largest single buyer. Sanctions, a Gulf conflict, volatile container rates, swinging fertiliser prices: each successive shock arrived before the industry had recalibrated from the last. The off-the-shelf report has not merely declined in relevance. It has, for practical purposes, ceased to function.

A Problem of Architecture, Not Just Speed
The issue runs deeper than late delivery. At most organisations, internal data sits in one system while commissioned research sits in another, rarely reconciled.

Companies are simultaneously data-rich and decision-poor.

Compounding this is a discipline-wide bias toward description over prescription. Executives receive exhaustive accounts of what has happened: market sizes, share shifts, sentiment readings. What they receive far less reliably is guidance on what to do about any of it. The Monday-morning question- what now, consistently goes unanswered. The most valuable page of any research deliverable, it turns out, is the one that was almost never written.

The System, Not the Study
The industry’s response has coalesced around a concept practitioners are calling decision intelligence. Beneath the terminology, the idea is direct: data infrastructure that ingests signals continuously; models that convert those signals into scenarios; and frameworks that connect scenarios to actual business choices.

Research, in this model, is not the output. It is one input among several, alongside internal data, operational context, and expert judgment.

The difference is most visible in how companies now handle input cost volatility. Under the old approach, the question generated a commissioned study and findings delivered months after the moment of maximum relevance. Under the new approach, it activates a live model demand signals, commodity prices, competitor moves, and freight rates drawn together continuously. The output is not a verdict but a layered view: scenarios, confidence intervals, trade-offs made explicit. Executives may still reach the wrong conclusion. They are no longer doing so without adequate information.

Dashboards Over Decks
Boards and chief executives have grown visibly impatient with the conventional deliverable. They want dashboards. They want scenarios rather than single-point forecasts. They want a traceable line from data to decision, with honest acknowledgment of what is and is not known. The 200-page methodology appendix, once presented as evidence of rigour, now reads more readily as evidence that a firm could not convert its own findings into anything actionable.

For research companies, this is a fundamental commercial reckoning. The unit of value is no longer the report; it is the system. Clients will invest meaningfully in platforms that fuse internal data with external intelligence, run scenarios on demand, and connect directly to planning cycles. One-off studies that arrive late and are filed away unread will find fewer buyers. Meeting that demand requires a different kind of organisation: analysts working alongside data engineers, methods built for continuous inputs, and pricing that reflects ongoing relationships rather than discrete project deliveries.

Rigorous qualitative research, disciplined quantitative methodology, and deep category expertise remain essential, but their role has changed. They are the raw material from which something more useful is now built. The static report had a long run.

What follows will be harder to produce and harder to package, but far more closely tied to the moment that has always mattered most: the one when someone decides what to do next.