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Consumer intelligence models face reset as behaviour outpaces traditional research: Report
Static personas and post-campaign analysis are increasingly misaligned with how consumers make decisions today, as AI-driven discovery and invisible influencers reshape purchase journeys, according to a new report.
Consumer intelligence frameworks are undergoing structural change as traditional research models struggle to reflect how purchase decisions are now formed, according to a new report by Consumr.ai.
The report, TwinSights: The Consumer Intelligence Trends Shaping 2026, argues that static personas, linear funnels and post-campaign analysis are increasingly ineffective in capturing real consumer behaviour, which it describes as fragmented, context-driven and shaped across multiple touchpoints, often before brands register intent signals.
According to the report, consumer journeys are becoming less linear and more influenced by AI systems and non-visible decision-makers. As a result, insight models built on fixed assumptions are explaining outcomes retrospectively rather than enabling predictive decision-making.
Vivek Bhargava, co-founder of Consumr.ai, said the industry is moving away from hindsight-based analysis towards predictive simulation, allowing brands to test messaging, creative and strategy before deploying budgets. He said this shift reduces decision risk by enabling real-time anticipation of consumer behaviour rather than post-facto evaluation.
The report outlines several shifts reshaping consumer intelligence. It finds that consumers influencing purchase decisions are often not directly visible through standard targeting or attribution systems. It also notes that as third-party data signals weaken, first-party behavioural data is becoming a critical input for understanding and predicting demand.
TwinSights introduces the concept of “invisible audiences,” individuals who influence purchase intent without being the final buyers. These include hidden influencers operating upstream in the decision process, users whose observed behaviour does not match their underlying motivation, and consumers forming intent well before search or transaction activity becomes visible.
The report suggests that these groups represent a disproportionate source of growth potential for brands in 2026, as early-stage influence increasingly shapes outcomes downstream.
The study also examines how AI-powered tools are changing discovery and trust formation. Consumers are increasingly turning to AI models such as ChatGPT, Gemini and Perplexity for recommendations and guidance, creating what the report terms “Answer Engine Influence”, where AI-generated responses affect preference and intent before consumers interact with brand-owned platforms or advertising.
As AI systems mediate more of the discovery process, the report argues that brands need to understand how their information is interpreted and surfaced by these models. It adds that behaviour-led data improves both predictive simulation and how brands are represented within AI-driven discovery environments.
The report concludes that consumer insight is no longer an episodic exercise tied to campaigns or planning cycles. Instead, it is evolving into a continuously updated system driven by observed behaviour, predictive modelling and AI-led interpretation. According to the report, brands that can anticipate behaviour rather than analyse it after the fact are likely to gain an advantage as consumer decision-making becomes more opaque and distributed.
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