Market Research Trends
Research trends: how AI, data integration, and new formats are transforming surveys into tools for forecasting
The market research industry is entering a phase of deep transformation. This shift is no longer about new tools or automating individual stages — it is about a fundamental change in the logic of research itself.
AI is reshaping the nature of research processes. The market is moving away from fragmented surveys, manual analysis, and after-the-fact reports toward continuous, intelligent, and adaptive research embedded into everyday business decisions.
Research is no longer a retrospective description of what has already happened.
It is becoming a tool for forecasting, early risk detection, and a more precise understanding of human behavior.
The gro.now team set out to explore the key trends shaping the market research industry in 2026.
1. AI continues to transform research processes
The shift from manual data processing to AI-supported analysis remains one of the strongest and most sustainable trends. Artificial intelligence is already widely used for natural language processing (NLP), sentiment analysis, behavioral pattern detection, and analytics automation — making research faster, deeper, and more scalable.
Why this matters:
accelerates analysis of large volumes of open-ended and text responses
enables discovery of patterns without predefined hypotheses
expands both quantitative and qualitative research capabilities
Conclusion:
AI is no longer just an automation tool — it is becoming a strategic element of research architecture and a foundation for decision-making.
2. From retrospective surveys to real-time and predictive analytics
Companies are increasingly dissatisfied with analytics based solely on historical data. The market is moving toward real-time analytics and predictive models that rely on streaming data, reviews, digital footprints, and continuously updated behavioral signals.
Why this matters:
enables faster decision-making
reflects customer behavior “here and now”
allows products, services, and communication to adapt dynamically
Conclusion:
Research is no longer a delayed report — it is becoming a tool for operational management and early response.
3. Enriching research data with business metrics (Data Enrichment & Data Fusion)
One of the most visible shifts in recent years is the transition from isolated surveys to integrated data analysis. It is becoming clear that surveys alone no longer provide a complete picture.
Real value emerges when research data is correlated with:
sales
conversion rates
website traffic
user behavior
CRM data
repeat purchases
decision-making time
Conclusion:
Research is no longer a standalone results file — it becomes part of a unified business analytics ecosystem where insights are directly linked to financial and operational performance.
4. Longitudinal research and the customer journey
Interest in long-term (longitudinal) studies is growing. These approaches collect data over extended periods, allowing researchers to track changes in behavior, loyalty, and customer experience over time.
Such studies reveal dynamics and stable patterns rather than one-time reactions.
Why this matters:
supports behavioral forecasting
reduces biases inherent in one-off surveys
highlights the role of time in shaping customer experience
Conclusion:
The focus shifts from momentary snapshots to understanding the full customer journey and long-term loyalty drivers.
5. Multimodal and interactive research methods
Conversational agents, voice interfaces, interactive scenarios, and simulated environments are increasingly becoming part of research practice. Research interactions are evolving into dialogues rather than form-filling exercises.
This leads to higher engagement and richer, more meaningful responses.
Conclusion:
The future of research lies in formats that mirror natural human interaction rather than formal questionnaires.
6. Synthetic data and AI personas
According to Qualtrics, a significant portion of future research may rely on synthetic data — AI-generated responses and behavioral models used for hypothesis testing, scenario modeling, and data gap filling.
Why this matters:
helps compensate for limited representative samples
reduces respondent fatigue
lowers research costs and timelines
Conclusion:
Synthetic data is becoming a supportive research tool, but it must be applied carefully and ethically in combination with real human data.
7. Ethical standards and data protection
Growing demands for transparency, data protection, and regulatory compliance (GDPR, CCPA, and others) are turning research ethics into a competitive advantage.
Why this matters:
directly affects people’s willingness to share honest feedback
increases trust in research processes
becomes part of brand value and loyalty strategy
Conclusion:
Trust in research begins with respect for data and the people behind it.
8. Hyper-personalization and emotional analytics through gamification
A key shift in research is the recognition that people make decisions primarily on an emotional level, while rational answers often serve as post-hoc explanations of feelings. Gamification helps gently move respondents from rational analysis to intuitive and emotional choice. Through game mechanics, scenarios, visual and interactive formats, research begins to capture not only what people think, but how they feel and why they choose.
AI acts as an interpreter of emotions and behavioral patterns, uncovering motivations and hidden preferences that traditional scales and direct questions cannot reveal.
Why this matters:
reduces cognitive resistance
increases engagement and response honesty
enables analysis of emotions and motivation, not just facts
makes insights more actionable for business
Conclusion:
Gamification in research is not entertainment — it is a way to speak to people in the language of emotion and real decision-making.
9. Integration of social data and digital footprints
Social platforms and digital footprints are becoming full-fledged research data sources. Analysis of discussions, comments, reactions, and behavioral patterns enables “surveys without surveys.”
Why this matters:
research reflects real conversations rather than artificial scenarios
data becomes continuous
companies detect shifts in perception and expectations faster
Conclusion:
Billions of data points about your brand, product, and customers already exist online. The key challenge is learning how to interpret them correctly to see reality — not assumptions.
Overall conclusions
Research is no longer a standalone function
It is becoming part of operations, marketing, product development, and customer experience.
The future belongs to continuous and adaptive research
One-off surveys give way to ongoing monitoring, signals, and real-time recommendations.
Value shifts from numbers to meaning and action
Businesses need insights, alerts, and clear next steps — not dashboards for dashboards’ sake.
People are at the center, not methodology
Research formats adapt to real behavior, cognitive limits, and emotional nature.
Emotions and motivation become key data
Understanding “why” matters more than recording “what.”
Trust is the foundation of future research
Without ethics, transparency, and respect for data, technology loses its value.
Research evolves from describing the past to predicting the future
Companies that adapt early gain a strategic advantage that is difficult to replicate.
