ANALYTICS & FORECASTING 2026

Overview: Validating and Integrating AI in Marketing Analytics

AI is reshaping how marketing organizations generate insights, support decisions, and estimate uncertain outcomes. Yet many organizations are still determining when AI outputs are reliable enough to inform consequential decisions, and what forms of human oversight are necessary to preserve accountability.

This conference will examine how firms are incorporating AI into analytics and forecasting workflows, including how they evaluate its outputs, define appropriate human oversight, and maintain transparency and credibility.
The focus is not on AI hype or automation for its own sake. Instead, we will look at how organizations are using AI to improve core analytics and forecasting activities, including measurement, modeling, experimentation, and scenario analysis.

We will also consider a broader view of forecasting, such as traditional sales projections, product adoption, market response, innovation success, and strategic resource allocation.

Join us September 14–15 at Cornell SC Johnson College of Business on Roosevelt Island in New York City. Hosted by MSI, the ARF, and Cornell SC Johnson School of Business , this event brings together leaders from academia and industry to share new research, exchange ideas, and spark discussion.

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Join us September 14–15 at Cornell SC Johnson College of Business on Roosevelt Island in New York City. Hosted by MSI, the ARF, and Cornell SC Johnson School of Business , this event brings together leaders from academia and industry to share new research, exchange ideas, and spark discussion.

This is a member only event and included as part of the membership benefits. Explore what membership includes here.

8:00-9:00am	Registration & Breakfast
9:00-9:15am	Opening Remarks Tulin Erdem, Ph.D. – MSI Executive Director, Professor of Marketing – Leonard N. Stern Professor of Business Oded Netzer – Vice Dean of Research and the Arthur J. Samberg Professor of Business, Columbia Business School
	Using Generative AI for Marketing Intelligence
9:15 – 9:45am	Managing Product Review in the Age of Gen AI—the Role of AI Detection In the era of LLM models like ChatGPT, many platforms and policymakers have become increasingly worried about the spread of AI-generated misinformation, including fake reviews created by AI. In response, researchers and companies have heavily invested in developing various AI detectors across different fields. Learn how well several leading LLM detectors perform in reliably telling apart human-written reviews, AI-generated fake reviews, and human reviews assisted by AI. While there may be a fundamental challenge in reliably distinguishing unwanted AI-generated fake reviews from human reviews helped by AI, can platforms identify potential bad actors with reasonable accuracy? Is the “arms race” between malicious AI-generated fake reviews and AI detectors likely to continue and become more complex? Liye Ma – Professor of Marketing, Robert H. Smith School of Business, University of Maryland
9:45 – 10:15am	The Next Era of Marketing Mix While annual planning remains essential for any business, it is no longer realistic to assume that planning and measurement can be done only once a year for brands. Managing the Marketing Mix and planning for the upcoming year has shifted from an annual task to an on-demand capability, available now and in the near future. To succeed, you must be accurate and actionable, as well as flexible and agile. The mix of art and science is becoming a crucial part of Marketing Mix and measurement overall, with both becoming more interconnected to support the alignment of multiple overlapping metrics. How can brands use the latest technology and top-tier data and modeling to turn Marketing Mix from an annual project into a core capability? Jonathan Dizney – Mix Director, Circana Troy Noble – VP, Global Measurement Product Lead, Circana
10:15 – 10:45am	Consumer Intent in AI-Mediated Interactions: A Focus on Inferring Purchase Intent This study explores how to predict purchase intent during consumer-initiated interactions with AI assistants—both within and outside the purchasing process. As AI assistant adoption rises, identifying purchase intent becomes essential for targeted advertising, especially since many interactions occur outside the traditional purchase funnel. Both methods accurately predicted purchase intent, but the graph-based model performed better for monetizable keywords, demonstrating that specialized “small language models” can outperform LLMs for niche tasks. These findings provide marketers with practical guidance on when and how to advertise via AI assistant channels. Wendy Moe – Dean’s Professor of Marketing, University of Maryland and Amazon Scholar
10:45-11:15am	Morning Break
11:15 – 11:45am	Leveraging Generative AI for Insight Discovery and Delivery By analyzing what has worked before, advertisers can boost their product knowledge and discover fresh approaches. The tricky part? Getting helpful insights to advertisers exactly when they need them. This presentation demonstrates how Amazon Ads addresses this challenge by leveraging a combination of capabilities and Generative AI. Pranav Pandit – Head of Insight Products, Amazon Ads
	Digital Twins for Synthetic Market Data
11:45am – 12:15pm	Twin-2K-500: A Dataset for Building Digital Twins The rise of large language models (LLMs) like GPT has sparked interdisciplinary interest in using “silicon samples” to emulate human responses, affecting both research and practical applications. If these simulations prove reliable, they could improve experimental design, theory development, and customer insights. An increasing number of companies now offer LLM-based tools for these purposes. A high-quality dataset is presented with over 500 questions and 2,000 respondents, demonstrating strong test-retest reliability and replication of known effects. The main focus is on building digital twins, which initially predict human behavior with up to 88% accuracy in test-retest measures. Although encouraging, there are limitations, such as issues with non-normative behavior, representational diversity, and the dataset’s U.S. focus. Still, this resource seeks to accelerate both academic and commercial use of LLM simulations. Tianyi Peng – Assistant Professor, Columbia University Olivier Toubia – Glaubinger Professor of Business, Columbia Business School
12:15 – 12:45pm	Predicting Behaviors with LLM-Powered Digital Twins of Consumers Digital twins of consumers have emerged as a promising approach to simulate consumer thinking, feeling, and decision-making. Grounded in the psychological theory, which conceptualizes behavior as a function of both personal traits and contextual factors, this research proposes and validates a dual-component framework for constructing Large Language Model (LLM)-based consumer digital twins. Fine-tuning on consumer-specific data, including user-generated content, allows the model to internalize individual traits, preferences, and cognitive and behavioral patterns. At the same time, retrieval-augmented generation (RAG) dynamically incorporates information specific to consumer context at inference. By aligning LLM adaptation techniques with foundational psychological theories about behavior, this method enables psychologically grounded simulations of individual-level consumer behavior at a scale. This research contributes to the literature on generative AI, synthetic agents, and digital twins in consumer research and, at the same time, offers a new methodology for theory-driven modeling and privacy-compliant personalization in practice. Shane Wang – Professor of Marketing, Pamplin College of Business, Virginia Tech
12:45 – 1:45pm	Lunch
	Extracting Consumer Preferences
1:45 – 2:20pm	Panel Synthetic Data: Pros and Cons for Market Research Synthetic data enables researchers to quickly create large, privacy-safe datasets, simulate rare consumer behaviors, and test hypothetical product scenarios. It can also help balance samples and cut data collection costs. However, synthetic data might not fully reflect real-world consumer complexity, risk introducing biases, and be difficult to validate. Insights based solely on synthetic datasets may overfit or fail to generalize, making careful testing against real data essential. Hear a variety of perspectives about the benefits and drawbacks of using synthetic data. Neeraj Arora – Arthur C. Nielsen, Jr. Chair in Marketing Research and Education, University of Wisconsin-Madison Ayelet Israeli – Marvin Bower Associate Professor, Harvard Business School Rob Kaiser, Ph.D. – Chief Methodologist, PSB Insights Rajan Sambandam – President, TRC Insights Moderator: Oded Netzer – Vice Dean of Research and Arthur J. Samberg Professor of Business, Columbia Business School
2:20 – 2:50pm	Replicas & Realities: The Strategic Role of Consumer Twins Digital consumer twins are unlocking new possibilities in innovation analytics, from spotting early winners to tailoring concepts for targeted segments. Drawing on Colgate-Palmolive’s CPG experience, this session explores where these models deliver the most value, where they fall short, and how they can shape a more effective learning strategy. Katy Qian – Data Scientist, Colgate-Palmolive Company Helen Wolf, Ph.D. – Senior Director, Global Consumer Experience Insights, Colgate-Palmolive Company
2:50 – 3:20pm	Semantic Targeting This research presents a methodology for real-time, personalized targeting by creating dynamic customer representations that integrate diverse behavioral data. The approach combines LLM-based item embeddings with attention mechanisms to address data sparsity and fragmentation. Evaluated on real-world digital advertising data, the attention-based model outperforms simple averaging, especially for long-term or abstract predictions, producing stable and generalizable customer embeddings. This work advances marketing research by adapting LLMs to customer data and provides practitioners with a scalable, flexible foundation for targeting systems that adapt to evolving consumer behavior. Isamar Troncoso – Assistant Professor of Business Administration, Harvard Business School
3:20-3:50pm	Afternoon Break
3:50 – 4:20pm	Optimal Product Design Synthesis: Pairing Generative Models with Adaptive Preference Measurement This research introduces a preference measurement framework that combines generative AI with adaptive survey design to guide product development based on individual consumer preferences. Using Stable Diffusion to generate realistic product images on-the-fly and high-dimensional Bayesian optimization to learn preferences efficiently, the method goes beyond traditional approaches like conjoint analysis. It captures nuanced aesthetic preferences—colors, textures, shapes—and enables automatic generation of designs aligned with consumer tastes. The framework can reduce design costs, accelerate development, and create products better matched to consumer preferences across industries such as fashion, automotive, and creative goods. Ryan Dew – Assistant Professor of Marketing, The Wharton School, University of Pennsylvania
4:20 – 4:50pm	LLM Time Machines: Valuing Brands Over Time This research explores using Large Language Models (LLMs) like ChatGPT to estimate brand value over time, especially for free digital services such as social media platforms. Traditional methods are expensive and static, failing to reflect dynamic changes in brand equity. By benchmarking LLM-generated valuations against annual data from incentive-compatible discrete choice experiments, LLMs can accurately mimic human choices and valuations. This approach allows for retrospective brand value estimation and provides predictive insights into future trends, highlighting shifts linked to major events and changes in consumer behavior. The method gives marketers a scalable, practical tool to monitor and forecast brand equity, improving strategic brand management. Felix Eggers – Professor, Copenhagen Business School
4:50 – 5:00pm	Closing Remarks Scott McDonald, Ph.D.– President & CEO, ARF
5:00 – 6:30pm	Cocktail Reception

8:00-9:00am	Registration & Breakfast
9:00-9:15am	Opening Remarks Scott McDonald, Ph.D.– President & CEO, ARF Olivier Toubia – Glaubinger Professor of Business, Columbia Business School
	Simulating Consumers
9:15 – 9:45am	From Noise to Signal: When Synthetic Data Adds Real Value Information theory, decision science, and structural analysis offer a principled approach to determining where synthetic data provides the greatest value in market research. By quantifying tradeoffs across analytical accuracy, economic impact, representativeness, and privacy, Dynata can prioritize use cases where synthetic data meaningfully enhances insight, fills gaps, or supports inference. They will illustrate this framework with examples showing how entropy, mutual information, and scenario modeling guide the selective deployment of synthetic data for segmentation, rare-event simulation, and feasibility forecasting. Alan Briancon, Ph.D.– Vice President of Research & Data Science, Dynata
9:45am – 10:15am	Using LLMs for Market Research This paper examines how LLMs can help researchers and practitioners understand consumer preferences. Focusing on the distributional nature of LLM responses, multiple outputs are generated per survey question and present two key findings. First, willingness-to-pay estimates derived from LLM responses are realistic and comparable to human studies. Second, that fine-tuning LLMs with prior survey data improves alignment with human responses for existing and new product features, though not for entirely new categories or segment differences. These results highlight when and how LLMs can meaningfully support product development and market research. Ayelet Israeli – Marvin Bower Associate Professor, Harvard Business School
10:15-10:45am	Morning Break
10:45 – 11:15am	Synthetic Data in Action: Ipsos’ Human-Centered Approach to AI Research  Synthetic data—especially through tools like PersonaBots and digital twins—is transforming market research by enabling faster project turnaround times, greater efficiency, and a powerful new way to access insights. In partnership with Stanford, Ipsos is building a ground-truth database to validate these AI-generated responses. While synthetic participants offer real-time interaction and can simulate hard-to-reach segments, a human-in-the-loop, ethically grounded approach is necessary to ensure accuracy and accountability. The potential is significant, but only with transparent methods for validation and thoughtful strategies to blend human and synthetic respondents. Stephanie Bannos-Ryback – Head of Business Transformation & AI Change Management, Ipsos Xufeng Wang, Ph.D. – Partner & Chief Data Scientist, Data Labs, Ipsos
11:15– 11:45am	Simulating Real Consumers: How Accurately Can LLMs Mimic Individual Respondents in Market Research? This study explores whether Large Language Models (LLMs) can emulate specific individuals for market research. Unlike previous work that focused on group-level preferences or stylized personas, detailed socio-economic, psychographic, and personality data from real people are integrated into an LLM to create synthetic counterparts. The results demonstrate that synthetic respondents can replicate individual consumers with high accuracy. However, systematic biases still exist. The findings suggest that LLM-based synthetic personas cannot yet replace traditional market research but can serve as scalable, fast, and insightful tools when used carefully. Leonard Kinzinger, M.Sc. – Doctoral Researcher at the Professorship of Digital Marketing, Technical University Munich (TUM)
11:45-11:50am	Closing Remarks Scott McDonald, Ph.D. – President & CEO, ARF

Overview: Validating and Integrating AI in Marketing Analytics

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