September 29 – 30, 2025 | Geffen Hall, Columbia Business School, 645 W. 130th Street| In-Person & Livestream
9am-5pm on 9/29; 9am-12:30pm on 9/30
Call for Content is Open!
We are currently accepting academic submissions from MSI academic members for this event. Find out more here.
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 16–17 at Cornell Tech on Roosevelt Island in New York City. Hosted by MSI, the ARF, and Cornell Tech, this event brings together leaders from academia and industry to share new research, exchange ideas, and spark discussion.
We seek theoretical, empirical, and methodological contributions about validating and integrating AI in marketing analytics. We also encourage industry-focused submissions—including case studies and applied perspectives.
The application is free and open to MSI and ARF members and non-members. All submissions are due by May 15 and must be submitted via the Call for Content portal. Download the extended abstract template here.
1) Validation and Performance Assessment
A central theme of the conference will be methods for validating AI-enabled analytics, including:
We will examine how leading organizations and thought leaders are building evidence to support (or challenge) AI-driven decisions, grounded in empirical research and real implementations.
2) Human + AI: Designing Productive Collaboration
As AI tools become embedded in modeling, forecasting, and decision support, firms must clarify the role of human expertise. Sessions will explore:
The goal is to move beyond automation toward thoughtful integration.
3) Transparency, Data Context, and Trust
As AI reshapes how data is generated, accessed, and interpreted, new transparency challenges emerge:
This discussion will prioritize practical experience over theory, focusing on how firms maintain credibility and accountability.
4) Evidence from the Field: Marketing Use Cases
Throughout the program, sessions will be grounded in ongoing empirical work and concrete use cases across marketing domains and verticals. We aim to highlight:
By grounding the conversation in evidence, the conference will help participants calibrate expectations and identify areas requiring further experimentation.
5) Emerging Research in AI and Marketing Analytics
Ongoing research continues to expand understanding of AI-enabled marketing analytics, decision systems, and data-driven marketing more broadly.
This work will help connect emerging research insights with evolving challenges in AI-enabled analytics and forecasting.
On September 29-30, the ARF and MSI co-produced the inaugural ANALYTICS & FORECASTING conference, exploring the evolving role of modeling in market research and forecasting, with a particular focus on the opportunities and limitations of synthetic data. Attendees engaged in critical discussions about the opportunities and limitations of modeling in market research applications and heard practical strategies and solutions from leading researchers and practitioners.
Neeraj Arora
Arthur C. Nielsen, Jr. Chair in Marketing Research and Education,
University of Wisconsin-Madison
Stephanie Bannos
EVP Transformation & Change Management
Ipsos
Alain Briancon, Ph.D.
VP of Research and Data Science,
Dynata
Ryan Dew
Assistant Professor of Marketing, The Wharton School,
University of Pennsylvania
Jonathan Dizney
VP, Global Measurement Insights,
Circana
Felix Eggers, Ph.D.
Department of Marketing,
Copenhagen Business School
Tulin Erdem, Ph.D.
MSI Executive Director, Professor of Marketing, Leonard N. Stern Professor of Business
NYU Stern School of Business
Ayelet Israeli
Marvin Bower Associate Professor,
Harvard Business School
Rob Kaiser, Ph.D.
Chief Methodologist,
PSB Insights
Leonard Kinzinger
Doctoral researcher at the Professorship of Digital Marketing at the Technical University of Munich (TUM)
Liye Ma
Professor of Marketing at the Robert H. Smith School of Business,
University of Maryland
Scott McDonald, Ph.D.
President & CEO,
ARF
Wendy W. Moe
Dean's Professor of Marketing ,Robert H. Smith School of Business,
University of Maryland, College Park
Oded Netzer
Vice Dean of Research and the Arthur J. Samberg Professor of Business, Columbia Business School
Troy Noble
VP, Global Measurement Product Lead,
Circana
Pranav Pandit
Head of Insight Products,
Amazon Ads
Tianyi Peng
Assistant Professor, Decision, Risk, and Operations
Division, Columbia Business School
Katy Qian
Data Scientist, Colgate-Palmolive
Rajan Sambandam
President,
TRC Insights
Olivier Toubia
Glaubinger Professor of Business, Columbia Business School
Isamar Troncoso
Assistant Professor of Business Administration, Harvard Business School
Shane Wang
Professor of Marketing at Pamplin College of Business,
Virginia Tech
Xufeng Wang, Ph.D.
Partner and Chief Data Scientist
Ipsos U.S. Data Labs
Helen Wolf, Ph.D.
Senior Director, Global Consumer Experience Insights
Colgate-Palmolive Company
Helen Wolf, Ph.D.
Senior Director, Global Consumer Experience Insights
Colgate-Palmolive Company
EVP Global Media, CPG and Retail, Circana
Head of Marketing Insights, LG Ad Solutions
E.W. Kelley Professor of Business Administration Indiana University, Kelley School of Business
Analytics Manager, Arc Worldwide
News Editor, Ad Age
eCommerce/omnichannel Marketing Director, PepsiCo
Research & Insights Manager, North America & Global Functions, TikTok
Professor of Marketing, Kellogg School of Management, Northwestern University
Managing Director, Commerce at GroupM's Mindshare
Founder and CEO,
Data POEM
Head of Insights & Analytics, Walgreens Advertising Group
Global Head of Virtual Experiences, Ipsos
Research Director, MSI
Chief Knowledge Officer, MSI
Chief Research Officer, ARF
| 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 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 |
Analytics & Forecasting 2025: Past Event Highlights
Privacy, Trust & AI: How U.S. Consumers Are Rewriting the Rules of Advertising
To further, through research, the scientific
practice of advertising and marketing
© the Advertising Research Foundation. All rights reserved. – Privacy Policy
