Monday, September 14 

8:00 – 9:00am 

Breakfast & Registration 

9:00 – 9:15am 

Opening Remarks 

Emerging Research in AI and Marketing Analytics 

9:15 – 9:45am 

From Prompt to Product: Using AI to Guide Aesthetic Design Decisions 

This session presents a research-based framework for structuring AI-assisted product design. While image generation tools enable rapid exploration of design alternatives, they offer no guidance on which aesthetic directions are appropriate for a given product category or which will resonate with consumers. The framework addresses this gap by identifying the design dimensions that can be manipulated within a product category, distinguishing them from dimensions locked in by category conventions, and isolating those that independently drive consumer choice. Results from studies across different product categories demonstrate how the approach informs design exploration, testing, and differentiation strategy. 

Jeffrey P. Dotson – Associate Professor, The Ohio State University, Fisher College of Business

9:45 – 10:15am

The AI Value Gap: What Marketing Leaders Do Differently

Despite high expectations, few organizations are realizing meaningful value from AI today. New research from Google and Bain & Company reveals that leaders are 2x more likely to see value from their AI investments. This session explores what they are doing differently and examines one of the biggest barriers to realizing AI’s potential: signal loss and disconnected data. Learn how leading marketers are redesigning workflows, strengthening data foundations, and embedding business intelligence to drive better outcomes. How can marketers reconnect signals, fill critical blind spots, and build smarter, more impactful AI-powered marketing solutions?

Imran Ahmed – Head of Measurement, Tech & Lifestyle, Google

Brian Dennehy – Expert Partner, Bain and Company

Hayden Ostendorf – Practice Sr. Manager, Bain & Company

10:15 – 10:45am 

Discovering Textual Drivers of Marketing Outcomes from LLM Internals: A Sparse Autoencoder and Multi-Agent Pipeline 

Firms sit on enormous volumes of customer text: reviews, support tickets, chats, social media posts. There is so much text that they need machine learning to make sense of it. But those techniques can only answer the handful of hypotheses the analyst thinks to test. This session shows how the internals of large language models can be used as a reusable measurement tool to test thousands of hypotheses analysts never considered.  

Shane Wang – Professor of Marketing, Pamplin College of Business, Virginia Tech University 

10:45 – 11:15am 

Morning Break 

11:15 am – 11:45am 

Panel Discussion: AI and the Future of Marketing Research 

What happens when AI becomes not just a tool for analyzing marketing research but a participant in it? As organizations experiment with synthetic respondents and AI-generated consumers, longstanding assumptions about research design and validation are being challenged. This panel brings together industry leaders and academic researchers to explore where AI is delivering meaningful value today, where expectations may be outpacing reality, and how marketing research is likely to evolve in the coming years. 

Raymond Burke – Professor of Marketing, Kelley School of Business, Indiana University 

Leabe Commisso – Senior Vice President of Strategic Growth, Ipsos 

Ankit Dhawan– Founder & CEO, BluePill AI  

Stefano Puntoni – Professor of Marketing, The Wharton School 

Moderator: Wendy Moe – Dean’s Professor of Marketing, Robert H. Smith School of Business, University of Maryland 

11:45am – 12:15pm 

Collaborative Intelligence: Reconstructing the Invisible Consumer From Fragmented Data 

Firms routinely use large-scale consumer surveys to support segmentation, targeting, and product strategy, but these surveys are often modular by design and fragmented in use, making it difficult to represent and interpret some consumers. The framework supports accurate prediction, outperforming off-the-shelf GPT and linear multi-task baselines, while recovering interpretable heterogeneity that fragmentation can leave unseen. 

Alice Li – Associate Professor, Fisher School of Business, The Ohio State University
 

12:15 – 12:45pm 

Synthetic Conjoint Methods for B2B Business Decisions  

In B2B software, where buying cycles run 9 to 12 months, and most prospects are locked into multi-year contracts with incumbents, forecasting adoption is one of the hardest jobs in product strategy. We compared the results of a human conjoint with five synthetic conjoint results. Two yielded decision-grade results. We achieved this by leveraging data from existing sales calls and implementing specific decision instructions. Our improved method showed only a small (1.5%) difference in the share of the none-option (used for adoption decisions).    

Rogier Verhulst – CEO Kwantumlabs.ai  

Marco Vriens – Founder Kwantumlabs.ai 

12:45 – 1:45pm 

Lunch 

Validating AI for Marketing Analytics 

1:45 – 2:15pm 

When AI Incentives Help and Hurt

This research examines how frontline employees experience and respond to continuous AI-generated feedback in customer service operations. As firms increasingly use AI/ML systems to evaluate every customer interaction and link scores to pay, scheduling, and advancement, AI feedback is becoming an always-on part of everyday work. Yet we know little about whether these systems serve as useful performance guidance or instead create stress, resistance, and unintended behavioral distortions. The study offers guidance for designing AI evaluation systems that improve performance while preserving fairness, learning, and employee well-being. 

Jia Li – Associate Professor, Wake Forest University
 

2:15 – 2:45pm 

Validating Enterprise AI Retrieval Systems for Structured Analytics Data

Can AI accurately answer questions across millions of rows of enterprise data? This session explores the challenges of applying RAG to structured datasets and compares several retrieval strategies through real-world evaluations. Learn which approaches perform best, where they fail, and how to validate AI before deploying it to production. 

Harini Devulapalli – Senior Manager, Chewy 

2:45 – 3:15pm 

From Reviews to Actionable Insights: An LLM-Based Approach for Attribute and Feature Extraction 

Firms have access to vast amounts of customer review data but often lack a scalable way to turn it into actionable decisions. This research introduces an LLM-based framework that distinguishes broad perceptual attributes from specific, managerially actionable product and service features. Applied to 20,000 Starbucks Yelp reviews, the approach yields results that closely align with human coding and strongly predict customer ratings, while processing reviews in seconds rather than minutes. The resulting structured data support dashboards that track sentiment across stores and over time, identify customer “joy points” and “pain points,” and prioritize high-impact improvements. 

Khaled Boughanmi – Assistant Professor of Marketing, Cornell University, Johnson Graduate School of Management 

3:15 – 3 :45pm 

Afternoon Break 

3:45 – 4:15pm 

Why Your AI Persona is Lying to You: The Danger of Data Overload in Synthetic Populations 

Amid growing reliance on Artificial Intelligence for market research and decision-making, the authors explore the limits of the technology by reviewing the conclusions of their multi-stage study comparing actual respondents to synthetic panels. While the literature thus far has focused on the ability to “sound human,” the study reveals that actually mimicking consumer behavior requires more than simply adding more data to the model. 

Luiz G. Duarte, Ph.D. – Sr. Research Consultant, Wortya  

4:15 – 4:45pm 

Extracting Consumer Insight from Text: A Large Language Model   Approach to Emotion and Evaluation Measurement 

This study introduces the Linguistic eXtractor (LX), a fine-tuned large language model trained on consumer-authored text that has also been labeled with consumers’ self-reported ratings of 16 consumption-related emotions and four evaluation constructs: trust, commitment, recommendation, and sentiment. LX consistently outperforms leading models, including GPT-4 Turbo, RoBERTa, and DeepSeek, achieving 81% macro-F1 accuracy on open-ended survey responses and over 95% accuracy on third-party–annotated Amazon and Yelp reviews. 

Peter Danaher – Professor of Marketing and Econometrics, Monash   University 

4:45 – 5:15pm 

Closing the Innovation Forecast Gap: An AI/ML Approach to New Product Forecasting and Incrementality at Kenvue 

Forecasting new product demand remains a persistent challenge because of historical sales  

data are often unavailable, and traditional methods frequently rely on surveys, analogs, or subjective judgment. This presentation introduces an AI/ML-based forecasting framework that unifies demand prediction and incrementality measurement within a decision-intelligence platform. The solution integrates syndicated POS data, historical launches, execution variables, and AI-enriched product attributes to forecast Year 1 and Year 2 performance while estimating cannibalization and net-new growth. 

Beyond improved accuracy, the approach enables scalable concept evaluation, scenario simulation, and execution guardrails, allowing organizations to make faster, more confident innovation decisions while reducing dependence on costly concept-testing studies. 

JJ Huang – Director, Global Data Science, Kenvue 

Lana Klein – Practice Lead, Cloverpop

Raul Jurado – Principal and Planning Intelligence Lead, Cloverpop

5:15 – 5:30pm 

Closing Remarks 

5:30 – 6:45pm 

Cocktail Reception 

_________________________________________________________________________ 

Tuesday, September 15 

8:00 – 9:00am 

Breakfast & Registration 

9:00 – 9:15am 

Opening Remarks 

Human + AI: Designing Productive Collaboration 

9:15 – 9:45am 

GEO in the Loop: A Framework for Integrating Generative Engine Optimization Into Marketing Analytics Workflows 

Generative AI tools such as ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews are now a de facto customer acquisition channel. In Q1 2026, AI-driven channels generated 876,000 credit card acquisitions in the U.S. alone, nearly doubling AI’s share of card issuance in a single quarter. Yet most marketing analytics teams lack a validated framework to measure, audit, or govern their brand’s presence in these outputs. This session introduces a practitioner-oriented GEO measurement framework built on four new KPIs – Answer Share, Citation Inclusion Rate, Factual Accuracy Score, and Brand Framing Sentiment, paired with a structured human-in-the-loop (HITL) audit workflow and governance model. Drawing on early field deployments in financial services, we share design principles, organizational friction patterns, and preliminary audit findings, including three error types (competitive misattribution, outdated product information, and brand framing inconsistencies) that automated scoring cannot detect. 

Manish Hira – Data Analysis Manager, Capital One; Ph.D. Scholar, Virginia Commonwealth University 

9:45 – 10:15am 

Who Chops the Onions? The Hidden Ingredients in Every Profitable AI Agent 

AI agents are moving into business processes much faster than firms are learning how to evaluate them. This session traces where agentification stands today and walks through its six stages, from deciding whether agents belong in a workflow at all to measuring what they actually returned. At each stage we pair anecdotes from the field with the best available academic and industry evidence to show where AI initiatives most often succeed, stall, or leak value. The central question we ask is: where in your own process does the next dollar of return come from? 

Dr. Adithya (Adi) Pattabhiramaiah – The Sharon A. and David B. Pearce Associate Professor of Marketing and Faculty Director, Center for AI in Business at the Scheller College of Business, Georgia Institute of Technology 

10:15 – 10:45am 

Operationalizing Causal Machine Learning to Optimize Online Ads Parameterization Using Brand Lift Studies Data 

In this session, we present work conducted with a multinational food and beverage corporation to optimize online ad parameterization using Brand Lift Study data. Our primary questions were: what are the causal drivers of brand lift on a major online video platform? And how do these effects vary across campaign levers and cost metrics? Our goal was to build a scalable, auditable analytical system. This system produces calibrated causal effect estimates for key campaign dimensions (format, duration, device, frequency, and audience) suitable for operational decision-making within a global marketing organization. We will first introduce Causality ETE, an end-to-end causal AI framework designed for applied marketing use cases. We will then report results from its large-scale deployment for the use case described above. This work makes two contributions: a structured methodology for integrating human expertise and causal AI in marketing analytics, and a reusable, auditable artifact implementing this methodology in a production environment.

Audrey Poinsot – Research Lead, Ekimetrics
 

10:45am – 11:10am 

Morning Break 

11:10 – 11:40am 

When Search Stops Sending Traffic: The Impact of Google AI Overviews on Organic Traffic Monetization in E-Commerce 

Google’s AI Overviews are changing not only how much traffic search engines send to e-commerce websites but also the commercial composition of that traffic. Using a large-scale panel of e-commerce domains worldwide, we examine the staggered international rollout of AI Overviews and its effects on organic sessions, transactions, revenue, and monetization quality. We find that AI Overviews reduce Google organic traffic, while completed transactions and total revenue remain largely unchanged. At the same time, conversion rates and revenue per session increase, whereas average order value does not change materially. These results suggest that AI Overviews act as a filtering mechanism: they absorb lower-intent information-seeking visits within the search interface while preserving more commercially qualified traffic. Search Console evidence further links this pattern to changes in search-result visibility, particularly for informational queries. 

Qiwei Han – Assistant Professor, Nova School of Business and Economics, Portugal 

11:40 am – 12:10pm

Mike Finnerty – President, US @ Mutinex  

12:10 – 12:15pm 

Closing Remarks

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