BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Wisconsin School of Business - ECPv6.15.20//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://business.wisc.edu X-WR-CALDESC:Events for Wisconsin School of Business REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 TZNAME:CDT DTSTART:20250309T080000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 TZNAME:CST DTSTART:20251102T070000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 TZNAME:CDT DTSTART:20260308T080000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 TZNAME:CST DTSTART:20261101T070000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 TZNAME:CDT DTSTART:20270314T080000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 TZNAME:CST DTSTART:20271107T070000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/Chicago:20260511T130000 DTEND;TZID=America/Chicago:20260513T120000 DTSTAMP:20260422T045230 CREATED:20251201T210023Z LAST-MODIFIED:20260406T152707Z UID:10001669-1778504400-1778673600@business.wisc.edu SUMMARY:Symposium on Artificial Intelligence in Marketing 2026 DESCRIPTION:Academic Event for Researchers\, Wisconsin School of Business\n\n\n\n\n\n\n\nThe Wisconsin School of Business at the University of Wisconsin-Madison is thrilled to host the 3rd Symposium on Artificial Intelligence in Marketing. The 2026 Symposium will be taking place from May 11\, 1pm to May 13\, 12pm in Madison\, Wisconsin. The symposium focuses on modern AI applications in marketing\, including: \n\n\n\n\nDeep Learning and Unstructured Data Analysis\n\n\n\nGenerative AI in Marketing\n\n\n\nHuman-AI collaboration\n\n\n\nAlgorithmic Bias and Fairness\n\n\n\nDigital Twins\n\n\n\nProbabilistic Machine Learning and Computational Methods for Marketing\n\n\n\n\nEmail your questions to aimarketing@wsb.wisc.edu \n\n\n\n\n\n\n \n \n\n Keynotes\n \n \n \n \n \n \n \n \n \n Simon Blanchard \n Professor of Marketing – Georgetown University Simon Blanchard is the Dean’s Professor and Professor of Marketing at Georgetown University’s McDonough School of Business. His research spans a broad range of marketing methods\, including survey design\, controlled experiments\, and econometric analysis of large datasets. As a methodologist\, he brings flexibility and rigor to studying complex decision-making\, often applied to how consumers navigate financial trade-offs and emerging technologies. \nFor a three year term beginning in April 2026\, he will serve as co-editor of the Journal of Marketing Research. Previously\, he served as Associate Editor at four journals (Journal of Marketing\, Journal of Marketing Research\, Journal of Consumer Research\, and the International Journal of Research in Marketing) and has served on the AMA Academic Council. He still serves on the board of the INFORMS Society for Marketing Science. \nOutside of academia\, Professor Blanchard advises organizations on research design\, surveys\, marketing analytics\, digital strategy\, and fintech. He also serves as a subject-matter expert for the Analysis Group. \n \n \n \n \n \n \n \n \n \n \n \n David Schweidel \n Professor of Marketing – Emory University’s Goizueta Business School David A. Schweidel is the Goizueta Chair in Business Technology and Professor of Marketing at Emory University’s Goizueta Business School. Schweidel received his B.A. in mathematics\, M.A. in statistics\, and Ph.D. in marketing from the University of Pennsylvania. \nHe has served on the faculty of the Wisconsin School of Business at the University of Wisconsin-Madison and Georgetown University’s McDonough Business School. His research has appeared in leading business journals including Journal of Marketing\, Journal of Marketing Research\, Marketing Science\, Management Science\, and Harvard Business Review.  Schweidel is co-author of Social Media Intelligence and author of Profiting from the Data Economy. \nHis current research focuses on consumers’ use of new technologies\, and how these technologies can be leveraged by marketers. In addition to his research\, he has consulted for companies including eBay\, Airbnb\, and Thumbtack. \n \n \n \n \n \n \n\n\n\n\n \n \n\n\n\n\n\n\n\nSchedule\n\n\n\nMonday\, May 11th\n\n\n\nTimeSession Title1:00 PM – 2:00 PMWelcome + AI Hub Keynote2:00 PM – 3:00 PMMSI Industry Panel3:00 PM – 3:20 PMBreak3:20 PM – 5:00 PMSession: Leveraging AI for Economic Value and Personalization5:00 PM – 5:10 PMBreak5:10 PM – 7:00 PMReception\n\n\n\nTuesday\, May 12th\n\n\n\n Time Session Title 8:00 AM – 9:00 AMBreakfast9:00 AM – 10:00 AMKeynote 110:00 AM- 10:20 AMBreak10:20 AM – 12:00 PMSession: Understanding Content & AI-Assisted Creativity12:00 PM – 1:30 PMLunch + Keynote 21:30 PM – 2:00 PMBreak2:00 PM – 3:15 PMSession: Modeling Consumers with AI3:15 PM – 3:45 PMBreak3:45 PM – 5:00 PMSession: Search\, News & Recommendations\n\n\n\nWednesday\, May 13th\n\n\n\n TimeSession Title 8:00 AM – 9:00 AMBreakfast9:00 AM – 10:40 AMSession: Causal Inference & Experimental Design10:40 AM- 11:00 AMBreak11:20 AM – 12:15 PMSession: Methodological Advances in Measurement\, Optimization\, and Estimation\n\n\n\n \n\n\n\n\n\n\n\nRegistration\n\n\n\nPlease register using the link below. Note that we have a very limited number of spots.Deadline: April 23rd. \n\n\n\nRegular Attendees: $250.00Includes access to all sessions\, materials\, and meals. \n\n\n\nPhD Students & Scientific Review Committee: $100.00Includes access to all sessions\, materials\, and meals. \n\n\n\nInternal Employees/PhD Students: (Wisconsin School of Business only)Registration covered by internal funding \n\n\n\n\nRegister Now\n\n\n\n\nThe confirmation email is usually sent within 15 minutes. \n\n\n\n\n\n\n\nLodging\n\n\n\nWe have partnered with the The Madison Concourse Hotel to provide rooms at affordable rate for both nights (5/11 and 5/12) at $169 per night.These prices will be available until April 24\, 2026 or until rooms are sold out (whichever comes first). For complimentary airport shuttles\, 4:30AM-10:30PM\, call 608-257-6000.To reserve a room\, follow the following link. \n\n\n\n\nReserve a room\n\n\n\n\n\n\n\n\nSessions\n\n\n\nLeveraging AI for Economic Insight and Personalization(Mis)Measuring the Drivers of Ad Performance: A User Guide for LLMs in Empirical Creative ResearchGijs Overgoor\, Samsun Knight\, Yakov BartEconomic Value of Visual Product CharacteristicsVineet Kumar\, Ankit SisodiaLearning Heterogeneity from Unstructured Data: An Application to Chatbot PersonalizationKhai Chiong\, Ryan DewCan Explanations Improve Recommendations? A Joint Optimization with LLM ReasoningYuyan Wang\, Pan Li\, Minmin ChenUnderstanding Content & AI-Assisted CreativityLarge Language Models as Engines of Understanding: Interpretable Discriminative Features from TextTong Wang\, Yiqing Xu\, Leo YangModeling Serialized Content Consumption: Adversarial IRL for Dynamic Discrete ChoicePeter S. Lee\, K. Sudhir\, Tong WangContent-Aligned Cover Design: A Multimodal Deep Learning Approach to Understanding and Enhancing Media Cover SuccessYijing Xu\, Remi Daviet\, Maria HademerGuided Creativity: AI Intermediation for Enhancing Quality and Originality in Visual DesignXuekang Wu\, Guy Aridor\, Artem TimoshenkoModeling Consumers with AIPredicting Behaviors with Large Language Model (LLM)-Powered Digital Twins of ConsumersBingqing Li\, Qiuhong (Owen) Wei\, Xin (Shane) WangBayesian Machine Learning Approach for Modeling Dynamic Consumer PreferencesHengxu Lin\, Kohei Onzo\, Asim AnsariCollaborative Intelligence: Reconstructing Invisible Consumers with a Fine-Tuned LLM and Multi-Task Learning FrameworkJiyeon Hong\, Alice Li\, Qing LiuSearch\, News & RecommendationsAddressing Consumers’ Sensitive Attributes in Product Recommendations: An Explainable AI Recommendations System ApproachPiyush AnandGenerative Search: Evidence from a Large-Scale Field ExperimentYuting Zhu\, Shuang ZhengThe Impact of LLMs on Online News Consumption and ProductionHangcheng Zhao\, Ron BermanCausal Inference & Experimental DesignLeveraging LLMs to Improve Experimental Design: A Generative Stratification ApproachGeorge Gui\, Seungwoo KimGENI: GenAI-Assisted Inference for Emotional Ad CopyXinyue Zhao\, Xueming LuoA Bayesian Latent-Factor Framework for Causal Decomposition in High-Dimensional ExperimentsKhaled Boughanmi\, Raghuram Iyengar\, Young-Hoon ParkPersonalized Policy Learning through Discrete Experimentation: Theory and Empirical EvidenceZhiqi Zhang\, Zhiyu Zeng\, Ruohan Zhan\, Dennis ZhangMethodological Advances in Measurement\, Optimization\, and EstimationPre-Training Estimators for Structural Models: Application to Consumer SearchZhenling Jiang\, Yanhao WeiTextBO: Bayesian Optimization in Language Space for Eval-Efficient Self-Improving AIEnoch Hyunwook Kang\, Hema YoganarasimhanTo Err Is Human; To Annotate\, SILICON? Reducing Measurement Error in LLM AnnotationXiang Cheng\, Raveesh Mayya\, Joao Sedoc*Names in bold are the presenters\n\n\n\n\n\n\n\nLocation\n\n\n\n\n\n\n\n\n \n \n\n Symposium Chairs\n \n \n \n \n \n \n \n \n \n Remi Daviet \n Assistant Professor of Marketing\, UW-Madison daviet@wisc.edu \n \n \n \n \n \n \n \n \n \n \n Qing Liu \n Associate Professor of Marketing\, UW-Madison qliu@bus.wisc.edu \n \n \n \n \n \n\n\n\n\n \n \n\n\n\n\n\n\n\nScientific Review Committee\n\n\n\nPapers are reviewed by at least two members of the scientific committee. \n\n\n\nCo-chairs: Ishita Chakraborty\, Remi Daviet \n\n\n\nMembers:Wenjia Ba\, University of British ColumbiaKhaled Boughanmi\, CornellEmaad Manzoor\, CornellGijs Overgoor\, Southern Methodist UniversityYuyan Wang\, StanfordMengxia Zhang\, Western University (Ivey) URL:https://business.wisc.edu/event/symposium-on-artificial-intelligence-in-marketing-2026/ LOCATION:Pyle Center\, 702 Langdon St.\, Madison\, WI\, 53706\, United States CATEGORIES:Artificial Intelligence,Marketing Leadership Institute ATTACH;FMTTYPE=image/png:https://business.wisc.edu/wp-content/uploads/2023/11/Grainger-Data-Sky-Red-replacing-pink.png END:VEVENT END:VCALENDAR