About

John R. Hauser is the Kirin Professor of Marketing at the MIT Sloan School of Management. He teaches courses in new product development, marketing management, and statistical and research methodology. He has served MIT in various leadership roles, including Head of the MIT Marketing Group, Head of the Management Science Area, Research Director of the Center for Innovation in Product Development, and co-director of the International Center for Research on the Management of Technology. Hauser is the co-author of two textbooks, 'Design and Marketing of New Products' and 'Essentials of New Product Management,' along with three other books. He has published over one hundred scientific papers and has been recognized with numerous awards, including the Converse Award, the Parlin Award, the Buck Weaver Award, and the Churchill Lifetime Achievement Award, for his contributions to marketing science and research.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Data Mining
• Econometrics
• Mathematics
• Engineering
• Economics
• Business
• Marketing

Selected publications

• Can Large Language Models Extract Customer Needs as well as Professional Analysts?

  SSRN Electronic Journal · 2025-01-01 · 2 citations

  preprintOpen accessSenior author
  PublisherDOI

• Transforming the Voice of the Customer: Large Language Models for Identifying Customer Needs

  ArXiv.org · 2025-02-25

  preprintOpen accessSenior author

  Identifying customer needs (CNs) is fundamental to product innovation and marketing strategy. Yet for over thirty years, Voice-of-the-Customer (VOC) applications have relied on professional analysts to manually interpret qualitative data and formulate "jobs to be done." This task is cognitively demanding, time-consuming, and difficult to scale. While current practice uses machine learning to screen content, the critical final step of precisely formulating CNs relies on expert human judgment. We conduct a series of studies with market research professionals to evaluate whether Large Language Models (LLMs) can automate CN abstraction. Across various product and service categories, we demonstrate that supervised fine-tuned (SFT) LLMs perform at least as well as professional analysts and substantially better than foundational LLMs. These results generalize to alternative foundational LLMs and require relatively "small" models. The abstracted CNs are well-formulated, sufficiently specific to guide innovation, and grounded in source content without hallucination. Our analysis suggests that SFT training enables LLMs to learn the underlying syntactic and semantic conventions of professional CN formulation rather than relying on memorized CNs. Automation of tedious tasks transforms the VOC approach by enabling the discovery of high-leverage insights at scale and by refocusing analysts on higher-value-added tasks.

  PublisherOA PDFDOI

• The Legacy of John Little for Marketing Science

  Marketing Science · 2025-02-19

  article

  John D. C. Little revolutionized marketing science, and we honor his legacy by reaffirming his decision-calculus principles, reviewing his most impactful research, and conveying his vision for a future that integrates rigor and relevance.

  PublisherDOI

• Real-time adaptive randomization of clinical trials

  Journal of Clinical Epidemiology · 2024-11-16 · 3 citations

  articleOpen accessSenior author

  OBJECTIVES: To evaluate real-time (day-to-day) adaptation of randomized controlled trials (RCTs) with delayed endpoints - a "forward-looking optimal-experimentation" form of response-adaptive randomization. To identify the implied tradeoffs between lowered mortality, CIs, statistical power, potential arm misidentification, and endpoint rate change during the trial. STUDY DESIGN AND SETTING: Using data from RCTs in acute myocardial infarction (30,732 patients in the Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Coronary Arteries, GUSTO-1) and coronary heart disease (12,218 patients in the EURopean trial On reduction of cardiac events with Perindopril in stable coronary Artery disease, EUROPA), we resample treatment-arm assignments and expected endpoints to simulate (1) real-time assignment, (2) forward-looking assignments adapted after observing a fixed number of patients ("blocks"), and (3) a variant that balances RCT and real-time assignments. Blinded real-time adaptive randomizations (RTARs) adjust day-to-day arm assignments by optimizing the tradeoff between assigning the (likely) best treatment and learning about endpoint rates for future assignments. RESULTS: Despite delays in endpoints, real-time assignment quickly learns which arm is superior. In the simulations, by the end of the trials, real-time assignment allocated more patients to the superior arm and fewer patients to the inferior arm(s) resulting in less mortality over the course of the trial. Endpoint rates and odds ratios were well within (resampling) CIs of the RCTs, but with tighter CIs on the superior arm and less-tight CIs on the inferior arm(s) and the odds ratios. The variant and patient-block-based adaptation each provides intermediate levels of benefits and costs. When endpoint rates change within a trial, real-time assignment improves estimation of the end-of-trial superior-arm endpoint rates, but exaggerates differences relative to inferior arms. Unlike most response-adaptive randomizations, real-time assignment automatically adjusts to reduce biases when real changes are larger. CONCLUSION: Real-time assignment improves patient outcomes within the trial and narrows the CI for the superior arm. Benefits are balanced with wider CIs on inferior arms and odds ratios. Forward-looking variants provide intermediate benefits and costs. In no simulations, was an inferior arm identified as statistically superior. PLAIN LANGUAGE SUMMARY: Randomized controlled trials (RCT) are the gold standard in clinical trials - typically half of the patients are assigned to a new drug or procedure and the other half to a placebo (or the current best option). Typically, half of the patients might get an inferior drug or treatment. We explore a method, real-time adaptive randomization (RTAR), that uses information observed up to the time of the next assignment to best allocate patients to treatments, balancing known current and unknown future outcomes-treating vs. learning. RTAR is based on a preplanned, but adaptive, assignment rule. Blinding can be maintained, so that neither the trialist nor the patient knows to which treatment the patient was assigned. During the trial, as the RTAR learns the "best" treatment, the RTAR assigns more patients to that best treatment than would a classical RCT. In two large-scale cardiovascular clinical trials, our simulations suggest that the RTAR would have saved lives while identifying the best post-trial treatment at least as well as an RCT. Some statistical measures are improved and others are worse. If endpoint rates in treatments would have changed dramatically during the trial, the RTAR would have adapted better than many other methods.

  PublisherOA PDFDOI

• Energy-Optimal Attitude Control Strategies With Control Moment Gyroscopes

  IEEE Transactions on Control Systems Technology · 2024-12-31 · 2 citations

  article

  In this work, an optimal spacecraft maneuver planner is developed for rest-to-rest attitude transfers using single gimbal control moment gyroscopes (CMGs). In contrast to conventional optimization approaches developed using simplified dynamical models, this work examines the optimal performance and unique control strategies available to a variable speed CMG array under comprehensive physical models for its dynamics and power consumption. This formulation employs a dynamical model which preserves the array’s (conservative) momentum exchange dynamics, a power model directly tracking the usage of the individual CMG motors, and typical operational safety constraints on input saturation, angular velocity, and camera exclusion cones. On average, the optimal control strategies produced under this comprehensive formulation present a 35% reduction in mean required electrical energy and a 44% reduction in maneuver time over the classic singularity robust (SR) control law. These improvements are observed to correlate with several specific control behaviors. To extend these improvements to practical spacecraft design restrictions, suggestions on how to reproduce these behaviors using existing feedback control methods are provided.

  PublisherDOI

• Supply Side Considerations When Using Conjoint Analysis in Litigation

  SSRN Electronic Journal · 2024-01-01

  articleOpen accessSenior author
  PublisherDOI

• From Clicks to Returns: Website Browsing and Product Returns

  SSRN Electronic Journal · 2024-01-01

  articleOpen accessSenior author
  PublisherDOI

• Product Aesthetic Design: A Machine Learning Augmentation

  Marketing Science · 2023 · 82 citations

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

  Aesthetics are critically important to market acceptance. In the automotive industry, an improved aesthetic design can boost sales by 30% or more. Firms invest heavily in designing and testing aesthetics. A single automotive “theme clinic” can cost more than $100,000, and hundreds are conducted annually. We propose a model to augment the commonly used aesthetic design process by predicting aesthetic scores and automatically generating innovative and appealing product designs. The model combines a probabilistic variational autoencoder (VAE) with adversarial components from generative adversarial networks (GAN) and a supervised learning component. We train and evaluate the model with data from an automotive partner—images of 203 SUVs evaluated by targeted consumers and 180,000 high-quality unrated images. Our model predicts well the appeal of new aesthetic designs—43.5% improvement relative to a uniform baseline and substantial improvement over conventional machine learning models and pretrained deep neural networks. New automotive designs are generated in a controllable manner for use by design teams. We empirically verify that automatically generated designs are (1) appealing to consumers and (2) resemble designs that were introduced to the market five years after our data were collected. We provide an additional proof-of-concept application using open-source images of dining room chairs. History: Puneet Manchanda served as the senior editor. Funding: A. Burnap received support from General Motors to partially fund a postdoctoral research position for the research conducted in this work. He certifies that none of the research or its results were censored or obfuscated in its publication. J. Hauser and A. Timoshenko certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Supplemental Material: The data files are available at https://doi.org/10.1287/mksc.2022.1429 .

  PublisherDOI

• Attitude Trajectory Optimization and Momentum Conservation with Control Moment Gyroscopes

  IFAC-PapersOnLine · 2023-01-01 · 5 citations

  articleOpen accessCorresponding

  In this work, we formulate a specialized trajectory optimization problem and adapt a computationally tractable numerical solver for rest-to-rest attitude transfers with CMG-driven spacecraft. First, we adapt a momentum conserving dynamical model which avoids many of the numerical challenges (singularities) introduced by common dynamical approximations. To formulate and solve our trajectory optimization problem, we design a locally stabilizing Linear Quadratic (LQ) regulator on the system's configuration manifold, then lift it into the ambient state space to produce suitable terminal and running LQ cost functionals. Examining the performance benefits of solutions to this optimization problem, we find significant improvements in maneuver time, terminal state accuracy, and total control effort. Finally, this analysis highlights an acute shortcoming in cost functions which use the control input (rather than accurately modelled power usage) to penalize maneuver energy cost.

  PublisherDOI

• Leveraging the Power of Images in Managing Product Return Rates

  Marketing Science · 2023 · 63 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science

  We use product images to predict individual item return rates, and we complement machine-based prediction with tools to interpret automatically extracted image-based interpretable features.

  PublisherDOI

Frequent coauthors

• Glen L. Urban

  Massachusetts Institute of Technology

  50 shared

• J. J. Wortman

  Pennsylvania State University

  46 shared

• M. A. Littlejohn

  43 shared

• G. Lucovsky

  23 shared

• S. M. Bedair

  North Carolina State University

  22 shared

• Duncan Simester

  21 shared

• Daria Dzyabura

  Skolkovo Moscow School of Management

  19 shared

• Guilherme Liberali

  Erasmus MC

  19 shared

Labs

• MIT Sloan Marketing LabPI

Awards & honors

• Converse Award for contributions to the science of marketing
• Parlin Award for contributions to marketing research
• Buck Weaver Award for lifetime contributions to the theory a…
• Churchill Lifetime Achievement Award for contributions to ma…
• Honorary doctorate from Erasmus School of Economics at Erasm…