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11 - 20 of 24 results for: MKTG

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MKTG 641: Behavioral Research in Marketing I

This course prepares the student to do empirical behavioral research. It will cover all aspects of the research process, from hypothesis generation to experimental design to data analysis to writing up your results.
Terms: Aut | Units: 3
Instructors: Wheeler, S. (PI)

MKTG 644: Quantitative Research in Marketing

The goal of this seminar is to familiarize students with the quantitative marketing literature and develop the process of generating research ideas and topics. Sessions will involve a mix of: nnni) a discussion of papers in a particular area in quantitative marketing; and/or nnii) a discussion of students' research ideas with respect to topics. nnnThe format will mix student presentations of papers with lectures by the instructor(s). When discussing papers in the literature, the focus will be on the topic and research question and not the methodological approach. When discussing research ideas, students should be able to articulate why their question is interesting, where it fits in the literature and how they would address their question.
Terms: Aut | Units: 4
Instructors: Hartmann, W. (PI)

MKTG 645: Empirical Analysis of Dynamic Decision Contexts

This course will focus on empirical tools for analyzing dynamic decision contexts, wherein current actions of firms or consumers have effects on future payoffs, profits and/or competitive conduct. The course will build the relevant material generally, but our applications will be mostly focused on empirical marketing and industrial organization problems. We will have an applied focus overall, emphasizing the practical aspects of implementation, especially programming. The overall aim of the class is to help students obtain the skills to implement these methods in their research. By the end of the class, students are expected to be able to formulate a dynamic decision problem, program it in a language such as Matlab or C, and to estimate the model from data. The course starts with an overview of consumer theory and static models of consumer choice. We build on this material and introduce discrete choice markovian decision problems, and continuous markovian decision problems, and focus on building the computational toolkit for the numerical analysis of these problems. We then move on to specific applications, and discuss multi-agent dynamic equilibrium models. Finally, we discuss recently proposed advanced methods for alleviating computational burden in dynamic models.
Terms: Spr | Units: 3
Instructors: Nair, H. (PI)

MKTG 646: Bayesian Inference: Methods and Applications

The course aims to develop a thorough understanding of Bayesian inference, with a special focus on empirical applications in marketing. The course will start with a brief theoretical foundation to Bayesian inference and will subsequently focus on empirical methods. Initial topics would include Bayesian linear regression, multivariate regression, importance sampling and its applications. Subsequently, the course will focus on Markov Chain Monte Carlo (MCMC) methods including the Gibbs Sampler and the Metropolis-Hastings algorithm and their applications. The overall focus of the course will be on applying these methods for empirical research using a programming language such as R.
Terms: Spr, Sum | Units: 3 | Repeatable 2 times (up to 6 units total)
Instructors: Narayanan, S. (PI)

MKTG 691: PhD Directed Reading (ACCT 691, FINANCE 691, GSBGEN 691, HRMGT 691, MGTECON 691, OB 691, OIT 691, POLECON 691, STRAMGT 691)

This course is offered for students requiring specialized training in an area not covered by existing courses. To register, a student must obtain permission from the faculty member who is willing to supervise the reading.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit

MKTG 692: PhD Dissertation Research (ACCT 692, FINANCE 692, GSBGEN 692, HRMGT 692, MGTECON 692, OB 692, OIT 692, POLECON 692, STRAMGT 692)

This course is elected as soon as a student is ready to begin research for the dissertation, usually shortly after admission to candidacy. To register, a student must obtain permission from the faculty member who is willing to supervise the research.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit

MKTG 695: Directed Research

This course is designed to prepare new marketing PhD students for conducting rigorous, independent research. In this course, the student will work closely with a faculty member in collaborative research activities and will become familiar with various aspects of the research process, including developing hypotheses, designing and conducting experiments and/or analyses, and reporting results.
Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit (up to 99 units total)

MKTG 802: TGR Dissertation (ACCT 802, FINANCE 802, GSBGEN 802, HRMGT 802, MGTECON 802, OB 802, OIT 802, POLECON 802, STRAMGT 802)

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit

MKTG 353: Social Brands

As savvy consumers are increasingly participating in brands rather than merely receiving their messages, how do leading organizations stoke conversations, co-create experiences and stories, and build engaging relationships with consumers? Moreover, how do they harness social media to build a brand, and empower employees and consumers to share these brand stories with others?n nSocial Brands is a hands-on, project-based course that will draw brain power from the GSB, School of Engineering, and other Stanford graduate programs to collaboratively and creatively explore these questions. While we examine various inspiring examples of social brands, we will find that the rules are yet to be written. This emerging genre of social commerce and marketing is the "Wild West" and students working in mixed teams will be challenged to design and launch their own social experiments to form their own hypotheses. n nAssignments will push student teams to audit a brand, focus on a strategic goal, and design a social interaction that invites people on campus to participate in an extraordinary personal experience with that brand. Teams will then capture this experience in short videos and compile them into a story -- one that highlights the brand experience they orchestrated, its impact, and their key learnings. This course will integrate approaches from the d.school and marketing curriculum - including brand strategy, storytelling fundamentals, human-centered methods, rapid prototyping, and a bias toward action. This is a class for those that want to learn by doing and creating.nnMKTG 353 - Social Brands class website: http://www.stanford.edu/class/mktg353/

MKTG 355: Designing for Happiness

We assume happiness is stable, an endpoint to achieve or goal to chase. It's not.nnnWhat we think drives our happiness often doesn't. So what does? And how can knowing this help us create strong companies and brands?nnnUnderstanding happiness is crucial to building successful relationships, products, and organizations. Yet recent research suggests that our definition of happiness is often confused and misguided. In this class, we explore new data on happiness, focusing on:nnnre-thinking happiness (a happy you)nndesigning happiness (a happy company)nnspreading happiness (a happy brand)nnnStudents will work together to use an iterative design-thinking approach to understand our own definitions of happiness, uncover what really makes us happy (vs. what we think makes us happy), prototype solutions/products to increase our happiness, and design happy companies and brands. The class will be data-driven, drawing on multiple methodologies both quantitative and ethnographic. Throughout the quarter, students will build a class-wide database to investigate real-world happiness data via an Designing Happiness app, and test hypotheses about what truly makes them, their teams and their customers happy. This class is recommended for students who plan to be a future entrepreneur building a strong brand, an employee who finds meaning in their work, or someone who wants to understand happiness.
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