Course Detail

Statistical Insight into Marketing, Consulting, and Entrepreneurship (41301)

Course Description by Faculty

  • Gilula, Zvi
  • Content
    This course is an elective course in marketing, statistics, and entrepreneurship. Class attendance is strictly limited to 40 students.

    The course is designed for students that have completed at least one quarter in the MBA program (with some exceptios).

    This course will prepare you for more advanced courses in our curriculum such as Big Data and Machine Learning.
    There is no mandatory textbook in this course. Course class slides will be available on the course site.

    Course description
    You decide to establish a start-up in marketing consulting. You search the Internet and find to your dismay well over 650 companies in that area, each one claiming to be best and unique. In order to compete in this arena you need to have the ability to identify upcoming trends and new problems in the marketing area, AND to be able to provide original, sound, fast and applicable solutions to these problems. One such example that is not dealt by many of the marketing consulting companies is the following shelf-planning problem.

    Imagine a customer in a deli store on a Sunday morning intending to buy bagels. There are only two bagels on the shelf. What would you predict the person would do? Hurry up and buy the only remaining bagels before they are gone? Would he consider the two bagels as being the least fresh, touched and left by all former customers, and therefore decide to wait for a fresher batch? As a consultant to the store manager, how would you determine the optimal number of bagels that should be on the shelf at a given time in order to avoid making customers reluctant to buy?

    As it turns out, the methodology covered by this course, that solves the above-mentioned problem, can also be used for the analysis of customer attrition, sale promotion and more.

    Unlike marketing research, marketing consulting is a problem-solving endeavor that requires a great deal of specificity and is fueled by experience. This course is meant to give future consultants and entrepreneurs important tools and ways of thinking that are relevant for dealing with insightful consulting and are useful in the practice of marketing consulting and beyond.

    The course addresses a variety of practical consulting problems and their solutions. Some examples are: (1) Optimal shelf-planning (see the bagels example above); (2) Analyzing customer attrition as a process (rather than as an event-driven phenomenon); (3) Prediction of a customer's purchase behavior (buying intentions, buying propensity, etc.) from the customer's patterns of usage of media, life style, political orientation, etc.; (4) Analysis of satisfaction -how to create a VALID satisfaction scale, how to rank products by satisfaction of customers, how to detect easy-to-please customers, etc.; (5) Analysis of brand loyalty -how to measure loyalty, how to determine whether loyalty to certain brands exists, and how to quantify it; (6) Optimizing predictive modeling when financial rewards and penalties exist in regard to correct and incorrect prediction, respectively.

    The course is taught in a way that emphasizes the interpretation of results rather than computations. Although this course uses statistical reasoning, it is NOT too mathematical in nature. To aid in the analysis, an interactive and user friendly R-based software containing innovative routines will be used in this course. There is no need of programming, or programming skills in this course – except the ability to use your finger to click on a key…

    Teaming up: Students are required to team up: 2-3 students per team. Homework assignments are handed-in on canvas as one copy per team that includes names of all team members.
  • Prerequisites
    Bus 41000 (OR 41100) or equivalent is mandatory: strict. Students that did not take one of these courses but believe they have a strong background in statistics can still bid for the course given the explicit written permission of the instructor. Instructor consent is also required for non-Booth students.
    • Strict Prerequisite

  • Materials
    Class slides.

    R-based software posted on Canvas
  • Grades
    The final grade is a weighted mean between weekly homework (15%), a mid-term (35%) and a final exam (50%), or final project instead.

    Pass/Fail policy: No Pass/Fail grades are allowed in this course. Also, no auditors.
    • No auditors

    • No pass/fail grades

  • Syllabus
  • Autumn 2022Section: 41301-01T 2:00PM-5:00PMBooth 455 (NBC Tower)130In-Person Only
  • Autumn 2022Section: 41301-81T 6:00PM-9:00PMBooth 455 (NBC Tower)130In-Person Only
Description and/or course criteria last updated: July 5 2022