Syllabus

MSIN0041 – Marketing Science

2024–2025

Introduction

Marketing professionals have long used data to guide their marketing decisions and measure  the effectiveness of marketing campaigns. Advancements in technology have brought forth  an unparalleled amount of data. Marketers need a more systematic way of analysing data, unearthing insights, and using those insights to help with business strategies.

Marketing science (also known as quantitative marketing) revolves around under- standing complex marketing dynamics to predict business outcomes and recommend strate- gic actions using quantitative research methods, often from economics and statistics. Some  major objectives of this module include the provisions of the following learning objectives:

•  A high-level understanding of the fundamental concepts in marketing

•  An exposure to the marketing problems leading companies seek to address with scientific methods, as well as the results of such efforts

•  A (controlled) practical experience with data and methods in marketing science

•  An intuitive understanding of methods in marketing science.

Logistics

Lectures are held on Tuesdays 2–5pm in Room 508 of Roberts Building. You are advised to check the UCL Timetable regularly for breaks and any unexpected changed to the schedule.

Lecture attendance is an essential part of your learning experience and required by the Programme. If you cannot attend a lecture, please inform the Programme Administrator in advance.

Lecture recordings are available through Lecturecast. Watching the recordings is optional, and lectures are prepared and delivered with the assumption of in-person attendance only. Consequently, the quality of the recordings is not guaranteed, and recordings are not a sub- stitute for lecture attendance. If you have questions about the lecture content, drop by the  regular office hours or reach out to the Module Leader for help.

Office Hours

The module leader’s office hours are held in person on Tuesdays from 1.45pm to 2.45pm in Roberts Building. You are welcome to email the module leader to make an appointment if you cannot attend the in-person office horus.

Emails

Your module leader and teaching assistants may receive a large number of emails during the teaching term. To ensure that your email is read and replied in a timely manner, please include MSIN0041 in the subject line of your email. Unless the matter is urgent, please be patient for a response.

Administrative matters such as technical issues with Moodle and extension requests  should be direct to the Programme Administrator. Sending the module leader or teaching as- sistants such emails will likely result in a delay to the resolution of your issue as the only  thing they can do is to forward your email. For academic questions, emails are only good  for brief and simple questions. More complex questions should be addressed in office hours  instead.

Reading and Software

This module has no required textbook, but there will be some individual readings and case  studies assigned. You are expected to contribute to the in-class discussions about these read- ings and case studies. If you care for additional reading, here are two recommendations by Dr Miao Wei, who has been teaching the well-regarded Marketing Analytics (MSIN0094) module for students in MSc Business Analytics. Both books are either free or available through the UCL Library Services:

• Handbook of Marketing Analytics: This book provides a good survey of analytical methods used in quantitative marketing and their high-impact real-life applications.

• Introduction for Econometrics with R: The authors intend this book to provide an interac- tive environment for the learning of econometrics with empirical applications. While the  authors have modestly stated otherwise, the book is a good introduction/refresher to both  econometrics and R.

Lecture slides will be uploaded to Moodle in two formats: HTML and PDF. The HTML format is recommended for viewing on a digital screen such as a tablet, whereas the PDF format is suitable for printing. Some topics will have supplementary materials notes, which will be uploaded alongside the slides and examinable.

R will be used for computations. You are required to (learn to) use it for your individual coursework. If you have not worked with R on a desktop before, Dr Miao has kindly prepared a step-by-step guide for you to get started with R.

AI Policy

This module allows the assistive use ofAI tools such as ChatGPT, Claude, and Gemini. You are  strongly encouraged to assist your learning with these AI tools. They are extremely helpful in  efficiently obtaining high-level understanding of many fundamental concepts in marketing  and programming. This module assumes that you are comfortable with R thanks to the Data  Analytics module series, but in case you are not, these AI tools should be very helpful in  getting you up to speed with the level of command needed for this module. A typical use of AI is to ask it to explain the codes in the lecture to you, or to ask it to help debug your codes. However, do be aware that AI tools are not a substitute for the learning of the fundamentals, and they are sometimes prone to hallucinate and provide false information. It is strictly pro- hibited to ask AI to (e.g., codes) write your coursework for you.

Assessment

Term                  Name                  Weight

           Table 1: Assessment Components

Assessment components and their respective weights are listed in Table 1. Individual course- work puts more focus on testing your understanding of the fundamental concepts as well as  the technical details. Individual coursework is expected to be challenging and tends to involve  a fair amount of thinking and programming. Questions in the individual coursework tend to  have well-defined correct answers, but open-ended questions will also appear.

Group coursework challenges you to apply the knowledge and skills you have learned  from the module to a real-world marketing problem of your choice. You will have total con- trol over what problem you wish to solve and how you wish to solve it. You will be asked to  form a group of four to six for your group coursework. Team-building and teamwork will be essential for a successful group project, and I encourage you to be cautious and proactive in forming your team. Once you have formed your team, your team collective owns the output from your group coursework, meaning that everyone in your team will receive the same mark barring extremely exceptional circumstances.

The format of the module exam is expected to be an online controlled-condition exam.

No past exams will be provided, but a practice exam that resembles the actual exam in terms of format and difficulty level will be provided to help with your revision. Questions in the module exam tend tobe more open-ended and designed to test your high-level understanding of the fundamental concepts in marketing. There will be no programming-related questions in the module exam. Numerical questions might still appear in the module exam, but the level of complexity and difficulty will be much lower than in the individual coursework.

Important Dates

Individual Coursework 1

•  Assigned on 16 October.

•  Due on 30 October

Individual Coursework 2

•  Assigned on 13 November

•  Due on 27 November

Group Coursework

•  Team formation by 13 November

•  Group coursework report due on 13 December

Module Topics

Note that topics are not necessarily allocated the same amount of time.

Product

Keywords: Language of products, branding, A/B testing, conjoint analysis Readings:

•  Avoid the Pitfalls ofA/B Testing

•  Apple vs Samsung: The $2 Billion Case

•  Apple vs Samsung: The $2 Billion Case Epilogue

Pricing

Keywords: price elasticity, demand estimation, pricing and competition

Price Discrimination

Keywords: first-degree, second-degree, and third-degree price discrimination, bundling Reading:

•  How Targeted Ads and Dynamic Pricing Can Perpetuate Bias

Promotion

Keywords: digital advertising, attribution, targeting, causal inference Reading:

•  Rocket Fuel: Measuring the Effectiveness of Online Advertising

Segmentation, Targeting, and Positioning

Keywords: RFM, K-means, marketing campaign simulation, value Positioning Reading:

•  Market Customization: Market Segmentation, Targeting, and Positioning

•  Artea: Designing Targeting Strategies

•  Business Model Innovation at Wildfang

Customer Lifetime Value

Keywords: customer acquisition, retention, churn, discounted cash flow Reading:

•  Business Model Innovation at Wildfang

User-Generated Content

Keywords: text data, sentiment analysis, topic modelling, recommender system Reading:

•  Have Text, Will Travel: Can Airbnb Use Review Text Data to Optimize Profits?