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Description
Marketing analytics addresses how to utilise the right analytics tools to guide marketing tactics and strategies in a scientific manner. Driven by recent advances in information and communication technologies, the very nature of marketing analytics has evolved. This module provides students with systematic and practical training in R programming, machine learning, and causal inference tools to solve real-life marketing problems.
In the first week, students will learn the principles of marketing and how to conduct cost-benefit analyses for marketing activities.听 Starting in week 2, students will gradually acquire data wrangling skills in R and learn how to apply these skills in descriptive analytics. In week 3 and week 4, students will learn predictive analytics and how to apply machine learning models for segmentation, targeting, and customer relationship management. In the remaining weeks, students will learn causal inference tools, which help marketers correctly measure the causal effects of marketing initiatives on marketing outcomes. Students will get hands-on training in A/B testing, linear regression, instrumental variable method, difference-in-differences design, regression discontinuity design, and causal machine learning.
The course uses a combination of lectures, cases, and exercises to achieve the best learning outcomes. This course takes a very hands-on approach with real-world databases and equips students with tools that can be used immediately on the job. At the end of the module, students will be able to carry out independent marketing research for their dissertation projects and their future jobs.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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