UCL School of Management

Module Fact Sheet

MSIN0094: Marketing Analytics

Taught by
Masters, level 7
MSc Business Analytics (MS); SOM MRes/PhD
Delivery method
10 x 3-hour teaching block
45% individual coursework, 45% group coursework, 10% class participation
Previous Module Code

Course overview

Customer-Centric Marketing Analytics addresses how to use data analytics to learn about and market to individual customers.

Marketing is evolving from an art to a science. Many firms have extensive information about consumers’ choices and how they react to marketing campaigns, but few firms have the expertise to intelligently act on such information. In this course, students will learn the scientific approach to marketing with hands-on use of technologies such as databases, analytics and computing systems to collect, analyze, and act on customer information. This is the key part of learning how to take advantage of Big Data. While students will employ quantitative methods in the course, the goal is not to produce experts in statistics; rather, students will gain the competency to interact with and manage a marketing analytics team.

The course uses a combination of lectures, cases, and exercises to learn the material. 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.

Learning outcomes

Step 1: To introduce the customer as the unit of analysis

  • To understand the premise behind customer-centric marketing
  • To understand the customer lifecycle
  • To understand the concept of customer profitability
  • To understand the basics of lifetime value calculations
  • To explore how lifetime value can be used to guide marketing decisions

Step 2: To introduce the key strategic initiatives using customer information

  • To understand how to acquire customers
  • To understand how to do customer development
  • To understand how to cross-sell
  • To understand how to up-sell
  • To understand how to manage customer churn (attrition)

Step 3: To introduce analytical and statistical modeling of customer information

  • To distinguish good from bad analytics
  • To understand different types of predictive models (Heuristics, Statistical Models, Machine Learning, Algorithmic)
    • RFM Analysis (Heuristics)
    • Logistic Regression (Statistical Model)
    • Neutral Nets & Decision Trees (Machine Learning Models)
    • Market Basked Analysis/Recommendation Systems (Algorithmic Models)
  • To understand how to choose among offers with experiments

Step 4: To understand when analytical models are appropriate and when they fail

  • To understand why customer analytics has sometimes failed in the past
  • To learn how to avoid common mistakes in implementing customer analytics
  • To understand how marketing objectives interact with how customer data should be used

Topics covered

  • Prospecting and Targeting the Right Customers
  • Developing Customers
  • Retaining Customers
  • Selecting the Right Offers
  • Limitations of Marketing Analytics

Assessment summary

45% individual coursework, 45% group coursework, 10% class participation

Current students should refer to Moodle for specific details of the current year’s assessment.

Past versions of this module

MSIN0094 18/19

MSING052 17/18

MSING052 16/17

Last updated Wednesday, 26 June 2019