讲解 MKTG 462 — Winter 2025 Customer Analytics辅导 数据结构程序

Customer Analytics

MKTG 462 — Winter 2025

Course Objectives

Customer Analytics explores how businesses can leverage data analytics to understand and market to individual customers effectively. In today’s data-driven environment, marketing is transforming from an art into a science. Many companies collect extensive information about consumer behaviors and reactions to marketing campaigns but lack the expertise to derive actionable insights. This course bridges that gap, teaching students how to approach marketing scientifically through the application of data analytics to customer-level data.

Students will gain hands-on experience with tools such as data, and analytics to collect, analyze, and act on customer information. While quantitative methods are central to the course, the primary goal is to develop competency in managing and interacting with marketing analytics teams rather than mastering advanced statistics. This foundational skill set enables students to take advantage of the potential of Big Data for impactful decision-making within the realm of marketing.

Learning Objectives

By the end of this course, students will be able to:

1. Understand the Analytics Process:

o Identify marketing challenges that can be addressed using data analytics.

o Select the appropriate analytical methods for specific marketing problems.

2. Conduct Analyses Using R:

o Apply statistical and machine learning methods in R to solve real-world marketing challenges.

o Work with datasets to perform. end-to-end analysis.

3. Interpret and Act on Analytical Results:

o Translate complex data insights into actionable marketing strategies.

o Support data-driven decision-making in areas like customer targeting, product recommendations, purchasing predictions, and customer retention.

4. Develop Practical Expertise:

o Gain hands-on experience with tools and techniques applicable immediately in a professional setting.

Course Format

The course employs a mix of lectures, readings and assignments to ensure comprehensive learning. Students will explore all stages of the analytics lifecycle:

· Identifying marketing problems suitable for analytics.

· Choosing the right analytical method for each problem.

· Conducting analyses using R.

· Interpreting findings and translating them into actionable marketing strategies.

Focus Areas

This course specifically emphasizes the use of individual-level customer data to:

· Target customers effectively.

· Recommend products tailored to their preferences.

· Predict purchasing patterns and behaviors.

· Develop strategies for long-term customer retention.

For students interested in other marketing analytics topics, such as:

· Automated product development,

· Algorithmic price optimization,

· Online advertising,

· Platform. design,

· Social media analytics,

we recommend additional courses in the Foster Marketing Analytics Specialization:

· Analytics for Marketing Decisions

· Digital Marketing Analytics

· Pricing Strategy and Analytics

Course Organization

The business challenges explored in this course often require us to predict individual customer behaviors. Examples include forecasting customer responses to specific offers, estimating how much a customer will enjoy a product, or identifying customers at risk of canceling their service.

To address these prediction problems, we will leverage a diverse set of modern analytical methods, including:

· Heuristic models: RFM (Recency, Frequency, Monetary) analysis

· Statistical models: Linear regression and logistic regression (logit) models, causality tools

· Machine learning & AI models: Neural networks, decision trees, and ensemble methods

· Algorithmic models: Recommendation systems

While the focus is on applying these methods within the domain of customer analytics, these tools are versatile and can be adapted to solve problems in a wide range of data-driven contexts.

Grading Policy and Assignments

The final grade in this course will be based on the following components:

In all elective courses at Foster, the distribution of final grades must satisfy school-wide requirements. Grades will be curved accordingly at the end of the quarter.

Assignments

Assignments are a crucial component of this course and are designed to enhance your learning through both individual and group work.

Assignment Guidelines

1. Individual Assignments:

o Must be completed independently.

o Collaboration with other students is not permitted, and your write-up should reflect your own work exclusively.

2. Group Assignments:

o Work in self-selected groups of 2–4 students.

o Only one write-up per group should be submitted.

o Groups should be formed at the beginning of the quarter and remain consistent throughout the course.

Skills Developed Through Assignments

This course emphasizes the dual skills necessary for fluency in analytics:

· Data Analysis: Using R to analyze customer data.

· Managerial Communication: Translating analytical insights into actionable recommendations for a managerial audience.

Assignments will require a combination of programming proficiency, statistical understanding, and strategic intuition to craft solutions and recommendations for real-world business challenges.

Peer Rating and Group Contributions

To ensure fairness and accountability in group assignments:

· At the end of the quarter, each student will submit a confidential Peer Rating Form evaluating their group members.

o Exceptional contributions (e.g., leadership roles) will enhance a student’s class participation and group contribution score.

o Insufficient contributions may negatively impact a student’s participation score.

Submission and Deadlines

· Assignments are due at the start of the class on the assigned due date.

· We will discuss each assignment in class on the day it is due.

· Late submissions will not be accepted, as timely discussions depend on everyone submitting their work on time.

Attendance and class participation:

Learning to articulate your analysis and to evaluate and respond to the analysis of others is an important part of what you will learn in this class. During the quarter, I will randomly do class participation activities. The nature of class participation would be based on three occasions where I ask students present in the class to form. a few teams and write their ideas to a class discussion subject and then we will discuss them in the class shortly. Each participation has a score of up to 3.5 points, where top two participations counts towards the 7% score for the discussion. In other words, it is ok if you miss one of the class participation activities. Thus, attendance is highly encouraged.