Advanced Customer Analytics (MSB209)

Content

Technologies, such as cloud, artificial intelligence (e.g., chatbots), social media (social commerce), and internet of things (e.g., wearables), generate enormous amounts of information about the customer. This information is collected and analyzed along with historical data to personalize the 4Ps of marketing (Product, Price, Promotion, Place) down to the individual customer level.

Large amounts of fine-grained customer data enable marketers to adopt a data-driven approach towards managing customer relationships (customer acquisition, growth, and retention), and managing the customer’s journey from the pre-purchase (awareness phase) to purchase and finally the post purchase (advocacy phase).

This course aims to introduce students to the fundamentals of managing customer relationships, expose students to novel quantitative approaches (such as text analytics, artificial intelligence (AI) methods) that can be applied towards managing customer relationships, drawing customer insights, and managing the customer journey, and also discuss the implications (pros and cons) of new technologies such as AI on the customer journey and customer experience.

Learning outcome

Knowledge

Upon completion of the course, students will have knowledge of:

• Fundamentals of customer relationship management (CRM) strategy.
• Customer decision making process (and the customer journey).
• Data-driven approaches (such as AI methods, text analytics) applied to managing customer relationships, drawing customer insights, and managing the customer journey.
• Implications of new technologies such as AI on the customer journey and customer experience (e.g. customer privacy implications, etc)

Skills

• Explain fundamentals of customer relationship management strategy
• Explain the customer decision making process (and the customer journey)
• Analyze customer transaction/textual data and apply AI methods and text analytics to support marketing decisions related to CRM/customer experience.
• Discuss the implications of new technologies such as AI on the customer journey and customer experience

Required prerequisite knowledge

Exam

Coursework requirements

The following are mandatory course requirements:

• Class participation (70%)
• Lab exercises
• Presentations

In order to take the exams, students must pass all coursework requirements.

70% attendance at all mandatory sessions starting from week 1 of teaching.

Course teacher(s)

Course coordinator:

Course teacher:

Method of work

Lectures and tutorials, labs, group work, and independent study. The estimated distribution of ECTS hours are as follows:

1. Lectures and tutorials: 50 hours

2. Group work and labs: 110 hours

3. Independent study of course material 120 hours

Open for

Course assessment

Literature