Big Data Marketing
| Code | School | Level | Credits | Semesters |
| BUSI3211 | Nottingham University Business School China | 3 | 10 | Autumn China |
- Code
- BUSI3211
- School
- Nottingham University Business School China
- Level
- 3
- Credits
- 10
- Semesters
- Autumn China
Summary
Consumers and business operations generate huge amounts of data, which creates opportunities as well as challenges for marketers. This module will introduce principles and practices associated with the use of data, and particularly ‘big data’, for data mining, sentiment analysis and predictive marketing. We will see how organisations gather data both internally and externally from internet sources including social media and how this is interpreted in the light of consumer behaviour to arrive at evidence-based marketing decisions. Other issues include machine learning, ethics and data security as these apply to marketing, and the use of software and programming tools.
Pre-Requisites: BUSI2131 Marketing Analytics
Target Students
Part 2 Business School students.Also available to exchange-in students with background equivalent to the pre-requisite requirements.
Classes
- One 1-hour-30-minute lecture each week for 11 weeks
- One 1-hour-30-minute laboratory each week for 6 weeks
Assessment
- 60% 3000 word group report.: 3000 word group report.
- 40% Individual coursework (1000 words): Individual coursework (1000 words)
Assessed by end of autumn semester
Educational Aims
This module extends and deepens the exploration of data-driven issues in the field of Marketing, with a particular focus on big data analytics and predictive marketing. It introduces the concepts, principles and techniques related to sentiment analysis and predictive analytics, aiming to help students understand the basics of applying sentiment analysis and predictive analytics for marketing issues.Learning Outcomes
Discuss the concepts and principles related to big data, sentiment analysis and predictive marketing.
Apply statistical and computational skills to support marketing decisions.
Collect, process and analyse consumer and market data to make informed decisions.
Employ machine learning techniques to solve marketing problems.
Apply critical thinking and analytical skills to interpret analytical results when addressing marketing issues.
Cooperate with team members to produce assessment output.
Conveners
- Dr Young Chang