UNIT SPECIFICATION Unit title . Level
Data Driven Marketing Level 6
Is this a common unit?
Credit value
20 (10 ECTS)
Yes
Expected contact hours for unit
40
Pre and co-requisites None Aims This unit aims to provide students with an advanced understanding of how various forms of data can be utilized to enhance marketing performance. Students will be able to gain stakeholder insights through online datateaching them how to do content analysis and how it can be incorporated from qualitative to quantitative. This will incorporate the ability to understand how sentiment is measured when looking at unstructured data on social media. From here students will begin to develop skills that will allow them to engage in predictive marketing. Finally, this course will introduce big data and large datasets and the ways in order to anlysze them and how to produce visualizations. The unit sets out to prepare students to become familiar with how they can use real world data insights in order to address challenges that marketers, public relations and advertising professionals will need. Intended learning outcomes (ILOs) Having completed this unit the student is expected to: 1. Demonstrate a working knowledge of how to analyse consumer generated data 2. Describe and apply the key concepts and theories about data integration 3. Demonstrate the ability to understand how to use big data and/or large data sets 4. Awareness of implications and issues about data 5. Research, identify and write about academically and professionally rigorous sources of information. Learning and teaching methods We will be using blended learning consisting of face-to-face and online activities. A weekly lecture will focus on problems and perspectives and will direct student activity. The seminars will be student-led and will normally have set reading and debates and presentations by students working in small groups to develop themes and issues, which emanate from the lecture programme and reading list. The seminars will be used for a class discussion of the major arguments presented to aid the development of a critical approach to the subject. Online activities could include panopto videos, blogs and wikis in the VLE. Assessment Formative assessment/feedback During the seminars you will have the opportunity to experiment with different techniques and theories, challenging their effectiveness alongside applying it to a real world issues concerning data collection, analysis and distribution. In lectures you will be expected to participate in discussions and there will be exercises connected to theory. You will be given support and feedback during these sessions, which should help you to build a solid understanding of the topic. Summative assessment Indicative assessment ILOs 1-5 will be assessed by 100% coursework The coursework will typically consists of two pieces: Assignment 1 is a 2,500 word individual essay that tests ILO’s 1, 2, 5 and is worth 50%. For example, the students critically analysis data on social media channels (so they can understand how different insights may impact marketing decisions). Assignment 2 is a group report worth the equivalency of 2,500 words person that tests ILO’s
3, 4, 5 and is worth 50%. For example, this could consist of analysing big data or data sets in order to provide recommendations. Indicative unit content 1. Defining and recognizing sources of data 2. Capturing, shorting and stemming data 3. Analysing sentiment and social networks 4. Predictive Modeling 5. Theoretical concepts/frameworks on data collection, storage, usage and delivery 6. Data integration: categories and sources of personal, behavioural, attitudinal, transactional and media data 7. Data models, essential data analytics and data mining to inform accountable decision-making 8. Data visualisation 9. Big data 10. Regulatory frameworks and its impact on data governance Indicative learning resources Borgatti, S. et al. 2018. Analyzing Social Networks. 2nd ed. London: Sage Field, A. 2017. Discovering Statistics Using IBM SPSS Software. 5th ed. London: Sage Neuendorf, K. 2017. The Content Analysis Guidebook. 2nd Ed London:Sage Struhl, S. 2015. Practical Text Analytics. London: Kogan Page Unit number
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Version number refer to AAM for practice within your Faculty
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unit specifications should provide a version number at the bottom of each unit. the system for versioning documents is as follows; ยง the unit should have a whole number in series following approval/review; ยง any units following modification should move to numbers (e.g. 1.1, 1.2); ยง after the first periodic review, the re-approved unit specification becomes version 2, then version 3 and so on; ยง any modified versions within the following periodic review cycles should have decimal numbers (e.g. 2.1, 2.2, 3.1, 3.2); ยง on a rare occasion, it may be necessary to introduce a third decimal number to differentiate between cohorts on different versions of the same unit.