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Modern marketing is both challenged and empowered by the developments in technologies and the volume, variety and velocity of data presenting across an increasing number of channels and devices. In an era where data is a key corporate resource the need for analytically disposed and capable graduates is pressing. This course in a modest way is an attempt to address the deficit and demand in industry for marketing analysts and data scientists. The increased availability, ease of use and sophistication of data mining tools and technologies leaves that gap narrowed but graduates with enhanced analytical skills are needed. The intention and focus of the subject is the further development and integration of quantitative techniques as encountered in marketing science and management. The subject will draw from and develop subjects encountered in earlier years of the course and will provide an integrative framework and vehicle for their application to data analysis and decision making in the marketing context. The subject area will revolve around the careful presentation of a range of marketing/management decision contexts and some of the potential analysis approaches that can be meaningfully adopted and applied with a view to developing and applying insight. The application of software tools and the leveraging of data and marketing intelligence systems will be central to the theme of analysis for better marketing decisions.
The intention is to develop a graduate with both an enhanced analytical capacity as well as having a demonstrable experience and competence in the area of digital marketing and the attendant wider skills demanded - such as professional engagement, teamwork, industry relevance and engagement. To this end a major feature or the module is the matching of student groups to various SME’s in the task of engaging in digital marketing campaigns and analyses with the firms. If properly conducted and managed this should allow for the development of a portable and demonstrable skill set in terms of advanced analyses, practical competences, workplace immersion and thereby differentiate the better student from more generic marketing graduates. Module aim
It is intended that the student, by being exposed to a range of analytical contexts involving predictive modelling, Analytical CRM, multivariate data analyses and digital traffic analyses they will be better equipped to conduct analysis pertinent to modern marketing management. It will be the aim to develop this analytical capacity through a range of practical applications relevant to marketing which will lay down a logical route from data to decision; underpinned by a framework of examples and practical digital projects, where analysis initiatives and cogency in presentation will be encouraged, recognised and rewarded. The aims in terms of envisaged benefits include that of conferring:
- Enhanced familiarity and usage of data analysis tools. - Capacity and disposition for conducting analyses of market and survey data. - A distinguishing skill-set differentiated from more generic business courses
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| Module content:
The module shall address some of the tools and analytics pertaining to issues such as Analytical CRM, predictive modelling & classification and multivariate analyses relevant to the analysis of marketing research survey data. This material will be complemented by the immersion of the students in a digital marketing context through collaboration with SME’s in digital campaigns and strategies and will be a carefully mentored group work experience; conferring industry relevant experience and exposure to the student and leaving them well prepared to immediately assimilate themselves in prospective firms upon graduation.
Introduction: (self-directed reading elements) the volume, variety and velocity of data in modern marketing. The need and occasions for analysis in marketing. Analytical approaches and tools, metrics, dashboards, big data.
Analytical CRM: lead generation metrics & KPI’s, ROI, customer lifetime value calculations CLTV, churn analysis, RFM & transactional data, Market Basket Analysis software, support, confidence, lift etc conducting analyses, computing metrics and marketing implications.
Predictive Analytics & data mining: classification approaches- tree based approaches such as CHAID, CRT etc, logistic regression and neural networks – data examples and analyses – confusion matrices and performance metrics such as sensitivity, specificity, AUC etc. comparing models and methods. Customer Churn analysis – Survival analysis, Cox regression model, hazard and segmentation, marketing optimization of intervention budgets, software tools such as SPSS, BigMl machine learning, 11 ants etc. predicting and profiling churners, strategic marketing implications.
Multivariate methods & Survey Analysis: Cluster Analysis: Segmentation and profiling, two-step clustering. Case study and dissertation applications. Marketing implications.
Principal Component Analysis/Exploratory Factor Analysis: (Data reduction) identifying underlying components, interpreting & labelling factors, reliability & Cronbach alpha, further analyses and applications. Case study and dissertation applications.
Correspondence analysis: Analysis and visualization of large contingency tables, association analysis. Case study and dissertation applications.
Non-parametric tests: when to use them, assumptions. Kruskal-Wallis test, Friedman test, Wilcoxon test, Cochran & McNemar tests. Application to marketing and Likert type data. Review of tests of association, measures of association, comparing proportions etc Dissertation applications.
Google Analytics: A review & extension of the features and usages of GA; measurement plans, customized dashboards, goal conversion funnels, ecommerce metrics etc
Miscellaneous techniques: Time permitting; a look at one/some of the following:
Discrete Choice Modelling: Brand choice modelling, Multinomial logit model, and modelling probability of choice, choice-based segmentation, Case study and dissertation options.
Visualization: use of Tableau software tool for visualizing data.
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| The primary delivery vehicle for course content will be the lecture format in a lab-based or online setting; allowing for the usage of software tools such as SPSS, IBM Modeller, Tableau etc Students will have access to data sets and tutorial material relevant to the usage and application of the software.
Significant learning will also be undertaken through engagement and collaboration with chosen firms in the context of digital marketing and appropriate mentoring provided and a high degree of autonomy encouraged in their focus and learning goals or this element. Peer presentation and critique will also inform their learning in the digital project. . Well-defined decision contexts in marketing management are presented with the intention being to present and expect in return, meaningful usage of some available analysis techniques. The student will be encouraged individually and within a group structure to explore these contexts in terms of available analysis approaches and through task- based assignments generate managerially useful reports/insights.
An element of self-directed study will also be a strong feature of the course with directed/suggested usage of web and journal resources particularly in the area of digital marketing & practitioner oriented material relevant to the course.
Where possible it is further envisaged that certain case study modules may use the digital marketing firms utilized in the project as part of their module work A comprehensive catalogue of past projects, exam papers and their solutions will be presented to the students during the year. This pool of material should represent a useful learning resource for each subsequent cohort and accelerate the learning curve and provide a benchmark and driver for quality
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| Module Content & Assessment | |
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| Assessment Breakdown | % |
| Formal Examination | 50 |
| Other Assessment(s) | 50 |