MARKETING ANALYTICS
Syllabus
EN
IT
Learning Objectives
The objective of the “Marketing analytics” course, in line with the aim of the Master’s degree course in Business Administration, is to prepare students for an aware design and planning of marketing analytics actions and to verify their returns and value generation.
● Learning objectives: The aim of the course is an introduction to theories, models, qualitative and quantitative methods, techniques and specific indicators for measuring and evaluating marketing performance. The course also aims to:
○ Study and share, through comparison, the decision-making processes concerning marketing in a dynamic perspective, which contemplates both the phase of defining resources as well as the phase of verifying results by focusing on the relationship with the customer and the consequent outcome;
○ Experiment with the processes, methods and techniques through with marketing management influences company competitiveness and value creation;
○ Provide experience and stimulate discussion on case studies, company testimonials and projects in the field.
● Knowledge and understanding: it is expected to acquire, through study and use by means of exercises, basic knowledge of the methods and tools of marketing analysis.
● Ability to apply knowledge and understanding: it is expected to apply methods and tools described in the lecture, recognise areas of applicability, create variations on the proposed case studies and experiment in different areas of application.
● Autonomy of judgement: it is expected to motivate the choice of marketing analysis methods and tools applicable to the different contextual situations that may arise in the business environment. Knowing how to abstract in order to be able to acquire a correct analysis of the problems to be solved, knowing how to integrate information and weight it in order to be able to provide analyses that allow a vision of the problems that, from the general context, arrive at the particular one, knowing how to grasp the correlations.
● Communication skills: it is expected to be able to illustrate, in a technical manner, information relating to the design, process and results of marketing analysis in an analytical manner in the first instance and then in a synthetic manner. Also required is the ability to be able to highlight relevant points and to be able to grasp the flow of information/events in the description of a process.
● Learning skills: it is expected to develop autonomous learning skills in being able to read and understand technical descriptions, manuals, scientific publications of popularisation or research and related topics.
The training as a whole develops the ability to design, implement and manage the corporate information system for measuring and evaluating performance and marketing processes, each of which is characterised by specific performance measures.
● Learning objectives: The aim of the course is an introduction to theories, models, qualitative and quantitative methods, techniques and specific indicators for measuring and evaluating marketing performance. The course also aims to:
○ Study and share, through comparison, the decision-making processes concerning marketing in a dynamic perspective, which contemplates both the phase of defining resources as well as the phase of verifying results by focusing on the relationship with the customer and the consequent outcome;
○ Experiment with the processes, methods and techniques through with marketing management influences company competitiveness and value creation;
○ Provide experience and stimulate discussion on case studies, company testimonials and projects in the field.
● Knowledge and understanding: it is expected to acquire, through study and use by means of exercises, basic knowledge of the methods and tools of marketing analysis.
● Ability to apply knowledge and understanding: it is expected to apply methods and tools described in the lecture, recognise areas of applicability, create variations on the proposed case studies and experiment in different areas of application.
● Autonomy of judgement: it is expected to motivate the choice of marketing analysis methods and tools applicable to the different contextual situations that may arise in the business environment. Knowing how to abstract in order to be able to acquire a correct analysis of the problems to be solved, knowing how to integrate information and weight it in order to be able to provide analyses that allow a vision of the problems that, from the general context, arrive at the particular one, knowing how to grasp the correlations.
● Communication skills: it is expected to be able to illustrate, in a technical manner, information relating to the design, process and results of marketing analysis in an analytical manner in the first instance and then in a synthetic manner. Also required is the ability to be able to highlight relevant points and to be able to grasp the flow of information/events in the description of a process.
● Learning skills: it is expected to develop autonomous learning skills in being able to read and understand technical descriptions, manuals, scientific publications of popularisation or research and related topics.
The training as a whole develops the ability to design, implement and manage the corporate information system for measuring and evaluating performance and marketing processes, each of which is characterised by specific performance measures.
Prerequisites
To understand the contents of the lectures and to achieve the learning objectives, it is important for the student to have knowledge of marketing before the start of the training activity, in particular with regard to the concepts of market, product, product innovation, customer segmentation, and pricing methods.
Program
1. Marketing Intelligence: past, present and future of marketing analytics. From traditional market research techniques to modern data-driven strategies. Case studies on different marketing analytics approaches. 3h
2. How big data and advanced analytics modified decision-making processes, enabling businesses to forecast market evolution, and analyse and predict consumer behaviour. 3h
3. Technologies & Marketing Intelligence: Role of technologies in empowering data production. 2 h
4. Impact of artificial intelligence and machine learning on the automation and optimization of marketing strategies. 2h
5. Customer Segmentation: Mathematical and statistical methods. Cluster analysis, Stochastic modelling. 6h
6. Customer Lifetime Value (CLV) to understand each customer's long-term financial contribution to the business. Applying customer segmentation to successful marketing strategies. 3h
7. Customer Relationship Management CRM: its role in collecting and analysing customer data comprehension of consumer behaviour and in marketing actions. Advanced CRM analytics, random forest, decision trees, and survival analysis for churn prediction and prevention. 3h
8. How to improve service delivery, enhance customer satisfaction, and foster long-term loyalty. 2h
9. Marketing Modelling: predictive vs causal modelling in analytics. 2h
10. How forecasting and “cause and effect” relationships between variables can be applied to solve different marketing problems. 3h
11. Supervised and Unsupervised Machine Learning: How algorithms on labelled data make predictions. How unsupervised learning identifies patterns and relationships in unlabeled data. 3h
12. Case studies on customer segmentation and in the analysis of choice and purchasing behaviour. 4h
2. How big data and advanced analytics modified decision-making processes, enabling businesses to forecast market evolution, and analyse and predict consumer behaviour. 3h
3. Technologies & Marketing Intelligence: Role of technologies in empowering data production. 2 h
4. Impact of artificial intelligence and machine learning on the automation and optimization of marketing strategies. 2h
5. Customer Segmentation: Mathematical and statistical methods. Cluster analysis, Stochastic modelling. 6h
6. Customer Lifetime Value (CLV) to understand each customer's long-term financial contribution to the business. Applying customer segmentation to successful marketing strategies. 3h
7. Customer Relationship Management CRM: its role in collecting and analysing customer data comprehension of consumer behaviour and in marketing actions. Advanced CRM analytics, random forest, decision trees, and survival analysis for churn prediction and prevention. 3h
8. How to improve service delivery, enhance customer satisfaction, and foster long-term loyalty. 2h
9. Marketing Modelling: predictive vs causal modelling in analytics. 2h
10. How forecasting and “cause and effect” relationships between variables can be applied to solve different marketing problems. 3h
11. Supervised and Unsupervised Machine Learning: How algorithms on labelled data make predictions. How unsupervised learning identifies patterns and relationships in unlabeled data. 3h
12. Case studies on customer segmentation and in the analysis of choice and purchasing behaviour. 4h
Books
Slides, reading materials and coding will be made available from the teacher
Bibliography
Brea C., “Marketing and Sales Analytics”, Pearson; New Jersey, 2014
Rackley J. “Marketing Analytics Roadmap” Apress, 2015
Lilien G.L. Rangaswamy A. De Bruyn A. Marketing engineering and Analytics” 3dr Edition, Decisionpro, 2017
Mizik N. and Hanssens “Handbook of Marketing Analytics” Edward Elgar Publishing, 2018
Grigsby M. “Marketing Analytics” Page K 2017
Unpingco J. “Python Programming for Data Analysis”, Springer, 2022
Rackley J. “Marketing Analytics Roadmap” Apress, 2015
Lilien G.L. Rangaswamy A. De Bruyn A. Marketing engineering and Analytics” 3dr Edition, Decisionpro, 2017
Mizik N. and Hanssens “Handbook of Marketing Analytics” Edward Elgar Publishing, 2018
Grigsby M. “Marketing Analytics” Page K 2017
Unpingco J. “Python Programming for Data Analysis”, Springer, 2022
Teaching methods
Classroom lectures, flipped classroom, exercises, case study discussions, project work, 6 hours of practical laboratory exercises using the statistical software. Examples of applications using the Keix platform will be shown. Students can replicate the examples at home using the free Keix software from which they can directly use numerous datasets from ISTAT, EUROSTAT and other European governmental sources.