MARKETING INTELLIGENCE AND ANALYTICS
- Anno accademico
- 2024/2025 Programmi anni precedenti
- Titolo corso in inglese
- MARKETING INTELLIGENCE AND ANALYTICS
- Codice insegnamento
- EM1423 (AF:471383 AR:258344)
- Modalità
- In presenza
- Crediti formativi universitari
- 6
- Livello laurea
- Laurea magistrale (DM270)
- Settore scientifico disciplinare
- SECS-P/08
- Periodo
- 3° Periodo
- Anno corso
- 2
- Sede
- VENEZIA
- Spazio Moodle
- Link allo spazio del corso
Inquadramento dell'insegnamento nel percorso del corso di studio
The module's objective is to understand the challenges of marketing intelligence by focusing on the application of data analytics to contemporary marketing practice. Lessons will match marketing theory with marketing problems and machine learning and statistical models. Students are actively involved in hands-on coding exercises to develop solutions using all the portfolio of data analytics techniques acquired in DABS teaching modules.
At the beginning of the module a company manager will offer a real marketing problem that students are requested to solve developing a business case.
Risultati di apprendimento attesi
Learning outcomes include:
- to recognise the most common problems in marketing intelligence;
- do identify the source and set of data related to the problem
- to chose the line of attack to the problem using appropriate methodologies
- to test and compare different modes and algorithms
- to offer a clear explanation and presentation also to non experts in data science
Prerequisiti
Contenuti
1. The Evolution of Marketing Intelligence
Explore the historical development of marketing intelligence, tracing its roots from early market research techniques to modern data-driven strategies. Understand how the advent of big data and advanced analytics has transformed decision-making processes, enabling businesses to predict market trends and consumer behavior more accurately.
2. Digital Technologies in Marketing
Examine the role of digital technologies in reshaping marketing practices. Focus on tools such as social media, mobile applications, and digital advertising platforms that facilitate real-time engagement with consumers. Learn about the impact of technologies like artificial intelligence and machine learning on automating and optimizing marketing strategies.
3. CRM and Consumer Behavior
Delve into Customer Relationship Management (CRM) systems and their critical role in understanding and influencing consumer behavior. Study how CRM tools help in collecting and analyzing customer data to improve service delivery, enhance customer satisfaction, and foster long-term loyalty.
4. Predictive Modeling vs. Causal Modeling
Differentiate between predictive and causal modeling in the context of marketing analytics. Predictive modeling focuses on forecasting future outcomes based on historical data patterns, while causal modeling aims to identify and quantify the cause-and-effect relationships between variables. Learn how each approach can be applied to solve different marketing problems.
5. Supervised vs. Unsupervised Machine Learning
Understand the distinctions between supervised and unsupervised machine learning techniques and their applications in marketing. Supervised learning involves training algorithms on labeled data to make predictions, whereas unsupervised learning identifies hidden patterns and relationships in unlabeled data. Explore case studies demonstrating their use in customer segmentation and behavior analysis.
6. Customer Segmentation, Marketing Campaigning, Attrition, and LCV
Investigate strategies for customer segmentation to tailor marketing efforts effectively. Study techniques for designing and executing marketing campaigns that target specific customer groups. Learn about identifying and mitigating customer attrition, and calculate Lifelong Customer Value (LCV) to understand the long-term financial contribution of each customer to the business. Analyze real-world examples to see how these concepts are applied in successful marketing strategies.
Testi di riferimento
For coding and exercises: Yildirim, G. and Kübler, R., 2023. Applied marketing analytics using R. SAGE Publications Limited (chapters 2-3-7-8)
Papers
1. Carlei, V. and Nuccio, M., 2014. Mapping industrial patterns in spatial agglomeration: A SOM approach to Italian industrial districts. Pattern Recognition Letters, 40, pp.1-10.
2. DeMartino, G.F., 2021. The specter of irreparable ignorance: counterfactuals and causality in economics. Review of Evolutionary Political Economy, pp.1-24.
3. Kitchin, R. "Big Data, new epistemologies and paradigm shifts." Big data & society 1, no. 1 (2014)
4. Nuccio, M., and Guerzoni M. "Big data: Hell or heaven? Digital platforms and market power in the data-driven economy." Competition & Change 23, no. 3 (2019): 312-328.
5. Pearl, J., 2018. Theoretical impediments to machine learning with seven sparks from the causal revolution. arXiv preprint arXiv:1801.04016.
6. Verbraken, T., Wouter V., and Bart Baesens. "A novel profit maximizing metric for measuring classification performance of customer churn prediction models." IEEE transactions on knowledge and data engineering 25, no. (2012): 961-973.
7. Wang, W., Feng, Y. and Dai, W., 2018. Topic analysis of online reviews for two competitive products using latent Dirichlet allocation. Electronic Commerce Research and Applications, 29, pp.142-156.
8. Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121.
Modalità di verifica dell'apprendimento
Students must prepare all the assigned texts and the materials provided by the instructor.
Extra points can be assigned to students that wish to give a class presentation based on exercises on real data.