MARKETING INTELLIGENCE AND ANALYTICS

Marketing intelligence and Analytics is an elective course among the teachings in business economics and management.
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.

Students learn to address a typical marketing problem by designing a set of activities aimed at its solution through the use of data and algorithms.
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

Descriptive and Inferential statistics ; basics of econometrics and basic coding in R.

The course will cover the following topics:

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.

Slides, reading materials and coding will be available on Moodle.

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.

The exam is written and includes an exercise in R on a dataset. The instructor will evaluate 1) the ability to understand and conceptualise a marketing problem 2) the ability to apply machine learning and analytics techniques to a dataset using R.
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.

Theoretical lessons; case studies; exercises in RStudio

English

Ca’ Foscari University applies Italian Law (Law 17/1999; Law 170/2010) concerning the support services and accommodations available to students with disabilities or specific learning disorders. If you have a motor, visual, or hearing disability or any other disability (Law 17/1999) or a specific learning disorder (Law 170/2010) and require support (classroom assistance, technological aids for examinations, individualized exams, accessible format materials, note-taking assistance, specialized tutoring to support studying, interpreters, or other services), please contact the Disability and Specific Learning Disorders Office at disabilita@unive.it.

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