Topics in Business Applications of Data Analysis
- help students to appropriate the methods and concepts they have learned in data analysis, research methods, and other courses as applied in marketing
- be able to apply data analysis techniques in R in estimating the customer lifetime value, churn prevention, modelling time to reorder, and reducing data complexity
- Predictive analytics. Modelling Customer Lifetime Value with Linear RegressionBasic concepts of consumer behavior and client analytics. Classification of consumer behaviour models. Generic marketing strategies. Types of business models. Statistical methods for client analytics. Predicting client’s life-time value (LTV) with linear regression in R.
- Churn Prevention AnalysisHow to predict customer churn? How to detect and prevent customer churn? Push-pull-mooring paradigm for churn and service switching, measurement of latent variables: satisfaction and expectation disconfirmation. Models of satisfaction, expectation, performance, disconfirmation. Predicting client’s churn with logistic regression in R.
- Time to Reorder and Lifetime Values with Survival ModelsCPH model interpretation, calculation of customer lifetime value. Addressing churn using segmentation and advertisement. Description of task and data for the final project. Predicting time till next purchase with survival analysis in R.
- Reducing Dimensionality with PCAUnderstanding the way customers interact with websites, dimension reduction and segmentation, Markov chain models and Sequential association rules. Clickstream Analysis in client analytics. Applications of principal component analysis in customer relationship management (CRM) in R.
- MOOC resultsMOOC grade includes successful in-time completion of the four chapters of the online-course assigned by instructors (exercises and theory from all the chapters should be completed within the time assigned). Late completion is penalised.
- In-class activity
- Case-based project 1Project. Students create teams of 2-3 and work together on their projects. The first project focuses on reworking the programming code in R in order to calculate customer life-time value. Project details are available in LMS.
- Case-based project 2Project. Students create teams of 2-3 and work together on their projects. The second project is due towards the end of the course, and it reproduces the whole cycle of customer analytics, from data screening, to analysis, to creating a report targeted at the company’s management. The second project is reported as individual scripts of team members, so that individual contribution is transparent, and the final script that is meant for the client. Project details are available in LMS.
- Interim assessment (2 module)0.1 * Case-based project 1 + 0.3 * Case-based project 2 + 0.4 * In-class activity + 0.2 * MOOC results
- Chapman, C., & Feit, E. M. (2015). R for Marketing Research and Analytics. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=964737
- De Vries, A., & Meys, J. (2015). R For Dummies (Vol. 2nd edition). Hoboken, New Jersey: For Dummies. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1017482