Customer Life Cycle: Regression
CA1. Explain the output of summary(model) for the following command (see lecture slides) (2p)
model <- lm(data=faithful, eruptions ~ waiting)
summary(model)
CA2. Let’s use external resources for clv (3p):
1) Listen to the tutorial: Customer Lifetime Value (CLV) on R
2) download data from [https://investors.att.com/financial-reports/quarterly-earnings/2017] and calculate the same measures for 2Q-2017. Note that the file is called Financial and Operational Trends instead of Financial and Operational Results. What can you conclude based on new calculations? (Either use an existing code or replace it with dplyr code, for dplyr code + 0.5 bonus points).
CA3. Take any sample dataset (not necessarily big but except the Practice dataset used in the practice session in the class) (5p)
1) Perform some descriptive analysis (some graphs) (2p)
2) Perform Linear Regression (1.5p)
3) Perform Multiple Linear Regression (1.5p)
- customer transactions - clv_transactions.csv