Summary

In this chapter, we gained an in-depth understanding of the factors affecting certain customer behaviors. We explored how regression analysis can help us understand the directional relationships between various factors and the outcome of customer behavior. Using our auto insurance marketing dataset as an example, we saw how to implement the statsmodels package in Python to run regression analysis and unveil the successes behind engagement rate marketing campaigns. We also discussed how decision trees can help us identify complex interactions that result in certain outcomes. Using a bank marketing dataset as an example and the scikit-learn package in Python, we successfully built a decision tree that unveiled the hidden interactions among various factors that lead to customer conversions. Lastly, with the bank churn dataset and the dowhy package in Python, we saw how causal analysis can bring deep insights into the root causes and directional contributions to the outcome of...