Portfolio, Churn & Customer Value
Abstract
1
Introduction
1.1
How to define a customer
portfolio
?
1.2
What is
attrition
?
1.3
What does customer
value
mean?
2
Literature Review
2.1
On customer portfolio
2.2
On attrition
2.3
On customer value
2.3.1
Recency Frequency Monetary models
2.3.2
NBP-Pareto model
2.3.3
Econometric models
3
Duration models
3.1
Definition
3.2
Censoring and Truncation
3.2.1
Censoring mechanisms
3.2.2
Selection bias
3.3
Probabilistic concepts
3.3.1
Survival function
3.3.2
Hazard and Cumulative Hazard functions
3.4
Nonparametric models
3.4.1
Notations
3.4.2
Hazard function estimator
3.4.3
Kaplan-Meier estimator
3.4.4
Nelson-Aalen estimator
3.5
Parametric models
3.5.1
Constant hazard (exponential model)
3.5.2
Monotone hazard
3.5.3
Concave and convex hazard
3.6
Semi-parametric estimation
3.6.1
Proportional Hazards
models
3.6.2
Cox PH model
3.7
Machine Learning for Survival Data
3.7.1
Survival Trees
3.7.2
Random Survival Forests (RSF)
3.7.3
Cox Boosting
3.8
Performance metrics
3.8.1
Concordance index (C-index)
3.8.2
Brier score
4
Data Mining methods
4.1
Mutliple Correspondence Analysis (MCA)
4.1.1
Definition
4.1.2
Complete disjunctive table
4.1.3
Distances
4.1.4
Algorithm
4.2
Unsupervised classification
4.2.1
Hierarchical Clustering on Principal Components (HCPC)
4.2.2
Agglomerative Hierarchical Clustering (AHC)
4.2.3
The k-means algorithm
5
Data
5.1
General Overview
5.2
Churn_Value
and
Tenure_Months
Demographic data
Data on services subscribed
Customer account data
5.3
Churn, duration and price
6
Estimation techniques
6.1
Feature selection
6.2
Portfolio
segmentation
6.2.1
Transforming qualitative variables into principal axes
6.2.2
Hierarchical clustering on principal components
6.3
Churn analysis
6.3.1
The Cox model
6.3.2
Other survival models
6.4
Portfolio
value
estimation
6.4.1
The model
6.4.2
Customer Lifetime Raw Value
6.4.3
Cluster contribution to the
portfolio
value
6.4.4
Simulations
Conclusion
Appendix
Hazard function
Link between cumulative hazard and survivor functions
Contribution to the partial likelihood function in PH models
Partial likelihood function in PH models
Multiple correspondence analysis
Hierarchical clustering on principal components
Cluster visualisation
References
Get the source code on Github.
Portfolio, Churn & Customer Value
Hierarchical clustering on principal components
Cluster visualisation
Figure 6.23: Cluster visualisation onto MCA axes
