Telco Customer Churn

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Customer churn is critical for all kind of companies. My project's aim is to show a solution for a telco company's customer churn modelling with h2o. The result (the scores) of the developed model could be used for campaign activities.

Content of files in the project folder

file name	description
data	contains the dataset downloaded from Kaggle
img	contains images used in README.md
Grid_GBM_py_2_sid_9f00_model_python_1585656568205_1021_model_1	final h2o model
README.md	README file
telco_customer_churn.ipynb	Jupyter notebook containing all the steps of the modelling process with small descriptions.
utils.py	A collection of functions used during the modelling process.

Dataset

The original dataset can be found on Kaggle.

Column description

variable name	description	possible values (if categorical)
customerId	customer identification
gender	Whether the customer is a male or a female.	Male, Female
seniorCitizen	Whether the customer is a senior citizen or not.	Yes, No
partner	Whether the customer has a partner or not.	Yes, No
dependents	Whether the customer has dependents or not.	Yes, No
customerLifetimeMonths	Number of months the customer has stayed with the company.
phoneService	Whether the customer has a phone service or not.	Yes, No
multipleLines	Whether the customer has multiple lines or not.	Yes, No, No phone service
internetService	Customer’s internet service provider.	DSL, Fiber optic,
onlineSecurity	Whether the customer has online security or not.	Yes, No, No internet service
onlineBackup	Whether the customer has online backup or not.	Yes, No, No internet service
deviceProtection	Whether the customer has device protection or not.	Yes, No, No internet service
techSupport	Whether the customer has tech support or not.	Yes, No, No internet service
streamingTV	Whether the customer has streaming TV or not.	Yes, No, No internet service
streamingMovies	Whether the customer has streaming movies or not.	Yes, No, No internet service
contract	The contract term of the customer.	Month-to-month, One year, Two year
paperlessBilling	Whether the customer has paperless billing or not.	Yes, No
paymentMethod	The customer’s payment method.	Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic)
paymentIsElectronic	Whether the customer's payment is electronic or not.	Yes, No
monthlyCharges	The amount charged to the customer monthly.
totalCharges	The total amount charged to the customer.
churn	Whether the customer churned or not	Yes, No

Results

AUC	Customer Nbr	Target Nbr	Cum Capture Rate TOP 10%	Cum Response Rate TOP 10%	Target Found TOP 10%	Cum Capture Rate TOP 20%	Cum Response Rate TOP 20%	Target Found TOP 20%
0.84	2093	550	0.29	0.75	159	0.50	0.65	275

If we want to use the best model from the developed for CRM activity, we can catch

• the 29% of all churning customers with selecting only the TOP 10% and
• the 50% with selecting the TOP 20%
  of the customer base according to the model's scores (for example for campaigns).

Variable importance

The most important variables based on the variable importance:

Package versions

Package                Version
---------------------- -------------------
h2o                    3.28.1.2
matplotlib             3.2.1
numpy                  1.18.1
pandas                 1.0.3
seaborn                0.10.1