This document summarizes an analysis of customer data from Santander Bank to predict customer churn using neural networks. The data set contains information on over 76,000 customers with 370 attributes. A neural network model was configured and trained to predict the likelihood of customers being dissatisfied based on the attributes. The model was then tested on additional customers to evaluate its performance and provide predictions of customer dissatisfaction risk to help the bank identify customers to contact.

1. CHURN PREVENTION WITH ADVANCED ANALYTICS
Sergio Sánchez
sergiosanchez@artelnics.com
linkedin.com/in/sergiosanchezjorge

2. Analyze hundreds of anonymized clients from Santander Bank to predict if a
customer is satisfied or dissatisfied with their banking experience.
OBJECTIVE

3. DATA SET
The bank database used here was taken from customers of Santander Bank.
It contains information about 76019 customers of a bank with 370 attributes.
This adds up to a total of 28.127.030 data.

4. Neural Designer is the most advanced software for advanced analytics.
NEURAL DESIGNER

5. DATA DISTRIBUTION
We conducted a general analysis of data.
The churn rate here is 4.0%.

6. Evaluate which variables can influence churn.
VARIABLES IMPORTANCE
The máximum correlation (0,15046) is yield between the variable num_var30
and the target variable.

7. Configure the neural network architecture. This model takes the attributes of
clients to predict their likelihood of being dissatisfied customers.
NEURAL NETWORK

8. LOSS INDEX
If the weighted squared error has a value of unity then the neural network is
predicting the data 'in the mean', while a value of zero means perfect prediction
of the data.
The error term is the weighted squared error. It weights the squared error
of negatives and positives values.

9. TESTS
The next step is to evaluate the performance of the trained neural network.
Confusion matrix
Binary classification
ROC curve

10. RESULTS
We apply the predictive model to 75819 potential customers.

11. The probability that client 1 is
dissatisfied is 27%: the bank will
not contact this customer.
The probability that client 4 is
dissatisfied is 61%: the bank will
contact this customer.
RESULTS

12. The predictive model can be easily integrated into the bank's computer system.
MODEL DEPLOYMENT

13. artelnics.com
Artificial Intelligence Techniques, SL
Carretera de Madrid 13
37900 Santa Marta de Tormes
Salamanca (Spain)
Telephone: +34 923 133 612 Ext.13
E-mail: artelnics@artelnics.com
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