Churn Prediction

Understanding and preventing churn rate to improve loyalty strategies.
Thematic area: Digital society, Projects
Financing: IFAB for PMI
Enabling Technology: Big Data Analytics, Machine Learning

The customer churn rate represents the ‘churn rate’, i.e. the percentage of customers who stop using a product or service over a given period of time. The project aims to understand and predict churn to improve retention and loyalty strategies.

Goals

Understanding and predicting customer churn rate to improve patterns and factors influencing churn.

The initial challenge

Explaining why Emil Banca’s customers stop using a product or service, then understand what to improve and how.

The solution

The project involves two approaches:

  • XGBoost with Master Data, using the machine learning algorithm trained on customer master data to identify patterns and factors influencing churn.
  • Survival Analysis with Lifelines, a time-based analysis that studies the temporal evolution of features up to the event (in this case, churn). It allows risk factors to be identified and the ‘probability of churn’ to be quantified over time.

Benefits

The expected outcome is to be able to compare and integrate the results of the two strategies to provide effective and informed recommendations on how Emil Banca can reduce customer churn.

Partners

For further information, please contact: barbara.vecchi@ifabfoundation.org

 

Sustainable Development Goals

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