Data Mining Consultancy for Axa Sigorta

Project Title 49

Data Mining Consultancy for Axa Sigorta

Name of legal entity

Country

Name of client

Origin of funding

Dates 

(Start-end)

Name of consortium 

members, if any

BYS Grup

Türkiye

Axa Sigorta

IDS

(Subcontracting)

14.12.2015- 14.12.2016

IDS

BYS Grup (Subcontractor)

Detailed description of project

Type and scope of services provided

This project is designed to enhance customer retention strategies through the development and implementation of a Churn Modeling system using data mining techniques. The primary objective is to analyze customer data structures and behaviors to predict and mitigate customer attrition risks effectively. The system will leverage advanced data mining algorithms to process and analyze vast amounts of data, identifying patterns that indicate potential churn. This will enable proactive measures to enhance customer engagement and retention. Using customer info and data, creation of different modelling algorithms and implementation of a winner model to increase customer loyalty, based on machine learning algorithms clustering customers to their churning rates.

Key components of the project include:

  • Data Structure Analysis: Examining the existing data architecture to ensure it supports efficient data mining and analytics.
  • Churn Modelling: Developing predictive models to identify customers at high risk of churning. This involves selecting and implementing suitable algorithms that can handle the complexity and volume of the data.
  • Algorithm Implementation and Management: Setting up and fine-tuning data mining algorithms that are tailored to the specific needs and dynamics of the customer base.
  • Technical Support and Monitoring: Providing ongoing support and updates to ensure the churn prediction system operates effectively and adapts to changes in customer behaviour and company strategies.

 
  • Development of Data Mining Processes:

Implementing robust data mining methodologies to track processes and analyze data structures, enabling detailed insights into customer behaviors and patterns.

  • Churn Model Development and Implementation:

Building and integrating churn prediction models using advanced analytical algorithms. This includes training the models with historical data to enhance their accuracy and reliability.

  • Technical Oversight and Algorithm Adjustment:

Continuously monitoring the performance of the algorithms, making necessary adjustments and updates to improve efficiency and effectiveness in real-time churn prediction.

  • System Integration and Deployment:

Integrating the churn modeling system with existing customer relationship management (CRM) and data warehousing systems to ensure seamless functionality and data synchronization.

  • Technical Support and Maintenance:

Providing ongoing technical support and maintenance to handle any issues that arise with the system, ensuring its continuous operation and optimization to reflect evolving market and customer dynamics.