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Customer Churn Management

Identify early signs of customer defection, and take action to retain them before they are lost.


In the financial services industry, customer expectations are rising, but customer confidence continues to fall. Losing a customer means losing an important asset, and the cost of reacquiring that asset is high.

Accenture has developed an application to help identify early signs of customer defection, so that businesses can take retention actions before the customer is lost. This predictive tool provides a contact list to the sales force, reducing the effort and increasing the rates of customers processed.

Specific Services

Accenture’s application takes into account all the information available about customers—transaction developments, cross-selling or product expiries—in order to catch initial signs of likely attrition and identify customers at risk.

For identified segments, predictive analytics models estimate the likelihood of the customer to churn, based on the most significant indicators and trends.

Key Features

  • Industry-specific, pre-defined attrition triggers
  • Unified churn definition
  • No black box. Business fully enabled.
  • Insight-driven to enable ease of change management
  • NBA focus - a way to standardize churn best practice
  • Standardized data-driven approach for measuring campaign effectiveness
  • Standardized data-driven approach for churn treatment (e.g. by segment, value)

Why Accenture

Our scalable Accenture Analytics Applications Platform has financial services applications built in, providing fast and easy access to an array of industry and functional applications that bolt on to the platform. In this way, clients experience quicker time to market and more rapid results.

Clients also access our wide range of capabilities rooted in:

  • Industry knowledge. The validity of our advanced analytics outcomes is underpinned by our deep knowledge of the sector.

  • Business. Applications are designed for business users and focused on business results, minimizing advanced analytics complexity. Getting to accurate outcomes does not require statistical, mathematical or IT knowledge—just business know-how.

  • Flexibility. Applications are based on a framework that can be easily integrated in an enterprise operational environment to drive business process action.