C Spire Wireless

Predictive analytics and decision models used to optimize cross-selling and prevent churn

Published on 01-Nov-2013

"We’re not only getting a more complete picture of our customers’ needs, we’re translating those insights into a higher-value customer experience." - Justin Croft, Manager of Brand Platforms and Analytics

Customer:
C Spire Wireless

Industry:
Telecommunications

Deployment country:
United States

Solution:
Big Data & Analytics, Big Data & Analytics: Customers, BA - Business Analytics, BA - Predictive Analytics, Smarter Planet

Overview

Based in Ridgeland, Mississippi, C Spire Wireless is the eighth-largest wireless provider in the United States, with approximately 900,000 customers in Mississippi; the Memphis Metropolitan Area; the Florida Panhandle; Rome, Georgia; and parts of Alabama such as Mobile.

Business need:
To outcompete the resource-rich wireless giants, C Spire Wireless needed to beat them at the small things that matter most: getting closer to customers and keeping them satisfied.

Solution:
C Spire Wireless is using predictive models to examine the complexity of its customers’ behavior and determine which service mix is optimal for each customer’s need, as well as the indicators of imminent churn.

Benefits:
Through improved risk detection and proactive intervention, the solution enabled C Spire Wireless to increase the effectiveness of its customer retention campaigns by 50 percent, while the ability to optimize offers based on customers’ account profiles led to an increase in the cross-sales of some accessories by 270 percent.

Case Study

Based in Ridgeland, Mississippi, C Spire Wireless is the eighth-largest wireless provider in the United States, with approximately 900,000 customers in Mississippi; the Memphis Metropolitan Area; the Florida Panhandle; Rome, Georgia; and parts of Alabama such as Mobile.

The Opportunity
To outcompete the resource-rich wireless giants, C Spire Wireless needed to beat them at the small things that matter most: getting closer to customers and keeping them satisfied. Its challenge was to convert what it knows about customers into actionable insights that help account reps craft the optimal offers that meet their needs and head off customer dissatisfaction.

What Makes It Smarter
C Spire Wireless is using predictive models to examine the complexity of its customers’ behavior and determine which service mix is optimal for each customer’s need, as well as the indicators of imminent churn. By embedding these insights into its customer-facing processes, C Spire Wireless has empowered its reps to optimize their interactions with customers.

Real Business Results
Through improved risk detection and proactive intervention, the solution enabled C Spire Wireless to increase the effectiveness of its customer retention campaigns by 50 percent, while the ability to optimize offers based on customers’ account profiles led to an increase in the cross-sales of some accessories by 270 percent. Along with the more personalized customer experience enabled by the solution, these capabilities increased satisfaction levels and strengthened the company’s relationships with its customers.


For More Information
Please contact your IBM representative or IBM Business Partner.
Visit us at ibm.com/spss.

To learn more about C Spire Wireless, visit www.cspire.com.

Products and services used

IBM products and services that were used in this case study.

Software:
SPSS Statistics, SPSS Modeler

Service:
IBM SPSS Lab Services

Legal Information

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