IDENTIFYING THE CHALLENGES

As with most customer-centric service industries, mobile operators seek to build a successful and profitable service around three customer attributes: value, loyalty and satisfaction. Although maintaining a high level of customer satisfaction is the overall goal, it is very important to understand why a customer’s value and loyalty might suddenly drop or, most critically, why they might decide to leave.

The prevention of actions such as churn, complaints or diminishing spend requires a clear understanding of the complex, interrelated and evolving data that accompany these sudden changes. Customer Life Cycle Management is a complicated issue that can’t be solved in isolation by simple linear statistics, or fully accounted for by any individual characteristic such as customer satisfaction.

Currently, most network events that affect customer service (such as faults, outages, or reduced service quality) are first discovered by the mobile operator when the customer calls them. When this occurs, customer experience can be enhanced by a rapid and honest response. However, the goal must be to address these issues before the customer is even aware of them.

EXPLORING THE SOLUTIONS

The iCAN platform for intelligent customer analytics is a Java-based client/server software platform that incorporates a number of predictive approaches for customer life cycle modelling, including Hidden Markov Models (HMM) and Temporal Pattern Recognition. It can be used both to improve network performance for operators and as a diagnostic tool that enables them to serve their end users more effectively.

The Data Mining tool allows previously vague Customer Experience data to be turned into actionable performance improvements that directly affect customer satisfaction scores. It uses a wide range of network performance statistics and customer satisfaction data to predict the customers most likely to produce outages; those most likely to be affected by them; those most likely to churn; or those wanting to make a repeat purchase.

REAPING THE BENEFITS

• Complaint prediction
• Churn prediction
• Customer life cycle modelling for proactive service and intervention
• Modelling of customer surveys to guide strategic investments
• Monitoring of industry trends to identify best practice