Customer Experience Management (CEM)

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Create a unique, personalized, and meaningful
experience your customers deserve

Customer Experience Management

Exceptional customer care is about engaging your customer and consistently delivering a personalized experience. Predict your customer’s plans and target their needs.
NTELS Customer Experience Management interprets customer experience by collecting network data and applying it to a systematic statistical analysis. The resulting CE Index helps you gain insight into your customer’s mind through comprehensive, multi-angular analysis. Customer profile, usage history, and QoE score are visualized in an interactive report to enable you in improving customer care process, customer retention rate and churn management.

Customer Experience Management

Features

Real-Time QoE Data

Real-Time QoE Data

Real-time QoE scores based on traffic data collection/analysis per service (e.g., Web and streaming)
Intuitive Data Visualization

Intuitive Data Visualization

Comprehensive, at-a-glance reports for customer profile, QoE index, data usage history, and other various, complex information
Cross-selling and Upselling Opportunities

Cross-selling and Upselling Opportunities

Fast detection of changes in average revenues per user (ARPU), pricing plans, and usage history through systematic analysis to provide cross-selling and upselling opportunities
Prompt Response to Customer Requests

Prompt Response to Customer Requests

Proactive and prompt response to VoC to address customer concerns and requests through comprehensive analysis of network data
Radically Improved Customer Experience

Radically Improved Customer Experience

Far exceeded customer expectations by offering retention/promotion programs based on quality of experience index
Intelligent Network Value Chain Building

Intelligent Network Value Chain Building

Virtuous circle of customer care: real-time analysis of network data, network resource management to improve QoE, and customer retention management

Key Functions

Real-time Data Management

  • Real-time network data collection and management
  • Formatted/unformatted network data conversion and storage

Data Visualization

  • Visual reports for customer information and analysis results
  • Subscription data, service usage history, top-N query, estimated churn rate, and QoE score

Statistics & Analytics Algorithms

  • Various statistical and analytical techniques
  • Logic regression, decision tree, neural network, support vector machines (SVM), and heuristic evaluation

Churn Management

  • Customer retention programs based on analysis data
  • Churn prediction, device & plan promotion, cross-selling, and upselling

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