Investments in AI and ML have the potential for transforming customer satisfaction for telecom operators, says Marcus Taylor, Research Analyst at Analysys Mason, in a report.
Customer experience indexes (CEIs) are becoming an essential tool for telecommunications service providers (CSPs) to measure and enhance customer satisfaction.
Traditionally, CSPs have relied on retrospective metrics such as the Net Promoter Score (NPS) to gauge customer sentiment, as the computational effort required to generate CEIs for each individual customer was prohibitive.
However, advancements in artificial intelligence (AI) and machine learning (ML) have made real-time CEI implementation feasible, allowing CSPs to assess customer experiences dynamically. Leading vendors like Ericsson, Huawei, and Nokia are developing CEI models, signaling wider adoption across the telecom industry.
By leveraging CEIs, CSPs can proactively address network issues before they impact the customer experience. This preemptive approach not only enhances customer satisfaction but also reduces operational costs. Automating issue detection and resolution streamlines operations, benefiting both customers and CSPs alike.
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CEIs analyze numerous parameters that influence customer perceptions, such as network coverage, service quality, and pricing. With AI/ML-powered analytics, CSPs can identify which network performance issues have the most significant impact, enabling them to prioritize and resolve problems efficiently.
In the past, the cost and complexity of computational power, data storage, and algorithm training made widespread CEI adoption impractical. However, declining hardware costs and advancements in AI/ML algorithms have eliminated many of these barriers, making CEIs a viable solution for modern CSPs.
AI/ML enables CSPs to monitor all network data sources, including connections, devices, radio networks, and core networks, to generate a comprehensive view of service performance. ML models leverage adaptive algorithms that account for trends and variability, facilitating real-time monitoring and even predictive issue resolution. By identifying problems before they arise, CSPs can provide a seamless and consistent customer experience.
Major telecom vendors have already developed advanced CEI models. For example, Nokia’s AVA solution is actively used by CSPs such as Ooredoo, stc, and Swisscom. These models integrate AI algorithms with data from various customer touchpoints to predict service issues and make real-time adjustments. By analyzing customer feedback, service performance indicators, and device usage patterns, these solutions help CSPs deliver a superior customer experience.
Historically, CSPs have relied on NPS surveys to measure customer satisfaction. However, these surveys capture customer sentiment only after issues have occurred, offering a delayed and often incomplete view of customer experiences.
NPS also depends on customer self-reporting, whereas CEIs continuously track every aspect of service quality. This granular approach enables CSPs to pinpoint problem areas and make targeted improvements, which can, in turn, enhance NPS scores by addressing the most pressing concerns faced by customers.
Customer care agents often struggle to diagnose network issues due to a lack of real-time, subscriber-specific data. This blind spot leads to frustration for both customers and support teams. As telecom networks and services grow increasingly complex, effective network and service management is crucial for delivering high-quality experiences. By anticipating and resolving issues proactively, CEIs provide a solution that minimizes customer dissatisfaction and reduces operational inefficiencies.
AI/ML-driven CEI models also lower overhead costs by decreasing the volume of customer complaints and reducing the need for extensive customer support staffing. Automated problem resolution for widespread service issues eliminates the necessity for large call centers, allowing CSPs to allocate resources more efficiently. This shift from reactive customer service to proactive issue management represents a fundamental transformation in telecom customer care.
By adopting AI/ML-powered CEI models, CSPs can maintain service quality without relying on traditional customer service interventions. Connecting real-time network data with customer experience metrics enables CSPs to revolutionize their assurance processes, optimize network planning, and enhance customer support. This transition is critical for CSPs looking to remain competitive in a saturated market where service differentiation is minimal.
A well-executed CEI model fosters customer loyalty and satisfaction, ultimately reducing churn and strengthening retention. In markets with little variation between CSP offerings, such as the UK, superior customer experience can serve as a key differentiator. CSPs that prioritize exceptional customer care through AI-driven insights can establish a competitive advantage, driving long-term success in the evolving telecom landscape.
Baburajan Kizhakedath
