Digitalization of customer care in utilities: how BPO and AI improve SLA and customer satisfaction

In the utilities sector, the relationship between suppliers and end users has historically been characterized by high management complexity. Considerable contact volumes, unpredictable seasonal peaks, stringent regulations and heterogeneous demands require customer care infrastructures capable of ensuring business continuity and service quality. Digitization has therefore become a necessity to effectively manage the expectations of customers who are increasingly informed and less tolerant of inefficiencies.

The role of SLAs in customer care utilities

Service Level Agreements (or SLAs) are the contractual framework through which service quality parameters are defined in the relationship between utility provider and end customer. In the context of customer care, SLAs establish contractually agreed service conditions, specifying such aspects as uptime and speed of service response. The ability to consistently meet SLAs determines not only regulatory compliance, but also the reputational positioning of the company.

Traditional operational criticality

Traditional utility contact centers face complex structural challenges: management of fluctuating volumes with concentrated peaks (billing, service interruptions, commercial campaigns), need for vertical expertise on ARERA regulations, multi-commodity tariffs and specific technical processes. The manual approach inevitably results in variability in response quality, extended operator training time, and difficulties in maintaining uniform standards across H24 shifts.

Pressure on SLAs intensifies during critical events: network failures, weather emergencies, administrative deadlines. In these scenarios, the ability to rapidly scale resources while maintaining quality and efficiency becomes a discriminating factor between operators that can preserve the customer relationship and those destined to experience high churn.

Performance indicators in the utilities sector

In the deregulated utility market, where competition is primarily on the service level, indicators such as First Response Time (FRT), Average Handling Time (AHT) and First Call Resolution (FCR) become decisive metrics. A high FRT can translate into immediate dissatisfaction, while a low FCR generates operational inefficiencies and additional costs. Two-thirds of large Italian companies have increased budgets in 2024 for AI initiatives in customer relationship processes, confirming how optimizing service parameters has become a strategic priority.

Key indicators for evaluating the effectiveness of customer care in utilities include quantitative and qualitative parameters.
On the quantitative front:

• Average Speed of Answer (ASA),
• Percentage of calls handled within predefined thresholds(Service Level),
• Abandonment Rate (Abandonment Rate).

On the quality front:

• Customer Satisfaction Score(CSAT), or customer satisfaction score, indicates a customer’s level of satisfaction with a company’s products or services.
• Net Promoter Score(NPS), is the standard measure of customer loyalty and satisfaction.
• Quality Assurance Score is a systematic process that determines whether or not a product or service meets a specific set of requirements.

Theintegration of operational metrics and customer perception provides a comprehensive view of service effectiveness. A company may formally meet SLAs but record negative NPS if the quality of interactions is inadequate. Conversely, excellent response times accompanied by incomplete resolutions generate recontacts, negating apparent efficiency. Digitization enables real-time monitoring of these indicators, identifying correlations and triggering immediate corrective actions.

INGO’s solutions for customer care utilities

INGO has developed an operating model specifically for the utilities sector that integrates vertical industry expertise with advanced automation and artificial intelligence technologies. The combination of specialized teams and AI-driven systems ensures first- and second-level management with a one-call solution approach, dramatically reducing resolution time and improving regulatory compliance.

A consolidated experience in the multi-utility field enables it to manage the full range of customer touchpoints: activations, switchovers, complaints, network failures, delinquency management, technical assistance. H24 availability and dynamic sizing of resources based on traffic peaks ensure operational elasticity, a critical element for meeting SLAs even in the most challenging conditions. Knowledge-based services and AI-supported automation systems
are customized and updated in a timely manner, ensuring constant alignment with industry regulatory and tariff evolutions.

Intelligent automation for multichannel management

INGO’s automation solutions radically transform the efficiency of customer care utilities through tools designed to handle operational complexity and high volumes.

• Mail Assistant automates email management with immediate and accurate responses, particularly effective in handling recurring administrative requests (sending invoices, contract clarifications, regulatory communications).
• AI Reception handles the intelligent sorting of incoming calls, reducing wait times and directing requests to the most appropriate channel.
• Chatbots and voicebots use advanced algorithms and intelligent conversational patterns to personalize engagement and anticipate customer needs.

In the utilities context, these tools autonomously manage operations such as: communicating meter readings, checking practice status, managing technical appointments, and information on scheduled outages. Multilingual capability and 24/7 availability eliminate time and language barriers, a relevant factor in areas with significant international customer presence.

Customer Instant Insight and predictive analytics

The service Customer Instant Insight represents the evolution of data analysis applied to customer care. Through real-time processing of interactions, the system identifies behavioral patterns, customer sentiment, probability of churn, and up-selling opportunities. In the utilities sector, this predictive capability makes it possible to anticipate critical issues: identifying customers at risk of delinquency before escalation, detecting latent dissatisfaction before it translates into a formal complaint, identifying anomalies in consumption that could indicate faults or fraud.

Conversational analysis supported by natural language processing (NLP) extracts qualitative insights from interactions, providing operational intelligence to management teams. Sentiment analysis metrics correlated with performance indicators provide insight into not only whether SLAs are being met, but what impact they generate on service perceptions. This data-driven approach enables continuous optimizations based on hard evidence rather than subjective evaluations.

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Toward an intelligent hybrid model

The digitalization of customer care in utilities does not imply the total replacement of human intervention, but the evolution toward a hybrid model where AI and specialized operators collaborate synergistically. Automated systems effectively handle structured and repetitive requests, freeing up human resources to handle complex case histories that require empathy, negotiation skills and creative problem solving.

This balance ensures maximum return on technology investment while maintaining high quality standards. Operators, supported by automation and artificial intelligence, increase their solving effectiveness and reduce average handling times. The result is a service that systematically meets contractual SLAs, consistently improves customer satisfaction indicators and generates measurable economic efficiency.