From Insights to Impact: 7 Ways Data is Shaping the Energy & Utility Sector

1. Next-generation Customer Support and Digital Solutions

Research from EY indicates that more than 50 percent of energy consumers prefer digital channels for eight out of 10 interactions with their providers, from monitoring consumption to paying bills.³ Digital-first, data-led solutions can meet these new expectations in several ways:

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  Resolving Billing and Payment Inquiries

  When customers encounter billing issues or unexpected charges, agents must have quick access to consumption trends, historical bills and billing accuracy metrics. This becomes crucial for assisting financially or physically vulnerable customers where due diligence in reviewing their financial situation can ensure uninterrupted service. Analytics-backed tools can provide agents with at-a-glance summaries of bills and relevant financial information, enabling them to resolve issues swiftly.

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  Creating Self-serve Solutions

  Developing effective self-service options – via chatbots, voice bots, websites or mobile apps – requires a deep understanding of customer issues and their complexity. By using data analytics to map customer inquiries by complexity level, utilities can implement resolution pathways for common issues. For instance, a leading UK energy provider reduced over four million voice contacts within a year by introducing a digital channel, streamlining customer interactions and enhancing operational efficiency.

2. CX Management

Recognizing the need for effective CX management, leading energy and utility organizations are prioritizing digital solutions that address evolving customer demands. Quality, contextualized data sits at the heart of these solutions, which blend human expertise with Artificial Intelligence (AI) prowess to create a holistic digital customer experience. Use cases include, but aren’t limited to:

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  Gauging Customer Sentiment with Interaction Analytics

  Speech and text analytics help gauge customer sentiment and provide insights into customer satisfaction. By leveraging tools that analyze Customer Satisfaction (CSAT) score and Net Promoter Score (NPS) using Natural Language Processing (NLP) models, companies can detect trends in customer sentiment, improve first-call resolution rates and monitor compliance. These insights drive more targeted improvements to customer service, reducing repeat inquiries and boosting customer loyalty.

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  Enhancing Responsiveness with Data

  With data-driven insights, companies can streamline issue resolution and improve customer interactions. Improved access to cross-departmental data minimizes the need for customer transfers or follow-ups, saving time and effort for customers and resources. It also enables proactive outreach, with customers who receive outage-related updates reporting satisfaction rates 62 points higher than those who don’t.⁴

3. Managing Debt and Credit Risk

Debt collection and receivables management in the utility sector are inherently complex, often requiring agents to navigate intricate account histories, understand the reasons behind overdue balances and offer tailored payment solutions. Through a human-digital blend, utilities can proactively identify vulnerable customers and offer contextual interactions, providing much-needed support where necessary. Key areas where data supports debt management include:

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  Identifying Root Causes and Offering Personalized Payment Solutions

  Using analytics to identify the underlying causes of debt allows companies to develop targeted solutions. For instance, if a large portion of overdue payments stems from billing inaccuracies, fixing the root issue can prevent future debt accumulation. Agents with data-driven tools can automatically assess the customer's payment capacity, recommend suitable payment plans and present settlement options.

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  Assessing Account Risk Assessment

  Data-driven risk assessment models can help utilities segment customers based on their likelihood of default. This approach enables proactive measures, such as personalized outreach and flexible payment plans, mitigating the overall risk of non-payment.

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  Optimizing Contact Prioritization and Channel Strategy

  With insights from propensity-to-pay models, utilities can define effective contact strategies, optimizing the timing and channels for collection outreach. Creating auto-pay models using advanced analytics can minimize the risk of missing payments. For instance, a leading US-based utility provider leveraged the propensity-to-pay model to reduce bad debt to the tune of USD 1.4 Million.

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  Managing Debt

  Periodic data quality checks, partnering with third-party data providers and undertaking data enrichment efforts can help firms avoid the risk of late-stage debts and write-offs. Insights on transaction patterns and customer behavior can help identify fraudulent activities and account manipulations such as suspicious billing anomalies, irregular payments or inconsistent consumption patterns.

4. Operational Performance Management

Operational performance management focuses on optimizing resources and ensuring cross-functional collaboration to streamline processes, reduce costs and achieve strategic goals. Data plays a pivotal role in enabling companies to react swiftly to daily challenges, maintaining operational excellence.

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  Real-time Performance Monitoring

  A well-designed data ecosystem enables organizations to monitor Key Performance Indicators (KPI) and maintain high service levels in real-time, ensuring quick adjustments in response to demand spikes, equipment malfunctions or grid disruptions.

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  Operational Efficiency

  Data visualization dashboards can provide holistic performance views by integrating data across departments – such as current demand, installations, field visits, customer service and tech support. Tracking demand fulfillment can help organizations optimize their total operating costs and make iterative tweaks where necessary.

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  Optimized Field Visit Costs

  Leveraging data insights on service appointments, field visits, cancellations and maintenance schedules for smart meters, home care products and charging points can help utilities optimize truck rolls. Advanced video and voice AI solutions further enhance efficiency by enabling users to record issues and receive immediate support, reducing unnecessary field visits.

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  Field Management

  Data-driven field management supports the efficient deployment of repair crews, workforce management and maintenance staff. For instance, real-time monitoring of grid conditions enables utilities to dispatch crews to locations with potential issues, reducing response times and improving service continuity.

5. Data-driven Scenario Planning for Resilience

Unifying data and creating a real-time view of organizational performance can also enable energy and utility companies to begin looking forward, developing foresight that builds resilience.

Doing so will prove integral, with risk on the rise: Research forecasts that by 2026, 25 percent of utilities will face grid disruptions due to cyber-physical attacks on distributed energy assets, impacting large-scale energy availability and service delivery.⁵ Scenario simulation and what-if analyses, powered by AI, can enable enhanced predictive capabilities in areas including:

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  Demand Response Management

  Data allows utilities to implement dynamic pricing strategies that adjust electricity usage in response to demand fluctuations. For example, incentivizing customers to reduce energy consumption during peak hours or providing variable intra-day tariffs can motivate consumers to shift usage.

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  Predictive Maintenance

  Data can predict equipment failures before they occur, reducing downtime and maintenance costs. Combined with sensors and IoT-enabled devices, it minimizes the need for reactive repairs, extending the life of critical infrastructure and improving overall system reliability.

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  Energy Forecasting

  By analyzing historical and real-time consumption data, utilities can make accurate energy forecasts, allowing for efficient resource allocation and minimizing reliance on costly backup sources. This approach reduces the risk of grid overload during high-demand periods.

6. Driving ESG Efforts for Net-Zero Goals

Data-driven strategies are essential for implementing sustainable practices. They enable energy providers to reduce their environmental impact while helping consumers contribute to global sustainability goals.

While benefiting the planet, such actions can also help the bottom line: Consumers are willing to spend an average of 9.7 percent more on sustainably produced or sourced goods, according to PwC.⁶ Moreover, McKinsey research shows products that promote themselves as Environmental, Social and Governance (ESG)-compliant products experience stronger sales than those that don’t.⁷ Here are some critical ways data supports ESG initiatives:

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  Energy-saving Devices and Smart Meter Analytics

  Smart meter data, combined with usage analytics, gives consumers personalized insights into their energy consumption patterns, enabling data-driven recommendations for reducing energy use. Based on this data, utilities can also provide personalized energy-saving tips or product recommendations, such as energy-efficient appliances, empowering customers to reduce their carbon footprint.

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  Retrofitting and Electric Vehicle (EV) Integration

  As the green energy revolution accelerates and companies motivate consumers to adopt solar and EV infrastructure, utilities can leverage data to identify and reach out to customers. Adapting to newer means of power generation can benefit consumers, companies and the environment.

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  Data-driven Renewable Integration

  As utilities shift toward renewable energy sources, data analytics can help organizations manage grid stability and balance supply and demand in real-time. For instance, weather data can predict solar and wind energy availability, allowing for more efficient renewable integration.

7. Delivering Revenue Growth through Data-driven Green Opportunities

Data analytics not only drives operational efficiencies but also opens up new revenue streams by enabling utilities to capitalize on new energy opportunities and cross-sell relevant products and services in the following ways:

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  Green Energy-based Offerings

  Data insights help utilities identify sustainability-focused customers willing to pay a premium for green energy. By offering tailored green packages, such as solar subscriptions or wind energy credits, utilities can attract eco-conscious customers, driving revenue while supporting ESG goals.

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  Cross-selling and Upselling Opportunities

  With detailed customer data, utilities can identify cross-selling opportunities for energy-efficient products, smart home devices and EV-related services. Utilities can also cross-sell renewable energy packages or EV charging solutions to customers purchasing electric vehicles.

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  Personalized Product and Service Recommendations

  By using data analytics, utilities can offer personalized product recommendations based on individual usage patterns, such as energy-efficient appliances, solar panel installations or battery storage systems. Personalized suggestions enhance customer satisfaction and drive revenue by aligning utility offerings with customer needs and preferences.