Moving Beyond Billing to Real-Time Insights

Data is everything. If you can measure it, you can improve it. Yet, many providers still struggle to translate their data into actionable insights that drive customer engagement. For too long, energy communication has been passive—customers receive a bill at the end of the month and react accordingly. The industry must move beyond this outdated model and embrace real-time, personalized engagement.

Why Customers Don’t Engage with Energy

Energy is a "dissatisfier"—something customers only think about when it’s unavailable or expensive. The traditional bill isn’t enough to drive meaningful interaction. Most consumers don’t know what a kilowatt-hour means, let alone how their behaviors impact usage. To bridge this gap, energy providers need to shift from generic statements about energy conservation to data-driven insights that make consumption tangible.

People rarely notice energy until it’s missing—just like a toilet paper roll. No one actively thinks about it when it’s there, but the moment it’s gone, it becomes a priority.

This lack of attention to energy consumption is what makes proactive engagement essential. If customers are only noticing their energy use when there’s a problem, then utilities need to step in before that moment arrives.

For example, in a pilot with Austin Energy, a prepay program allowed customers to track their daily spending. One participant noted that it helped them manage their budget better—most days cost $2, but on laundry days, it jumped to $4. That simple insight empowered them to make small adjustments and avoid surprise bills.

Beyond the Bill: Making Energy Data Work for Customers

Leading energy providers are embracing behavioral engagement strategies to simplify communication. Weekly energy summaries, usage alerts, and targeted recommendations create a feedback loop that keeps customers aware of their consumption without overwhelming them. The challenge is striking the right balance—providing enough information to empower action without causing confusion.

Home Energy Reports, for example, have demonstrated a 1-2% energy savings by leveraging social norming—showing customers how their usage compares to similar households. A case study with Entergy New Orleans found that including a comparison metric in customer bills led to more engagement and ultimately a measurable reduction in usage. Additionally, this tactic helped their community solar program fill subscriptions faster than anticipated, with spots oversubscribed in less than three months.

AMI 2.0: Real-Time Insights for Proactive Management

Advanced metering is no longer just about collecting interval data—it’s about turning that data into actionable intelligence. Instead of yesterday’s data today, the focus should be on immediate, forward-looking insights. For example, predictive analytics can alert customers when their bill is trending higher than usual, helping them make adjustments before it’s too late.

At Madison Gas & Electric, a demand response program provided facility managers with near real-time energy data. One participating middle school, despite being relatively new construction, discovered inefficiencies that were costing them $20,000 a month. By leveraging data-driven insights, they optimized operations and redirected those funds to other educational needs.

Another example comes from Rhode Island, where state mandates require interval data to be available to customers within 45 minutes. Through collaboration with metering partners, this goal has become a reality, allowing customers to make immediate adjustments to their energy usage instead of waiting for end-of-month surprises.

AI and Automation: The Next Frontier in Customer Engagement

AI-driven automation is reshaping how energy providers engage customers. Predictive models can anticipate high usage patterns, while AI chatbots can provide instant answers to billing and usage questions—eliminating the need for costly call center interactions. Some providers are already experimenting with automated bill optimization, where customers receive recommendations on rate plans based on their historical usage patterns.

For example, Spark Energy implemented an AI-powered customer engagement platform that analyzes usage data and provides automated alerts. One of the most effective features has been high-bill warnings before statements are issued. Customers receive an estimate of their upcoming bill based on projected usage, weather forecasts, and past trends, allowing them to make changes in advance.

However, AI also presents risks. A major Texas energy retailer attempted to roll out an AI-powered chatbot for customer service but had to pull the plug within 24 hours due to fraud concerns. Bad actors quickly exploited the system, demonstrating that while AI can enhance engagement, security must be a priority.

What’s Next? Practical Steps for Energy Providers

To stay ahead, energy providers need to move from static engagement models to dynamic, real-time solutions. Some key priorities include:

• Proactive Alerts: Give customers a heads-up when their usage spikes rather than surprising them with a high bill. For instance, a peak-time rebate program in the Midwest rewarded customers with bill credits for reducing usage during critical hours, leading to a 15% participation rate and an average savings of $20 per summer.

• Customer-Defined Notifications: Let users set their own thresholds—whether it’s a daily cost limit or an unusual consumption pattern. Austin Energy’s prepay program allowed customers to set personalized notifications, leading to better budget control and fewer billing disputes.

• Integrated Insights: Make energy data easy to access and interpret, integrating it into mobile apps and customer portals. Otter Tail Power, for example, rolled out a platform that includes weather overlays, allowing customers to compare their historical usage with temperature trends—a crucial feature for those managing heating costs in extreme climates.

• AI-Powered Assistance: Use automation for high-impact areas like demand response notifications, rate plan recommendations, and real-time support. Lindsey Energy successfully implemented an AI-based chatbot, reducing the need for live agent calls by 30% while improving response time for simple inquiries.

Conclusion

The energy industry is at a turning point. With AMI 2.0 and AI-driven engagement, providers have the opportunity to transform how customers interact with their energy consumption. Those who lead the charge in real-time, proactive engagement will differentiate themselves in an increasingly competitive market.

The question isn’t whether utilities should embrace these innovations—it’s how quickly they can implement them to stay ahead of evolving customer expectations.