In today’s data-driven world, utilities have access to a wealth of information that can provide valuable insights into customer behavior, energy consumption patterns, and the effectiveness of various programs. However, unlocking the full potential of this data goes beyond just collecting and storing it. By taking a close look at the datasets already available, utilities can uncover new perspectives, challenge existing assumptions, and identify opportunities for improvement. In this blog, we explore the key types of data utilities typically have access to—from household-level insights to broader community-level trends—and how these datasets can reveal actionable intelligence to optimize energy use, customer engagement, and program outcomes. Whether you’re tackling specific customer concerns or planning future initiatives, understanding and leveraging these data sources is essential for driving smarter, more efficient decision-making. 

Although data synthesis and analysis are often required to gain deep and/or novel insights from data, simply reviewing available datasets can uncover new understandings of established beliefs, practices, and results. Below, we’ve outlined some of the main types of data that utilities have access to along with a summary of common insights: 

Household/Individual-level data. This account-level data can, when reviewed over time, provide insight into actual usage trends. When paired with program participation data, household data can identify how changes in behavior or equipment investments result in saving energy, emissions, and money.  

This account-level data is commonly reviewed and disaggregated by utilities in order to provide insight into specific aspects of household consumption. These insights are helpful when working through issues with specific customers, such as high bill complaints, and can be used proactively to make behavior modification recommendations or forecast high bills.  In this way, account-level data can be used for individual targeting and engagement. 

Building data.  Building data is key to providing context to account-level usage data.  For example, a customer with seemingly high consumption can be viewed as relatively energy efficient if we understand that they live in an old, large, and leaky home.  Similar to an energy label for appliances, building data helps utilities to understand the ‘standard’ or ‘expected’ energy consumption of a building and then compare that to the customer’s actual usage.  As opposed to comparing customers to averages or historical consumption, incorporating building data allows for deeper and more accurate personalization when communicating with customers.  Building data is particularly useful when recommending structural building-level upgrades, like HVAC retrofits or solar on rooftops, and is also useful when targeting specific behavior modifications.  When used in aggregate, building data can inform potential studies and forecast measure program uptake and results. 

Community-level data.  This level of data captures information at the level of a neighborhood, zip or postal code, town, or even the entire universe of customers engaging with a specific platform. Demographic data is a common example of community-level data.  This level of insight allows an understanding of usage trends, program participation, and buying patterns. Community data is best overlaid with utility activities like marketing or against the results from a cold call outreach campaign, or a limited time offer. Not only does this level of data help us to understand trends and the efficacy of current activities, but it can also serve as benchmark data when developing future programs and projects. 

The wealth of data available to utilities offers a unique opportunity to gain deeper insights into customer behavior, energy usage patterns, and the effectiveness of various programs. By analyzing and leveraging household, building, and community-level data, utilities can move beyond assumptions and surface-level observations to uncover actionable intelligence that drives smarter decision-making. Whether addressing individual concerns or planning future initiatives, the key lies in using these data sources to inform strategies that optimize energy efficiency, enhance customer engagement, and improve overall program outcomes. In a world where data is abundant but often underutilized, understanding how to effectively harness these insights is crucial for utilities seeking to make informed, impactful choices that benefit both their customers and their operations.