Gaining Insight from Data
Written by EnergyX Solutions Inc. ・Toronto, Ontario
A great place to start is by taking stock of the data you have available. Although data synthesis and analysis are often required to gain deep and/or novel insight 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 neighbourhood, 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.
This is part of EnergyX Solutions’ 3-part-series on Data:
Gaining Insight from Data (this article)
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