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A Data-Driven Approach to Utility Customer Engagement

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As we look forward towards what 2019 has in store for utilities, there is a major customer-themed trend evolving, one that has been slowly growing over the past several years, that is now prepared to take off in 2019: utilities need to engage directly with their customers in a targeted fashion.

Customers (both residential consumers and businesses) have grown used to companies using data to understand their demands and personalize product offerings to meet their unique needs. They not only expect this behavior from their online shopping and their wireless carrier, but also from their utility.

2019 is poised for this significant data-driven trend for three main reasons:

 

  1. Utilities have started to see the downside of being stuck classifying customers with stale or static “personas,” which attempt to group all customers into a few generic descriptors. Traditionally, utilities have leveraged a fixed set of 8-10 personas that are used to segment customers into different groupings based on traditional demographic information. This approach works great when data is limited or too complex to utilize, but personas are static and can get stale. With the increase in data availability, customer segmentation approaches are becoming much more granular and dynamic – Amazon certainly doesn’t recommend a new product for you to purchase because your demographic fits some pre-set persona.
  2. Utilities are recognizing the inefficiencies of allowing their vast amounts of customer data go under-utilized. Marketing new products to customers is expensive, and it becomes twice as expensive when, for example, a new Demand Response (DR) or Energy Efficiency (EE) product – one meant to alleviate some grid constraint – doesn’t deliver the expected result. The same applies for the nascent but growing Distributed Energy Resource (DER) market and the variety of rate offerings utilities are beginning to put in front of customers. Utilities have begun to recognize that as they get ready to deploy new products and services, they can use propensity modeling to determine which products will have the best adoption, and, therefore, which products will best serve the needs of the grid. Additionally, as utilities prepare to deploy these products and services, they can save significant marketing costs by leveraging a data-driven approach to targeting only the residential consumers and businesses most likely to adopt the new products and services.
  3. Utilities are recognizing the customer satisfaction and increased adoption rates that result from a coordinated customer engagement approach. Historically, many utility products have had their marketing campaigns managed on a product by product basis. Demand Response product #1 finds its target customers and runs an advertising campaign separate from the marketing efforts of Demand Response product #2, and Energy Efficiency product #1…. This can lead to the same customer getting multiple different fliers for different products, all from their same utility – very confusing for the average residential customer and even for more sophisticated businesses. Utilities are recognizing that the same data-driven approach can be applied to a portfolio of products and services instead of just to individual product offerings. Using a data-science approach, utilities can create a customized package of offerings and present it to the customer in a coordinated and simplified fashion, resulting in increased customer satisfaction and increased product adoption.

 

By adopting a data-driven approach to customer engagement, some utilities have already been able to gain a 2x-4x increase in adoption rates and, most importantly, get the energy results needed for successful grid operations, whether that is localized Demand Response, higher adoption of Energy Efficiency, or increased penetration of Electric Vehicles supporting the roll out of a more robust utility EV Charger network. As an added benefit, they’ve been able to do this while lowering overall marketing costs!

Tom Martin's picture

Thank Tom for the Post!

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