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EV Residential Charging Principle 2: Cast a Wide Net to Identify EV Drivers

By Erin D'Amato on

Woman Drives Car with Dog

Utilities need to know who is driving EVs to reach them with program offers.

This article is part of a series; Read Residential EV Charging Principle 1: Meet Customers Where They Are.

Understanding who owns an electric vehicle (EV) is critical for driving adoption of EV programs. Even if a utility does not have lofty EV program enrollment goals in the near-term, establishing a comprehensive view of EV drivers in their territory will mitigate the risk of low customer enrollments in the future as maximizing controllable EV load becomes more critical. 

It is not enough to simply know where an EV parks; utilities also need to understand the customer driving that EV to motivate the customer to take action through personalized offers–ultimately enabling them to reach their program adoption goals. This is not unique to utilities or to EVs; it reflects a society-wide expectation. According to McKinsey, 71% of consumers expect companies to provide personalized interactions, and 76% become frustrated when they don’t receive personalized experiences. 

Many utilities try to use state databases to understand EV adoption and identify EV drivers. However, few states have comprehensive datasets. Public initiatives exist, for example the Open Vehicle Registration Initiative by Atlas Public Policy tracks the adoption of electric vehicles by geographic region. But this data relies on state participation, which is low (15 states participating today), and the states’ ability to provide accurate and up-to-date information, which opens another set of challenges. 

So what EV datasets and solutions are available and correct? And what kind of decisions and actions can be derived from a 360 view of EV customers? Let’s dig into how Uplight helps utilities cast the widest net possible so they can identify and understand their EV customers. Below are a variety of EV customer detection methods that utilities can choose from or stack on top of each other to build out comprehensive EV customer insights. These identification methods are listed from basic to more sophisticated, and any combination of the following can be configured to help meet a utility’s goals. 

Stack EV Identification methods

EV customer identification methods

1. Self-identification Tools

By enabling quizzes and questionnaires through existing utility portal applications, such as Residential Online Assessments (ROA) or EV education portals, utilities can start to build EV customer lists. A western utility that utilizes Uplight’s ROA  had over 7,000 customers self-identify as EV or PHEV drivers without any additional marketing or promotion of the assessment. This customer list can be used to target customers as the utility develops EV programs such as EV rates and managed charging. 

2. EV Propensity

Even if a utility does not have AMI data or questionnaires, there are additional mechanisms for identifying EV drivers and serving them personalized offerings. Uplight has built a proprietary EV propensity model that leverages third-party data to segment customers based on demographic and socioeconomic factors. Our model is able to identify customers that may already be an EV owner or are a likely candidate to purchase an EV. 

We use this deeper understanding of customers and our ability to segment them based on EV journey indicators to create more targeted and effective EV program marketing. Currently Uplight’s EV Propensity model leverages 15 data points from third-party services, with 10+ data points on the roadmap to enhance accuracy. Using the EV Propensity model and more personalized marketing content, AES Indiana saw a 15% higher open rate and 2% higher click-through-rate on their EV program promotions. 

3. 360 View of Customer Program Participation 

Using various vendors creates challenges for EV Program managers and their marketing counterparts to have a holistic view of all programs a customer has already participated in, or understand what they may be a good candidate for. Having a one-stop-shop to view all of a customer’s program participation enables more effective conversions – from rebate to managed charging or behavioral nudges, because offers can be more personalized based on historical and current program engagement.

Uplight’s end-to-end EV solution manages all varieties of residential utility EV incentive programs,  creating an opportunity to consolidate EV customer data. Many utilities contract multiple point solution companies to run EV programs, which then creates vendor and data silos across incentive programs. For example, it is common to rely on one type of vendor to issue EVSE rebates, a separate vendor as an EV DERMS, and potentially a third and maybe a fourth vendor for providing rate coaching, EV demand response, and/or EV managed charging. 

Fragmented EV Customer Journey

Additionally, more systems to integrate means higher potential for customer drop-off throughout the signup experience for programs such as EV rates and managed charging. Having a consolidated data platform and experiences that stack incentive programs can drive higher adoption rates, such as the 76% conversion rates we see when EV charger rebates and DR pre-enrollment are bundled in Uplight’s Marketplace.

4. EV Detection using AMI Disaggregation 

Utilities can leverage AMI data disaggregation to detect EVs with high accuracy. They can be confident that their EV program marketing is reaching the right customers and make program offerings more targeted and personalized. Uplight is a leader in AMI disaggregation technology as our footprint spans 35 million homes and our robust detection models have been awarded 19 patents & trademarks. 

Our comprehensive approach to disaggregation is universal, meaning we can ingest data from smart meters, monthly-read meters, and simulation models. With AI models that have been trained and tested utilizing 31.3 billion data points and have 93%-99% accuracy, validated by NREL and Pecan Street, you can trust our ability to detect homes with electric vehicles and use that information to reach the right customers at the right time. 

 

Whether a utility has AMI data or not, it is important to start to build comprehensive EV customer profiles to not only help with current programs, but future enrollments and grid flexibility. Starting with state EV ownership databases makes sense, but there are many other methods for identifying EV drivers including self-identification tools, EV propensity modeling, a 360 view of customer program participation, and EV detection using AMI disaggregation. Building out this database today will help utilities future proof EV program adoption. 

 

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