CPOs Are Still Building EV Charging Stations and Hoping Demand Will Follow. But is the Model Working?

Publisher's Note: Why we wrote this story?

Follow the money!

Charging point operators (CPOs) are continuing to build electric vehicle (EV) charging stations, banking on the idea that demand will eventually follow. However, with utilization rates stuck around 17%, it’s becoming clear that the current approach isn’t working as effectively as hoped. Many CPOs continue to rely on traditional data sources, such as traffic data, license plate scans, or third-party datasets, to determine the optimal locations for new stations. But there’s a fundamental problem with this method—this data tells you where people are, not where EV drivers actually want to charge their vehicles.

What’s Missing?

The issue lies in the type of data being used. Traffic data shows high-volume locations, but it doesn’t account for driver behavior and preferences when it comes to charging. What’s missing is the intent data—information about where drivers actually plan to stop and recharge during their trips. By integrating routing data, CPOs can gain a much clearer picture of where drivers are choosing to charge, which stations they are skipping, and where they are rerouting to competitors. This kind of data allows operators to see where drivers abandon trips due to insufficient coverage, providing valuable insights into how to improve their networks.

The Data is Lacking

By analyzing routing data, CPOs can begin to identify white spots—areas where charging infrastructure is lacking and demand is unmet. These gaps can be identified before stations start losing valuable sessions. The data can also help simulate various expansion scenarios, allowing operators to test and refine their plans for adding new stations before any physical infrastructure is built. The result is a more data-driven approach to network growth, based on actual driver behavior rather than assumptions based on external traffic metrics.

Using routing data also allows CPOs to see how drivers behave across different use cases and geographies. For example, a busy urban station may have different usage patterns than one in a rural area, and understanding these differences can help CPOs make more informed decisions about where to deploy charging stations. This behavioral insight is difficult to spot using traditional data sources, but once CPOs start analyzing intent data, they can no longer ignore the critical signals it provides.

This is particularly helpful for teams focused on network growth or partnerships. With intent data, operators no longer have to guess where demand will be—drivers themselves are essentially telling them where they want charging infrastructure. Instead of making decisions based on traffic data that may not reflect true demand, CPOs can make informed, strategic decisions about where to expand, when to add stations, and how to improve the overall user experience.

As the Industry Matures

As the EV industry continues to grow, this approach is likely to become more common among CPOs. By leveraging intent data, operators can shift from a reactive, guess-based model to a proactive one that is grounded in real-world driving behavior. This insight not only helps optimize station placement but also enables better partnerships and more efficient use of resources. The future of EV charging infrastructure will be shaped by how well CPOs can align their networks with the actual needs and preferences of EV drivers—making data-driven decisions essential for success.

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