ChargerHelp!’s Data Moat: Building the Largest Field Service Dataset in EV Charging
Understanding why technology fails is often more valuable than knowing how it works. In a recent episode of Category Visionaries, ChargerHelp! founder Kameale Terry revealed how collecting and analyzing failure data became their greatest competitive advantage in the EV charging infrastructure space.
The Technical Challenge
“Charging stations are computers,” Kameale explains. “For fast chargers, there are many different, what we call handshakes, which essentially interoperability of software that has to properly work in order for one charging event to happen.” This complexity creates multiple potential points of failure, making reliability management a significant challenge.
The scale of the problem is substantial. According to recent industry reports cited by Kameale, “they’re seeing between 30% to 40% of the current infrastructure being inoperable. And a lot of the issues is because of these failures of handshake.”
Building the Dataset
ChargerHelp!’s approach to this challenge was comprehensive. “At ChargerHelp!, what we believe is that you can get field service data, you can troubleshoot, but you can collect the steps that you did when you tried to fix a problem,” Kameale notes. “You could bring that problem into a system alongside other data sets, and you could start uncovering what’s happening when the stations don’t properly work.”
With 18,000 field service interactions across 17 states, ChargerHelp! has built what Kameale describes as “the largest set of field service interactions that there are in the US across multiple different manufacturers and network providers.”
From Data to Predictive Analytics
The real value isn’t just in collecting data, but in using it predictively. “More importantly, you can start predicting when you think an issue is going to happen. And then if an issue does occur, you could actually solve it a lot faster,” Kameale explains. This predictive capability transforms their offering from simple maintenance to what they call “reliability as a service.”
For B2B customers, particularly utilities and fleet operators, this predictive capability is crucial. ChargerHelp! can now offer “a fixed cost o and m [operations and maintenance]” solution, using their data advantage to minimize downtime and optimize maintenance schedules.
Strategic Market Evolution
The data advantage has allowed ChargerHelp! to evolve their market positioning. “Today, if you drive an electric vehicle and you need to go to a charging station, you typically use your electric vehicle, will tell you what’s the nearest available charging station,” Kameale explains. However, this information is often unreliable because “the station data is typically saying, oh, I’m available. But when that driver gets to that station, the station is actually broken.”
This insight has led to their next strategic evolution: becoming a verification system for the entire charging network. “We believe that if we get enough data, that we can be a verification system for those car oems,” Kameale notes.
Lessons for Technical Founders
ChargerHelp!’s approach to data collection and analysis offers valuable insights for founders building technical infrastructure:
- Field service data can be a powerful differentiator when collected systematically
- Understanding failure modes can be more valuable than focusing solely on operational data
- Cross-manufacturer data provides unique insights that individual manufacturers can’t access
- Predictive capabilities can transform service businesses into technology platforms
The key lesson? In infrastructure businesses, systematic collection of operational data can create powerful competitive moats. As Kameale demonstrates, understanding how and why systems fail at scale can be the foundation for building category-defining companies.