Arcadia

Aspen Power Partners, a developer of community solar projects, faced a significant challenge in the customer validation process and billing experience for subscribers. The process of developing a physical solar array, which can take two to three years, required sourcing enough future subscribers to make the project financially viable at launch. This process was labor-intensive and often involved in-person door-to-door sales. Furthermore, many projects proceeded with only a single data point for a subscriber, often collected at the point of sale. This led to a problematic scenario where a developer would attempt to convert their pledged subscribers into active customers only to find a significant percentage were inactive, in default, or had moved to another utility.

The retail energy market is notorious for its complex rate plans, hidden fees, and teaser variable rate hikes. This complexity often leads to consumers paying more than necessary for their energy needs. Additionally, the outdated and archaic platforms used by many energy providers make it difficult for consumers to make changes to their plans efficiently online. The process of shopping around for different energy options can be frustrating and time-consuming. Consumers often find it challenging to find the best rate, opt into clean energy, or even make any changes at all. The rise in energy costs nationwide during the pandemic further exacerbated these challenges.

Ford, a leading automobile manufacturer, faced a challenge when they began selling electric vehicles (EVs). They realized that new EV owners would need support beyond the dealership, particularly in understanding the home charging experience. Questions such as the cheapest time of day to charge an EV were expected to arise, and Ford wanted to help their drivers navigate this new experience. The company had an existing database of electricity tariffs, but it was difficult to maintain and was not comprehensive enough. This led Ford to seek an external partner to provide the necessary data to enable new charging features.

EVgo, the largest public fast-charging network for electric vehicles in the US, was facing a significant challenge in managing the accuracy of hundreds of utility bills received monthly from various locations across the country. The complexity of understanding local rate structures and tracking bill accuracy was overwhelming. The manual process of sorting through billing details was not only time-consuming but also prone to errors. EVgo needed a digital and accurate utility rate database that could handle complex factors such as energy usage, peak demand, monthly fixed rates, season, time of use, hours, and territories. This information was crucial for checking the accuracy of utility bills, optimizing utility tariffs, and making informed decisions about future projects. The existing databases were outdated, forcing EVgo staff to manually check each utility website for accurate data, a process that was unsustainable.

Station A, a predictive platform and clean energy marketplace, was facing a significant challenge in promoting the adoption of sustainable energy. The problem was twofold. Firstly, potential customers were often overwhelmed by the complexity of determining the financial viability of sustainable energy solutions, leading to a state of 'analysis paralysis.' Secondly, Station A needed access to large-scale energy data and specific electricity data at the building level to accurately model savings for property owners. The lack of such data was hindering their ability to effectively engage with potential customers and demonstrate the financial and environmental benefits of transitioning to cleaner energy solutions.

Stem, an energy technology and services company, was grappling with the challenge of providing accurate tariff data to its customers. The company needed to optimize cost across multiple types of utility tariffs, a process that involved balancing trade-offs between different hourly energy costs on the same tariff. Stem's team had been manually analyzing this data using spreadsheets, a process that required updating about 120 tariffs with some 60 utility companies, taking up to eight business days. Since utilities have different schedules for updating their rates, Stem had to stay on top of all these updates throughout the year, a task that consumed significant staff time and attention. The analysis process involved multiple teams and had to be redone if any mistakes were made, further adding to the complexity and time consumption.