The Problem: Rapidly Scaling Site Selection
How can Volta increase scale quickly, while maintaining precision and quality in site selection?
The Problem Solver: Volta
Volta Charging partners with property owners and businesses to install electric vehicle charging stations in high-traffic areas like shopping centers and grocery stores. Volta simultaneously partners with sponsor brands to install advertising on their charging stations, allowing vehicles to be charged at no cost to the customers.
The Challenge: Precision Charger Placement
To build the smartest, free national electric vehicle charging network, Volta requires effective placement of its charging stations to maximize value to both advertising partners and retailers. Volta must ensure their chargers are placed in high-traffic locations near points of interest (POIs) whose visitor demographics are attractive to brand advertisers. Furthermore, Volta needs to prove the value of these charging stations to stores and property owners, establishing a clear ROI in converting traditional parking spots into EV charging stations, while anticipating demand in the near future.
To tackle this complicated challenge, Volta began by using a heuristic driven approach. As Volta’s operations scaled rapidly into new markets, they evolved towards a more sophisticated, data-driven site selection process, tying in first, second, and third party datasets to ultimately predict placement success.
In the process, Volta found that many proprietary data solutions suffered from a lack of precision and scope. Public transit data sources had their own problems, like different schemas and methodologies for different regions, making it impossible to adopt across the firm. The lack of precision in proprietary data, combined with the noise and obscurity in available public data sources and heavy-lifting of ETL, made it difficult for Volta’s data science team to derive valuable insights to present to their retail and advertising partners.
The Solution: SafeGraph Patterns Data & Snowflake
To overcome these industry challenges, Volta’s data science team evaluated multiple location data providers before choosing SafeGraph.
Volta chose SafeGraph data not only for its high precision and scope, but also due to the usability and cleanliness of the data when compared to other POI data providers. By integrating SafeGraph Patterns data, Volta was able to leverage the power of anonymized mobile location data. They analyzed dwell time metrics and visitation patterns to show retailers how the installation of an EV station outside their property led to a measurable increase in store visit durations.
SafeGraph's partnership with Snowflake also helped Volta rapidly scale its site selection strategy. With SafeGraph data accessible directly through Snowflake, updates become immediately available to Volta data scientists without time-consuming ETL processes. This shortened distance between data and analysis has freed up time for Volta to focus on their larger goal: growing their business. With the seamless nature of the integration, Volta has even been able to quickly expand their use case, creating things that they've always wanted to build, like a dynamic proximity engine, or things they had no idea they would need, like an internal pandemic impact dashboard.
SafeGraph’s precision-based places data was crucial in allowing us to scale our operations and harness a data-driven approach to expansion.
To solve the problem of picking the best store locations, Volta used SafeGraph data natively in Snowflake to analyze foot traffic counts, establish travel motifs, and extrapolate demographic profiles of the stores to determine the POIs that best fit their network, ultimately creating stronger partnerships with grocers, retailers, entertainment venues (and more) by proving the value of their charging stations through data-driven insights. Volta was able to maintain quality placements while undergoing rapid growth, strengthen its relationship with site partners, and ultimately provide the most charge per dollar of capital invested.