Comprehensive Location Intelligence Across the Globe

Background of FLO® and Their Use of dataplor’s Data

FLO, a leading electric vehicle (EV) charging network operating across North America, is dedicated to revolutionizing the way drivers access and utilize EV chargers. To achieve this, FLO is on a mission to build predictive models that anticipate EV charger utilization patterns, optimizing the availability and placement of chargers to meet demand. Julien Lebrun, FLO’s Network Planning Lead, and his team are refining their data strategy to support this goal. 

Why dataplor?

Julien and his team identified a critical challenge with FLO’s existing data source. Many Points of Interest (POIs) were either outdated or incorrectly placed. This inconsistency presented some difficulties, particularly in bilingual regions where both French and English are prevalent, as variations in language usage often led to discrepancies in data interpretation and categorization. Moreover, the FLO’s data was updated annually, only updated its data annually, which meant that some POIs had been closed for years, rendering the data almost unusable for a company that relies on precision and up-to-date information.

Having previous experience with dataplor (a leading provider of global location intelligence) from a former role where he conducted market analysis for geospatial technologies, Julien decided to explore dataplor’s offerings. His familiarity with dataplor’s capabilities, coupled with a pressing need for more comprehensive, accurate, and fresh data, led him to work with dataplor.

What Differentiated dataplor from the Previous Provider?

FLO’s decision to transition to dataplor was influenced by several key factors:

1. Data Coverage: dataplor offered a substantially larger dataset of points of interest (POIs), providing FLO with a more complete understanding of potential locations for EV chargers. In benchmark areas, dataplor delivered 65% more records, addressing FLO’s need for a more complete view of potential locations for EV chargers.This increased coverage enabled FLO to identify previously overlooked opportunities and make more informed decisions about charger placement.
2. Data Consistency and Accuracy: dataplor’s data was more consistent and accurate, particularly in bilingual contexts. This helped reduce data duplication and confusion, improving the reliability of FLO’s models and analyses.
3. Data Freshness: dataplor’s data was regularly updated, ensuring that FLO’s models and decisions were based on the most current information. This is crucial for a rapidly evolving market like electric vehicle charging.
4. Data Quality: dataplor’s data met higher quality standards, reducing the need for extensive filtering and cleaning. This improved the accuracy and relevance of the data, leading to more reliable models and operational strategies.
5. Licensing Flexibility: dataplor’s licensing model was more flexible, allowing FLO to scale their use of data across multiple teams and applications without additional costs. This provided FLO with greater flexibility and efficiency in their data-driven initiatives.

How FLO® Uses dataplor’s Data Internally

FLO leverages dataplor’s data in several innovative ways:

– Machine Learning Models: FLO integrates dataplor’s POI data to enhance its machine learning models that predict EV charger utilization. By incorporating rich attributes, FLO can better forecast where demand for EV chargers will be highest.

– Sales Team Development: FLO’s sales team uses dataplor’s categorized POI data to identify potential hosts for EV chargers. By generating targeted lists of addresses and businesses, the sales team can efficiently reach out to potential partners, ensuring that FLO’s chargers are strategically placed in high-demand locations.

– Strategic Planning: For FLO’s strategy team, dataplor’s data serves as a vital resource for enriching its EV charger network data. The team analyzes the distribution of EV chargers across North America and correlates this data with POI attributes to gain insights into optimal charger placements. For example, knowing how many chargers a business has in each state helps FLO better plan future installations and expansions.

Achievements with dataplor’s Data

FLO’s partnership with dataplor led to significant advancements in FLO’s EV charger deployment strategy. With access to comprehensive and accurate POI data, FLO not only improved its predictive models but also enhanced its ability to strategically place chargers in high-traffic, high-demand areas. This approach resulted in increased prediction accuracy and enabled FLO to better meet the needs of EV drivers across their operational regions.

Additionally, the enriched data allowed FLO to more effectively target potential partners and hosts for their chargers, expanding their network more efficiently and driving growth. By integrating dataplor’s data into their backend systems, FLO streamlined operations and reduced the time to market for new charger installations.

Future Plans for Innovation with dataplor

Looking forward, FLO expects to continue leveraging dataplor’s data. As they expand their network and optimize charger placements, the data provided by dataplor will likely play a crucial role in guiding strategic decisions. FLO plans to continue utilizing dataplor’s fresh data updates to refine their models and enhance their EV charger network further.

With a strong commitment to data quality and innovation, FLO is well-positioned to maintain its growth trajectory, utilizing dataplor’s invaluable contributions to drive informed decision-making and capitalize on market opportunities effectively.

By prioritizing data accuracy, comprehensiveness, and flexibility, FLO’s reliance on dataplor has transformed its approach to market development and expansion in the EV charging industry.

FLO® is a registered trademark of Services FLO Inc. 

Join us on October 17th at the SDSC Conference in New York, to hear from FLO’s Network Planning Lead, Julien Lebrun and dataplor’s Head of Real Estate Solutions, Christina Virosteck.