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 Many Points of Interest (POIs) were either outdated or incorrectly placed.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.

Wolt’s Geospatial Evolution with dataplor

Wolt, a leading food delivery and technology company spanning 27 countries, initiated a transformative journey to enhance its market presence and identify untapped areas of opportunity. This case study explores why Wolt chose to partner with dataplor over other providers, the problems they sought to solve, and the remarkable accomplishments they’ve achieved through this partnership. Additionally, we delve into their future plans for innovation with dataplor.

Why dataplor?

Wolt’s decision to partner with dataplor was driven by their commitment to data coverage. They prioritized crucial factors such as the breadth of coverage, data accuracy, attributes and most importantly, data quality. dataplor surfaced as the top selection due to its ability to capture a vast number of Points of Interest (POIs) accurately and comprehensively. Wolt underwent an extensive evaluation process, exploring various data vendors. However, dataplor’s unmatched coverage and quality positioned it as the undeniable winner.

The Problems to Solve

Wolt aimed to leverage the data to enhance its market understanding and improve internal operations. They sought to evaluate data beyond their existing data sources to gain a more comprehensive view of merchants across various sectors, not just limited to restaurants. With dataplor, Wolt could address market gaps and identify untapped opportunities more effectively.

How Wolt Uses the Data Internally

Internally, Wolt extensively utilizes dataplor’s data to strengthen its competitive positioning and identify new opportunities. They integrate this data into their internal systems, serving it to Customer Relationship Management (CRM) platforms and ingesting it into various geospatial platforms. Wolt emphasizes the importance of data quality, recognizing it as the foundation for their operations. Centralizing processes enables Wolt to allocate resources efficiently, prioritize leads based on internal valuation, and enhance the accuracy of their predictive models.

Achievements with dataplor Data

Wolt’s collaboration with dataplor has yielded significant achievements. By leveraging dataplor’s comprehensive POI data, Wolt has improved its ability to serve customers and accelerate its growth in various markets. The integration of dataplor’s data has expanded Wolt’s market by nearly 40% in certain markets surpassing their initial projections. Wolt now possesses a more accurate and detailed understanding of merchants within their countries, enabling them to make informed decisions and capitalize on market opportunities effectively.

Background of Wolt’s Geospatial Focus

Leading Wolt’s geospatial initiatives, Sharat Ramamani brings a diverse background in strategy and analytics to the table. His journey into the geospatial realm began at Doordash, where he focused on new country expansion and selection. Following Doordash’s acquisition of Wolt, Sharat shifted his focus primarily to Wolt’s selection strategy across their 25 markets. Despite the challenges of navigating the early geospatial SaaS landscape and the initial learning curve, Sharat found the transition fruitful. Leveraging geospatial data has accelerated Wolt’s decision-making processes and accelerated their time to market.

Wolt’s strategic partnership with dataplor has transformed its approach to market development and expansion. By prioritizing data quality, accuracy, and innovation, Wolt is poised to continue its trajectory of growth and success, leveraging dataplor’s invaluable contributions to drive informed decision-making and capitalize on market opportunities effectively.

Join us on May 16th at the SDSC Conference in London, to hear from Wolt’s Analytics Lead, Sharat Ramamani and dataplor’s Enterprise Account Executive, Meagan Vigil firsthand.

Tensorflight, a cutting-edge technology company specializing in property analytics for the insurance sector, embarked on a transformative journey to enhance the quality and accuracy of data. Their mission was to empower property insurance companies with the tools to refine their insurance targeting and associated premiums. This case study explores why Tensorflight chose dataplor over other providers, the problems they sought to solve, and the remarkable accomplishments they’ve achieved through this partnership. Additionally, we delve into their future plans for innovation with dataplor.

Why dataplor?

Tensorflight’s rigorous assessment process involved evaluating multiple Points of Interest (POI) providers, all offering places data. They focused on critical factors like coverage breadth, address accuracy, and alignment with insurance-specific attributes. dataplor emerged as the standout choice. Although confidentiality commitments prevent Tensorflight from naming other providers, they assessed over five options, with dataplor distinguishing itself as the prime choice.

The Problem to Solve

Tensorflight sought to enhance the quality and accuracy of data for property insurance companies. Their objective was to enable insurers to determine which buildings were covered by policies, provide detailed attributes such as Building Occupancy Type and Tenant Type, and estimate building replacement costs accurately. They also aimed to merge dataplor’s data with visual and other datasets to optimize classification.

Internal Data Utilization

Tensorflight integrated dataplor’s data extensively into their internal operations. This data resides in their internal data warehouse, serving as a vital resource for analytics and reporting. It fuels proprietary models, including geocoding, building feature identification, replacement cost estimation, and survivability score computation.

Achievements with dataplor Data

Tensorflight’s collaboration with dataplor yielded substantial improvements across various metrics. They observed increased geocoding accuracy, enhanced precision in building replacement cost estimations, and improved accuracy in determining building occupancy types. The refined assessments related to multi-tenant attributes and tenant attributes have proven invaluable to their insurance clientele.

Future Innovations with dataplor

Tensorflight’s future plans for innovation revolve around extensive integration of dataplor’s insights. They are developing new attributes, such as building survivability scores, which are significantly informed by dataplor’s data. The overarching goal is to create a fully-automated AI system capable of assessing and pricing insurance risks, thereby streamlining the tasks of insurance underwriters. dataplor’s data, particularly information about businesses located within properties, plays a pivotal role in achieving this vision.

In conclusion, Tensorflight’s strategic partnership with dataplor has catalyzed remarkable advancements in the property insurance sector. With a commitment to data quality, accuracy, and innovation, Tensorflight is poised to transform the industry, making insurance underwriting more efficient and precise, thanks to dataplor’s invaluable contributions

Yeme Tech’s Community Platform is a powerful application that utilizes spatially mapped Human, Asset, and Activity data to provide profound local insights into communities. By leveraging this information, Yeme Tech empowers developers, community stakeholders, and others to take action toward creating positive social impact through enhanced interaction, engagement, and cohesion. Yeme Tech’s platform not only provides valuable information about the places around us, but also facilitates new ideas to improve our communities.

Yeme Tech relies on having the most accurate geospatial data to support its platform’s functionality and users. Prior to using dataplor for its location intelligence needs, the company had relied on open-source data and Google Places for their Point Of Interest (POI) data. However, these sources proved unreliable and costly, making it difficult to analyze the data and grow the platform into new areas.

The Challenges with Google Places and Open-Sourced POI Data

Yeme Tech faced several challenges with the open-source data they were using previously. The data was unstructured, decentralized, and complicated, requiring a lot of legwork to compile and analyze. Additionally, there was a considerable margin of error associated with validation and replicability for other projects and locations. This made it nearly impossible for Yeme Tech to provide their customers with accurate analysis and develop its community enhancement platform.

Yeme Tech then turned to Google Places as an alternative data source. However, this approach was not scalable due to the very high and unpredictable costs. The updates provided by Google Places were not recurring which made it difficult to grow into new areas. This resulted in Yeme Tech spending a considerable amount of time scraping multiple sources and compiling data in a usable format.

‍

The Solution: dataplor’s POI Data

To overcome these challenges, Yeme Tech needed a data provider that could offer accurate, comprehensive, and up-to-date global data to support their mission of creating walkable and sustainable cities. After extensive research and trials, Yeme Tech chose dataplor as its data provider.

dataplor’s expertise and support were one of the key factors in Yeme Tech’s decision to choose them as their data provider. dataplor’s proven strategies for collecting and verifying data gave Yeme Tech confidence that they could continue developing their community enhancement platform with reliable and up-to-date information. Additionally, dataplor’s breadth and depth of data allowed Yeme Tech to plan for future scalability.

‍

‍

The Benefits of dataplor’s POI Data

Using dataplor’s POI data, Yeme Tech has been able to integrate valuable insights into its platform. dataplor’s POI data has allowed Yeme Tech to advance their work of creating a 15-minute Walkable Fulfillment benchmarking tool. They have also used dataplor’s POI data in a series of consultancy projects, leading to transformational insights related to community engagement consultation analysis and business emissions, among others. 

Alejandro Quinto, Head of Innovation at Yeme Tech described their experience with dataplor stating “In creating a profound new Community Enhancement Tool, we recognised the importance of accurate, place-based asset data to our entire proposition. The quality, detail and format of this was critical to achieving our objective of a globally significant and market-leading platform. It has been fantastic working with dataplor as their culture of exploration led to a co-creation approach being developed. Their expertise and resources allowed us to be able to create valuable metrics in order to gather valuable insights and make informed decisions based on accurate and up-to-date information. Their commitment to quality and support has been essential in ensuring the success of our data-driven proposition.”

‍

One of the standout features of dataplor’s POI data is the asset categorization that identifies the different attributes of the POIs, enabling Yeme Tech to sort through the data easily. Additionally, Yeme Tech appreciates flexible approach to licensing.

‍

Looking Forward

Yeme Tech is a leader in community enhancement for cities and governments, and dataplor is excited to support their mission to expand into new areas throughout the globe. Yeme Tech plans to continue using dataplor to support their upcoming initiatives for developing a comprehensive and standardized social benchmarking tool capable of empowering citizens and businesses to take a bottom-up approach in leading social transformation of communities.

‍

‍

Yeme Tech’s partnership with dataplor has enabled them to provide consistent, accurate, and thorough insights into places globally. dataplor’s POI data has given Yeme Tech the confidence to develop their community enhancement platform with reliable and up-to-date information. dataplor’s expertise and support, combined with their comprehensive and cost-effective data coverage, have made them the ideal partner for Yeme Tech’s mission to create walkable and sustainable cities.

Client: Global Leader in Beverage Brands

Industry: Food and Beverage, CPG

Executive Summary:

A renowned global brand in the food and beverage industry aimed to enhance its international product growth through accurate and comprehensive location intelligence. The company sought to identify market opportunities and track product distribution worldwide. Partnering with dataplor, they found a reliable solution provider capable of delivering high-quality data and customized solutions. Leveraging dataplor’s extensive data library, the client successfully built advanced sales systems, expanded their market presence, and gained a competitive edge on a global scale.

‍

Challenge:

The client faced the challenge of acquiring precise global data to support their expansion plans. They needed detailed insights into product locations, market opportunities, and competition across various regions. Despite having access to multiple data channels, none provided the comprehensive, dynamic, solution-focused partnership required to drive their growth strategies effectively.

‍

Solution:

dataplor, the leading provider of location intelligence data, collaborated with the client to transform their market insights. By leveraging dataplor’s extensive data resources, the client gained access to a comprehensive catalog of accurate and high-quality location data. dataplor’s tailored data offerings surpassed the limitations of the client’s existing channels, empowering them with highly relevant location intelligence.

‍

Implementation:

With dataplor’s support, the client focused on building advanced sales systems to expand their international market presence. They seamlessly integrated dataplor’s location data into their back-end systems, enabling them to identify new sales channels while avoiding product cannibalization on a global scale.

‍

Results:

Since the initial launch of their partnership with dataplor, the client has expanded their contract to include eight additional regions. Leveraging dataplor’s core 34 attribute schema, custom competitive intelligence, and sales prioritization indicators, the client thoroughly understood their market penetration. The partnership has proven to be successful, as evidenced by its continued growth. The client commended dataplor as their preferred data partner.

‍

Quarter Results:

New Records: +130K

Observations: +6M

Closed Places: +40K

‍

Key Outcomes:

‍

Comprehensive Data Insights: dataplor’s location intelligence empowered the client to identify market opportunities, track product distribution, and analyze competition across the globe.

Advanced Sales Systems: Utilizing dataplor’s data, the client built advanced sales systems that facilitated the expansion of international product placement, ensuring optimal market penetration.

Tailored Growth Strategies: With access to dynamically updated high-quality data, the client developed actionable growth strategies based on a deep understanding of country-specific challenges, enabling them to capture more market share.

‍

Future Outlook:

The successful collaboration between the client and dataplor has laid a strong foundation for future growth and expansion. The client plans to continue leveraging dataplor’s expertise to enter additional markets worldwide. The partnership is set to flourish as both organizations work together to fuel the client’s global product growth and maintain their position as leaders in the food and beverage industry.

‍

Conclusion:

dataplor’s location intelligence solutions have empowered the client to make informed decisions and drive international product growth. By providing accurate and custom-tailored data, dataplor continues to play a pivotal role in enabling the client to identify market opportunities, optimize their sales systems, and devise effective growth strategies. The partnership stands as a testament to dataplor’s commitment to delivering superior location intelligence solutions and solidifying their position as a preferred data partner.

The Client

dataplor partnered with a top 10 global CPG company, a multinational drink and brewing business with more than 600 beer brands in 150 countries.

The Challenge

CPG companies depend on precise location data to grow abroad. They need to know where customers, retailers, and other supply chain partners are. This is a tall order in normal times — before partnering with dataplor, the beverage company had discovered that available data on distribution channels in the alcohol industry was fragmented and not frequently updated, especially in emerging economies. The client’s market, customer, and competitive intelligence team tried to maintain its own location data and found itself constantly having to turn around and source it again to avoid errors.

Then, COVID struck. If sourcing accurate and up-to-date point of interest data was hard before the pandemic, it became all but impossible during that period of historic disruption. This was especially the case in developing countries such as Brazil and Mexico, where the beverage company suspected opportunities for growth but could not begin to map out how to plot its expansion.

The client needed international location data that was comprehensive, accurate, and up to date. The market, customer, and competitive intelligence team also needed a way to gauge confidence in the data so that, when they brought their conclusions about growth opportunities to the firm’s leadership team, they could prioritize regional opportunities and know how much to trust their assessments. 

The Solution

dataplor’s POI data allowed the client to better understand their coverage and their competitors’ coverage in international markets, identify new distribution channels, and more strategically allocate resources to gain market share and decrease waste. The client team developed a multi-pronged approach to its market analysis that uses dataplor data to locate distributors and understand spatial relationships between distributors, customers, and prospective customers.

With dataplor Places data, the client now commands reliable and up-to-date information about its customers and supply chain partners. Much more easily than before, the client can identify where to focus its expansion efforts, targeting distributors who will maximize market penetration based on current gaps in market coverage and demographics.

The client can aggregate confidence scores as well as demographic, brand, and POI data to more strategically distribute its products and increase sales. And the market, customer, and competitive intelligence team now delivers detailed reports to leadership to identify growth opportunities.

‍Contact dataplor to learn more about how location intelligence can drive growth for your brand. Let’s add a contact us button here to capture leads.