Suppliers > United States > CARTO

CARTO

Overview

HQ Location United States	Year Founded 2012	Company Type Private	Revenue $10-100m
Employees 51 - 200	Website Open website	Twitter Handle @CARTO

Company Description

CARTO is the world’s leading Cloud Native Location Intelligence platform, enabling organizations to use spatial data and analysis for more efficient delivery routes, better behavioral marketing, strategic store placements, and much more.

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Case Studies.

Optimizing Coca-Cola's Vending Machine Sales with CARTO & Google BigQuery

Coca-Cola Bottlers Japan Inc. (CCBJI), the largest Coca-Cola bottling company in Asia, operates a network of over 700,000 vending machines across Japan. The company collects a vast amount of data regarding the overall sales performance of each machine and how individual products perform per machine and location. Historically, CCBJI had to extract the necessary data for analysis from the core system, build their own mechanism to create a data warehouse using ETL tools, and perform various analyses. The sheer size of the data being produced posed several challenges for the company. These included the length of time needed to return the results of a simple query and the complex maintenance of such a legacy system.

Leveraging Geodata Modeling in the Insurance Industry: A Swiss RE Case Study

Swiss RE, a leading global provider of reinsurance and insurance, was facing a series of challenges in the insurance market. The market was showing slow growth, with a significant percentage of customers acting like booking.com, reading reviews before joining an insurance product. Additionally, changes in law regulation, from IT to Internet to privacy, made it extremely difficult to release new products into insurance. This resulted in a lack of trust from regulators to insurance companies, from insurance companies to the people, and from people to insurance companies. Swiss RE needed to find a way to extend its geo-reach, geo-enable its data, and monitor its assets to overcome these challenges.

Leroy Merlin Combines DIY Ingenuity with Powerful Location Analysis

Leroy Merlin, a global home improvement retailer, needed to sense changes in selling patterns and quickly translate that intelligence into a series of decisions that go up the supply chain. Most decisions were made from headquarters, including the analysis of market areas, zones of influence, and distribution areas. This required executives and managers to know when and how much to ramp up or cut back on production, and where to distribute. It also meant choosing the right combination of transport modes to balance the urgency of delivery costs. Leroy Merlin found aggregating the data for key decisions an arduous and time-consuming task. To unify these data sources into a coherent and complete picture, managers created a seamless real-time reporting visualization to introduce precise predictive demand planning capabilities with a goal to share geomarketing and geolocalized data among store managers, marketing and business development departments, and executives.

Bringing Location Intelligence to Site Planning in Real Estate

Following seismic demographic, economic, and technological shifts across the city of London, JLL and London & Partners realized a modern site planning tool was needed that advertised the advantages of working in this global business center. JLL wanted to provide commercial Real Estate clients determining where to start, relocate, or expand operations with a solution that would process large volumes of data into accurate, precise, and up-to-date insights for clients without GIS expertise and accessible through a centralized application.

CartoDB for BI and Analytics

Rilos, a consulting firm operating in 15 countries, provides retail location intelligence solutions. They gather, update, standardize, and analyze data to help clients minimize risk, make informed decisions, and optimize sales and networks. However, this process was complex, costly, and time-consuming. As their client base grew, Rilos needed a solution that would allow clients to perform their own analysis with site studies and reports on demand, without the need for additional employee resources.

The Future of Location-Driven Mobile Big Data: Vodafone Analytics

Vodafone Business, the B2B division of Vodafone, was faced with the challenge of managing the rapidly increasing mobile data traffic and the growing demand for location-driven insights. The company wanted to leverage the big data generated across its mobile networks to better understand human mobility patterns. However, this presented two major obstacles. Firstly, to comply with privacy regulations, Vodafone needed to extract movement patterns on populations, not individuals, necessitating a robust anonymization and aggregation methodology. Secondly, to familiarize customers with working with mobile data, Vodafone needed an accessible interface capable of presenting the true value of these insights. In addition, the company saw a larger opportunity to deliver the first mobile data business solution by combining Vodafone’s data and Location Intelligence.

Improving Road Infrastructure Management with Location Intelligence

ConnectEast, the owner and operator of the 39km EastLink toll road in Melbourne, was facing challenges in managing its vast array of infrastructure assets. The company was using a paper-based method of asset management, which was inefficient and time-consuming. Documents were scanned and saved or transcribed into the system manually, or in the case of photos, uploaded. This process was not only cumbersome but also posed the risk of potential loss of information. Moreover, scanned documents (PDF’s) did not allow for spatial analysis or reporting capabilities. There was a lack of real-time visibility, and it was difficult to meet ongoing reporting demands, including KPI tracking for government reporting purposes.

Using Location Intelligence for Optimal Real Estate Decision-Making

Gloval's Data & Analytics team wanted to start using location analytics solutions to develop Valea, a web application that would allow its clients (professionals from real estate agencies and investment funds) to make spatially-informed decisions on residential property assets. They sought to provide its clients with a solution that would allow them to analyze and visualize large volumes of data on the Spanish real estate market. This included big data processing, need for faster insights, and going beyond traditional PDF formats. They needed to support business and sales strategies for their clients with asset closure data, available assets data, financial data on potential margins, and historic data on residential property assets. They also wanted to provide a faster and frictionless experience for clients, allowing them to understand the potential sale price of their property with an enriched market study, exhaustive & spatially-informed market analysis, supply and demand statistics, and socio-economic indicators to enrich existing data.

Google BigQuery Visualization: Mapping Big Spatial Data for GDELT

GDELT, the world's largest and most comprehensive open database of human society, faced a significant challenge. The Global Geographic Graph, a part of GDELT, spans more than 1.7 billion location mentions in worldwide English language news coverage dating back to 2017. The organization wanted to map the geography of the global news narrative, a task that was proving to be complex due to the sheer volume of data involved. The challenge was to find a way to effectively visualize this massive amount of data in a way that was both meaningful and accessible.

Nimble, Collaborative Rapid Response

The UNHCR’s Inter-Agency Coordination Unit based in Beirut, Lebanon’s capital and largest city, operates at the epicenter of a complex web of refugee support and response. They coordinate between over 200 organizations, governmental, NGO, and private sector. In order to effectively coordinate, they created 12 working groups, each with a distinct focus area. These working groups, supported by a unified IT infrastructure, create and execute on 4-year plans while also meeting unexpected challenges with rapid response. The refugee crisis, and the support system that has risen up to help refugees in Lebanon, is complex. Unofficial settlements pop-up regularly and the landscape is constantly shifting. Government policies impact refugee settlement status, environmental factors impact refugee safety, shifting populations require constant re-prioritization and resource allocation. While having this data is a great start, for the Inter-Agency Coordination Unit’s working groups to leverage the data towards their 4-year plans, and for emergency service and rapid response efforts, they also need a system that allows this data to be accessed, visualized, analyzed, and disseminated across their 200 partner organizations.

Moving to Omnichannel E-commerce Using Scalable Spatial Analytics

Allegro, a leading e-commerce platform in Central Europe, decided to invest in its own logistics and delivery services. Using its own fulfillment infrastructure, network of parcel lockers, and last-mile delivery services, the company aimed to improve consumer convenience and speed up deliveries. This meant that Allegro needed to have greater control over these services, to support their transition from an online to omnichannel player. The challenge in Allegro’s case was having the right analytics platform and tools in place to support their network expansion in 3 key areas: strategy, execution, and performance measurement. Specifically, the main goal was to drive an efficient and cost-effective growth strategy for its network of parcel lockers, a type of delivery that currently makes up a staggering 70% of deliveries in Poland.

Leveraging Location Intelligence to Combat Global Littering: A Case Study on Philip Morris International

Philip Morris International, a leading international tobacco company, is faced with the challenge of cigarette butts being the most littered item in the world, with 4.5 trillion discarded each year. As part of their global initiative 'Our World Is Not an Ashtray', they aim to increase the scale and reach of their participation in clean-up activities. They also want to raise awareness of the issues of littering, and cigarette butt littering particularly, in local communities. The challenge lies in the sheer scale of the problem, the need for accurate data on litter hotspots, and the requirement to monitor the impact of anti-littering activities effectively.

Optimizing Utility Management Workflows with Location Intelligence: A Case Study on Premier Utility Services

Premier Utility Services was faced with the challenge of managing their fieldwork operations, which included a large gas meter inspection and leak detection project in upstate New York. The project required the processing and management of large data sets, including data on over half a million gas meters. While Premier had turned to Fulcrum for help with data processing and management, they still needed a solution that would allow them to visualize and analyze the data at scale. The complexity of the application they needed was significant, as it had to handle vast amounts of data and provide useful insights for decision-making.

Skyhook Wireless's Global Location Intelligence with CARTO

Skyhook Wireless, originally a provider of location on mobile devices, was facing the challenge of managing and utilizing the enormous amount of data generated from their location services. The data, which was being used to provide location-based services like pinpointing a device's location on a map, was growing exponentially as the company expanded its services globally. The challenge was not just managing this data, but also compartmentalizing it into usable pieces for analysis. The expectation of high spatial-temporal accuracy, which was once a stretch for a county or a city, was now expected on a global scale. Furthermore, the company was also facing the challenge of meeting the growing expectations of businesses that required detailed data analysis, such as the number of burgers sold in one McDonald's location versus another.

Spatial Analysis in Identifying and Characterising Gentrification in London

The Centre for Advanced Spatial Analysis (CASA) at UCL was faced with the challenge of identifying, characterising, and locating neighborhoods in London that have recently undergone gentrification. They needed to disaggregate the different types of changes revealed by the data. Additionally, they aimed to predict which neighborhoods are likely to be the next targets of gentrification. The ultimate goal was to present and make available data, code, and novel interactive visualisations as a comprehensive tool for supporting policy and decision making in the city.

Leveraging Geospatial Analysis for Strategic Investment Decisions in Private Equity

The case study revolves around a private equity firm, American Securities, which was considering the acquisition of a retail chain. The primary question that needed to be answered before making the investment decision was whether the retail chain could continue to be profitable by building more stores. The firm was interested in two expansion strategies: whitespace, where the retail chain would expand into new markets where it didn't have a current brand presence, and In-Fill, where more stores would be created in the market that the retail chain was currently operating in. The challenge was to understand the potential profitability of these strategies in a short time frame, given the competitive and time-intensive nature of the bidding process. The firm also needed to understand the factors that could affect the performance of the stores, such as the impact of distance from the core market and other potential confounding factors.

Leveraging IoT for Analyzing UK's Covid Recovery & Mobility: A Case Study on Avison Young

Avison Young, a global commercial real estate services firm, faced a significant challenge due to the Covid-19 pandemic. The widespread retail slowdown and permanent work-from-home policies led to a drastic reduction in the demand for commercial spaces. This situation greatly impacted the commercial real estate market, which Avison Young is a part of. However, with vaccines becoming increasingly available, the commercial real estate market was expected to experience a sharp rebound. Many people were anticipated to return to shopping, traveling, training, and enjoying spaces outside their homes. Avison Young needed a way to visualize and understand these movement trends across local authority districts over time to better strategize and plan for the expected rebound.

Leveraging IoT for Real Estate: A Case Study on idealista's Property Investment Analysis

idealista, an online real estate platform, was faced with the challenge of understanding the trends and patterns in their web traffic. The company noticed that a significant 19% of their web traffic for Spanish coastal property listings was coming from foreigners, with the remaining 81% from a national audience. The popular Spanish slogan “sol y playa en España (sun and beach in Spain)” was attracting people from all over the world to invest in property in the Iberian Peninsula. idealista wanted to delve deeper into this data to identify interesting trends and gain a more visual understanding of their audience's preferences and behaviors.

Kroton: Delivering Locationbased Geomarketing Insights with CARTO

Kroton, one of the largest private educational organizations in Brazil, was struggling with the processing and analysis of millions of records of government information regarding population segments and demographics. The lack of a visualization tool made it difficult for them to make fast and accurate insights, which was critical for efficient market analysis. They needed a platform that could provide specific information on educational branches and geomarketing resources, and could be integrated with their business intelligence dashboard, Qlikview. Previous software they tried had difficulty handling the large amounts of data.

Commerce 360: Delivering Location Intelligence through BBVA’s financial Big Data

BBVA Data & Analytics identified a gap in the market to provide location-based insights to small to medium enterprises (SMEs) in Spain, which drive a large percentage of the country's economic activity. They wanted to develop a solution that could answer questions related to Performance Management and Site Planning. Point of Sale (POS) card payment terminals provided a wealth of historical sales data, which was then aggregated and anonymized to ensure statistics on retail activity could not be traced back to individual merchants or transactions of specific customers.

How Location Intelligence aids emergency planning and resilience efforts in Mexico City

The 7.1 magnitude earthquake that struck central Mexico on September 19, 2017, caused significant damage to the infrastructure of Ciudad de México (CDMX), posing unprecedented obstacles to response efforts. The earthquake destroyed 228 buildings in the city, with many more at risk of collapse, putting even more residents at risk. The immediate aim of the Commission for the Reconstruction, Recuperation, and Transformation of the City of Mexico was to rescue people trapped by the rubble and evacuate others in areas at risk of further collapses. However, the damaged infrastructure posed obstacles in terms of assessing citywide damage. The Commission needed a two-way digital resource where citizens could file property damage reports that provided local officials with crowdsourced data on where to allocate emergency resources.

Driving value for Smart Cities with IoT and Location Intelligence solutions

Telefónica’s Smart Cities and IoT team needed to build a Smart Cities dashboard that could process and analyze data from thousands of Internet of Thing (IoT) sensors as actionable insights to enable their public sector customers to run their cities more efficiently and sustainably. They needed an integrated, FIWARE compliant platform to make large amounts of real-time data accessible to the general public and public administrators through data visualizations, interactive maps, and customized reports. The dashboard would focus on verticals most suited to optimization using IoT technologies: parking, street lighting, waste management, environment, transportation, and service monitoring.

Sistema.bio's Story: Optimizing field operations with CARTO

Sistema.bio faced several challenges as they expanded their operations globally. They needed to visualize all existing and pending biodigester installations, over 15,000 existing digester locations on one map (and over 30,000 yet to come by 2023), to empower the staff to make informed decisions on their team resourcing and activities. They also needed to conduct market research and site selection for expansion plans. They needed to know where they currently have a strong market presence, and where they could be expanding. Exploring their existing customer base - such as through heatmaps - is a fantastic way to quickly build location into strategy. Lastly, they needed to connect their existing data pipelines and algorithms to optimize the activities of their field staff, whether that’s their credit, installation, sales or training teams. Most of these processes are currently managed in Salesforce.

Visualizing Linguistic Big Data for Deeper Insights

SIL International and Ethnologue are adapting to a global landscape that is adopting new technologies and growing more interconnected by the day. As Ethnologue looks to be a more powerful resource across sectors, they need to keep pace with rapid technology growth and the need for more dynamic language intelligence. Given the spatial nature of language, where it is spoken and how it either shrinks or proliferates, the ability to visualize these 7,117 living languages was paramount to the educational component of Ethnologue’s mission. Historically, they had been using more static maps as a resource, which is natural given their literary roots. They would subsequently publish the maps online. And while this is certainly useful, especially at the more granular country level, it doesn’t provide a complete and living resource on where languages are spoken EXACTLY. It also lacks a level of interactivity which helps the project to transcend to the goal of language intelligence.

Growing Sports Communities through Geospatial: Australian Football League Case Study

The Australian Football League (AFL) aims for around 3-4 percent growth in community engagement each year. Ensuring facilities are right in each area is an important aspect in attracting new players and retaining current players around Australia. Organisational effectiveness is important to ensure resources go to the right places. In each region, clubs and leagues make requests to the state and then to the national body for funding and the AFL must assess each application based on its merits. Local region managers are also required to make their case to local councils to obtain investment. Communicating the status of a region’s participation gives people on the ground the right starting information so they can take the right actions to improve participation. In the world of data driven decision making, the AFL needed a way to communicate quickly to local and state governments about their local needs. Communicating with local authorities using a standardised report that wasn’t tailored to a region made it more difficult to convey the AFL’s message.

Advancing Renewable Energy through Spatial Analysis and Visualization: A Case Study of NREL

The National Renewable Energy Laboratory (NREL) is the only federal laboratory in the United States that focuses solely on renewable energy, commercialization, development, and research. The challenge NREL faces is how to realize high penetrations of renewable energy while achieving broad goals of reliability, resilience, and affordability. The complexity of the energy grid, with its numerous generators and variable load, requires sophisticated tools and visualizations to understand and manage. A fundamental challenge with renewable energy is its variability and continuity in both space and time, which poses challenges to traditional models. The question is how to take a phenomenon that’s inherently continuous and variable, and fit it into a discrete model space, whether it’s nodal or regional. The biggest question is how to ensure that the resource is properly characterized and preserved.

Developing an Emergency Response Tool: A Case Study of NYC's Use of CARTO

In the aftermath of Hurricane Sandy, New York City (NYC) was faced with the challenge of scaling its ability to perform analytics immediately after a disaster and in preparation for future disasters. The city had a significant need to enhance its geospatial analytics capabilities, particularly in terms of map-making. The primary challenge was finding a tool that was user-friendly enough to be used by any analyst, regardless of their level of expertise in geospatial analytics. The goal was to quickly visualize data and use it to inform response efforts. The city was also looking to move away from segmenting analysis by a specific type of tool, which limited the ability of analysts to fully utilize and visualize the data.

Unlocking Location Intelligence for Retail Marketing: A Case Study on Posterscope

Posterscope, a location-based marketing agency of Dentsu Aegis Group, was faced with the challenge of innovating the way brands are built by leveraging location intelligence. The agency aimed to connect brands with Out-of-home (OOH) audiences at the right moment, making communication more personal, contextual, and relevant. The challenge was to understand the location of these moments and use this information to develop efficient communication solutions. The retail industry was evolving beyond transactions, with activation, engagement, and transactions becoming closer than ever at any touch point. Posterscope needed to adapt to this change and make retail more experiential using location and data. Despite the rapid growth of ecommerce, physical retail was still a significant contributor to the global industry. The challenge was to apply the concepts that made ecommerce grow so fast, such as contextuality and data analysis, to physical retail.

Visualizing the Transformation of Commerce & Communities through On-demand Services

The challenge faced by Postmates, a leader in on-demand delivery in the US, was to efficiently connect consumers with merchants and deliver goods from any merchant to the customer's door in minutes. The company had to deal with the complexities of location data, as location carries weight and meaning. The company had to understand the preferences of customers which varied throughout the year, the week, and within a city itself. They also had to deal with the challenge of delivering a wide variety of items, from furniture to late-night medicine runs and diapers, across 300 cities in the US and Mexico.

Optimizing Public Transport through IoT: A Case Study of SAGULPA

The Sociedad Municipal de Aparcamientos de Las Palmas de Gran Canaria (SAGULPA), a public transport company, was facing a challenge of managing and optimizing their mobility resources. They had access to multiple location data sources but lacked a unified platform to organize and analyze this information. The company wanted to better understand the mobility patterns in the city, including the distribution of journeys, the profile of the travelers, and the usage of their services. The goal was to use this information to improve their services, reduce traffic congestion, and promote sustainable transport solutions.

Improving Healthcare Site Planning with Geolocation: A Sanitas Case Study

Sanitas, a healthcare provider, was grappling with the challenge of understanding the impact of location on the performance of their clinics and hospitals. The healthcare industry is one where location plays a significant role, and Sanitas wanted to gain insights into the specific characteristics of their clinic locations. They wanted to understand how these characteristics, including the age and income of the local population, affected the performance and income of their clinics. Additionally, they wanted to profile their clinics based on the information about their customers, competitors, and other points of interest in the area.

Leveraging Spatial Data for 5G Deployment: A T-Mobile Case Study

T-Mobile, the second-largest wireless carrier in the United States, faced a significant challenge when it launched its 5G home internet service in early 2021. The service was made available to 30 million homes across the US, following a pilot period that began in 2019. The challenge lay in the fact that wireless internet for a fixed address requires a significantly different qualification process than a traditional internet service provider (ISP). Making informed decisions about candidacy required a large amount of spatial data. With 185 million unique addresses in the US, each with untold variables associated with them, T-Mobile needed to determine who was qualified for service. This included determining at an aggregate level which zip codes, MSAs, or states had the greatest potential.

Indoor Mapping & Airport Routing Solution by Aena & Telefonica

Aena, the world's largest airport management company, was faced with the challenge of improving navigation for passengers within its airports. The company wanted to enhance the overall passenger experience at all stages of their journey. The primary goal was to enable passengers to visualize their entire journey and locate key points of interest such as restaurants, shops, toilets, or security controls. Additionally, Aena wanted to provide passengers with an estimate of the time required to reach the boarding gate. This was a significant challenge considering the vast number of passengers, over 275 million, passing through its 46 Spanish airports and 23 airports in London-Luton and America annually.

ASDA's Strategic Expansion Using CARTO for Site Selection

ASDA, one of the largest British supermarket chains, faced a challenge in expanding their 'toyou' parcel service. Initially, the service was available in 639 locations within their estate. However, ASDA aimed to grow this number to 5,000 locations by incorporating external drop boxes closer to their customers, rather than relying solely on their traditional estate, which was typically a 10-minute commute for consumers. To kickstart their expansion plan, ASDA needed location data and insights to understand the key drivers for the site selection of new click-and-collect locations before deployment. They also needed to understand the proximity to target demographic and socioeconomic segments, such as universities.

Leveraging Spatial Analytics for Market Expansion: A Bumble Case Study

The team at Bumble was seeking opportunities to expand in under-penetrated markets. Traditionally, they gauged their market presence by looking at monthly and daily active users. While this gave them an idea of their user base size in a particular market, it did not provide a clear picture of their actual market penetration. For instance, markets with large populations like the US, China, and Brazil naturally have larger user bases. However, the number of active users does not necessarily reflect the company's market position. Therefore, Bumble needed a more accurate method to estimate their total addressable markets (TAM) and market penetration.

Leveraging Location Intelligence for Safe Beach Visits in Valencia

The Valencia region in Spain, a popular destination for both local and international tourists, is home to over 340 beaches. During the de-escalation phase, the region was receiving a high number of visitors. The Generalitat Valenciana, the government of the autonomous region of Valencia, identified a challenge in managing the beach capacity to ensure the safety of the visitors. They needed a tool that could provide real-time information about the beach capacity to the visitors. The tool needed to be mobile-friendly as most visitors would likely access the information on their mobile devices.

Google and CARTO's Collaboration: Shaping the Future of GIS & Spatial Data Science

Spatial data analysis is at a critical juncture. Over the past decade or so, customers have had to manage their own infrastructure, including the installation and maintenance of their GIS software. This could be on a laptop, desktop, or even a mainframe, but the responsibility of determining the size of the computer, the CPU cycles needed, and the storage capacity required fell on the customer. This process was not only time-consuming but also required a significant amount of technical expertise. Additionally, customers had to find and prepare the datasets they needed for their analysis, which could be a challenging and costly process.

Leveraging Location Intelligence for Profitable Commercial Real Estate Investments

Hodges Ward Elliott, a commercial real estate investment company, was looking to leverage data science to gain a competitive edge in the real estate market. The company recognized the potential of data science in real estate, an industry that has traditionally been slow to adopt such technologies. The challenge was to find a way to effectively use data science to understand the preferences of groups of people and how these preferences change over time. This understanding is crucial to predicting trends in real estate, as it can provide insights into where people want to live, shop, and work. However, obtaining and analyzing relevant data was a significant challenge due to the lack of readily available data in the real estate industry.

Case Study: CARTO for Future Cities - the Data Portal of Medellín: Infrastructure for an Innovative City

The city of Medellín wanted to serve a broader demographic by applying new technologies to connect city departments and agencies like transportation, administration, healthcare, education, utilities, and construction. To accomplish Medellín’s objectives, priority was given to the areas of the city where bigger social and economic issues were found. Emphasis was placed on the planning and development of urban projects to transform the physical environment in order to implement profound social and cultural shifts in the targeted communities.

Case Study: CARTO for Public Utilities - REAL-TIME Data Insights for Red Eléctrica De España

Red Eléctrica de España (REE) wanted to provide the public with real-time information on over 2,000 indicators related to the Spanish electrical grid. It was critical to find a solution that was comprehensive and easy-to-use because indicators are responsible for making timely information public by law. With thousands of indicators, many updating every ten minutes, REE had to find a way to organize and display them so they could be found, analyzed, and understood. There was also a desire to show the data to an audience that lacked technical knowledge. They needed a solution that was fully customizable and provided an easy platform for spatial data analysis. Additionally, REE needed a model to visualize the power supply across the whole country, liaise with suppliers, coordinate supply with neighboring countries, and many other measures, that would reveal the performance of the energy market and other interesting data in a way the public could understand.

Case Study: CARTO for Internet Service Providers - Fon: Visualizing Millions of Data Points for Better Client Access

Fon Wireless Ltd. (Fon) is a leading, worldwide WiFi provider that operates a system of dual access wireless networks. With more than 19 million crowdsourced hotspots, their goal is to extend WiFi around the world. Fon wanted to develop a tool to expose their access point coverage throughout the world, as well as their partner network in various countries. They needed to build an open portal to help customers and clients explore and find millions of access points that they could connect to in the Fon Community WiFi. The portal required a visualization that could be adapted at every zoom level, preserving optimum performance, and the ability to include logos and details of all of Fon’s partners. Additionally, Fon wanted to have a more appealing visualization on their corporate website that enabled partners to have their own map including their specific access points.

Powering Site Planning & Demand Modelling with Location Intelligence

Endesa, the largest electric utility company in Spain, was looking to grow its customer base and maintain its leadership in the Spanish market. They needed to understand how to best serve their existing B2C customers and how to gain market share from their competitors. Their Business Intelligence team had previously used Microsoft Excel to evaluate their coverage and measure demand in complex models which were not always accessible to business users in other departments across their business. Endesa wanted to make this process easier, using a more intuitive tool to share insights with departments such as Corporate Strategy, Finance and Location Planning.

OneMap Case Study

OneMap, a real estate and property development mapping platform, was facing a time-consuming process for producing custom maps and reports. They also had to undertake a substantial amount of data entry for their weekly updates. Their primary objective was to reduce the time involved in providing their customers with complete and accurate property reports. They aimed to compile detailed property information into a single, easy-to-use platform capable of leveraging location data and processing a high volume of attributes from each point. They also sought the capacity to create high-quality digital maps that would empower their customers, providing them with the means to visualize multiple data streams and perform their own filtering and spatial analysis.

Bringing Location and Predictive Sales Analytics to Mexican Retailers

Descifra, a global Location Analytics provider based in Mexico City, aimed to disrupt the site planning and sales prediction process with their product, Omen. The product brings together a large and diverse range of datasets, making it crucial to have an intuitive and slick interface for decision-makers to extract insights. The visualization tool needed to handle millions of data points, often in real-time, without compromising on speed, security, and geographical granularity. The challenge was to forget traditional 150-page PDF reports and put cutting-edge Location Intelligence visualization in the hands of their clients.

Unleashing The Power of Location for Increased Sales Territory Performance

Securitas Direct, a leading connected alarms provider, had a large and growing sales force of 1,000 reps tasked with visiting existing and potential customers to retain and acquire new business. However, the allocation of leads and opportunities from their marketing department was done manually without exploiting the context of location data. This resulted in several different sub salesteams looking at the same opportunities and leads often being manually assigned by province, rather than geographical proximity, wasting sales rep’s time and resources. Securitas Direct needed a location-driven solution that would allow their team to be more efficient and productive, and that would work smoothly alongside their existing CRM technology (Force Manager).

Wecity: Using Location Intelligence to drive sustainable mobility strategies in smarter cities

Wecity, a sustainable mobility app, wanted to crowdsource and visualize smartphone-generated location data collected from thousands of users every month, for both their web and mobile app. They needed a location technology that would allow them to seamlessly collect, visualize and analyze user-routing data from cities and governments, and also about intermodality for potential partners, to create safety insights and indicators for its users. They also wanted to create a compelling platform by embedding maps that were beautiful and easy to use, as well as in line with the rest of their app.

Using Location Intelligence to Improve Agricultural Sustainability

Indigo Agriculture, a company that works with growers to reimagine the entire agriculture system, faced several challenges in their data-driven approach to business. Their analytics team had to deal with a wide range of internal and external data sources, including internal CRM data from Salesforce, external meteorological datasets, and data collected in the field. The team needed to pull data from existing systems and upload a variety of data formats for collaboration across departments and the creation of a seamless end-to-end experience for customers. Another challenge was turning complex geospatial data into actionable insights for business users. The team sought to provide business users in marketing, sales, and field operations a more intuitive and interactive format for viewing data, instead of relying solely on traditional, tabular data. Lastly, the business units often needed to see prototypes or analysis against tight deadlines. The team needed to quickly bring together data, carry out complex geospatial analysis, and deliver dashboards to ensure geospatial analytics were considered in all business decisions.

Data Monetization for Credit Card Providers with Location Intelligence

Mastercard, one of the world's largest financial services companies, processes over 160 million transactions every hour. This transaction data is extremely valuable and represents a significant resource for the company. However, monetizing this data presents several challenges. Firstly, any data monetization strategy must prioritize data privacy and security to maintain Mastercard's promise of keeping payments safe and secure. This requires anonymizing and aggregating the data to remove individual identifiers and prevent the inference of specific identifiers once the data has been aggregated. Secondly, the data needs to be productized in a way that appeals to a diverse audience with varied needs. This requires delivering an intuitive and singular user interface while ensuring the user experience is tailored based on industry and role. Lastly, the spatial nature of the transaction data presents challenges in determining the spatial scales at which to aggregate data. This is particularly complex when working internationally, as different countries have distinct geographic units.

Real Estate Market Analysis: Tinsa Digital Case Study

Tinsa Digital wanted to provide more powerful solutions that would allow their clients (Financial Institutions, Investment Funds and Real Estate agencies) to manage their portfolios in a more agile way - with more spatial insight. The solution needed to provide a series of indicators in relation to asset locations so that clients could see relevant segments using multiple layers of information in just a few clicks. Working with CARTO’s platform allowed Tinsa Digital to automate processes, such as the loading and visualization of information, generating a 'user-friendly' display, requiring no specialized training. This allowed these different divisions of their business to perform complex calculations, delivering results faster, as well as including different layers to complete Real Estate, financial, or geospatial analyses. This would allow them to create AOIs (Areas of Influence) correctly attributing values to different administrative regions, as well as optimizing their own geometries. The key challenges included: User management for large volumes of information, Flexibility and responsiveness when working alongside Tinsa Digital clients, Effective integration and automation of ETL processes.

Boosting Field Sales Efficiency with Location Intelligence: A Case Study on Securitas Direct

Securitas Direct, a leading connected alarm provider in Europe, faced a significant challenge in their sales department. Despite having a robust sales force of 1,000 representatives, the company was not fully leveraging the potential of location data in their sales strategy. The sales team had been manually allocating leads and opportunities from their marketing department, without exploiting the context of location data. This approach was not only inefficient but also failed to maximize the productivity of their sales force. To enhance their sales performance, Securitas Direct needed a location-driven solution that would not only increase the efficiency and productivity of their team but also seamlessly integrate with their existing CRM technology.

Supply Chain Network Optimization & Cold Chain Transportation for SEUR

SEUR, a leading parcel delivery company in Spain, was facing challenges in optimizing their cold transportation network. The company was looking for a solution that would allow them to assess the current state of their network, identify areas of high demand, and determine if their distribution centers (DCs) were strategically located. They also wanted to quantify the impact of changes in their current network, such as the opening or closing of DCs and changes in delivery areas. Furthermore, SEUR was seeking to build an optimization model to identify where DCs should be located and design their transportation network (supply chain network design).

Telefónica Enhances Customer Loyalty with User-Centric Mapping Solution

Telefónica, a multinational telecommunications company, was looking to enhance its data analytics capabilities. The company identified three levels of data analytics - descriptive, predictive, and prescriptive. The descriptive aspect, which involves visualizing data patterns, was a particular challenge. Traditional methods of data representation, such as Excel sheets, were not effective in conveying the patterns of smart steps, including how people move, where they come from, and where they go. The company needed a solution that could provide a more interactive and visually appealing way of presenting data to its clients, who are very visual and prefer to interact with data.

Vodafone's Location Intelligence: A Case Study on Mobile Data Insights

Vodafone, a leading telecommunications company, relies heavily on location intelligence to manage its networks and provide superior services to its customers. The company aims to understand where a customer is, where they are moving, and how to provide them with the best service, including fiber and other technologies. The challenge lies in the accuracy and precision of the data. Vodafone aims to build a product that can predict where people are moving with such precision that it knows exactly where people are going from and to. To achieve this level of precision and to outperform their competition, Vodafone needs to overcome the challenge of building technologies using complex algorithms and software.

Enhancing Urban Mobility in Madrid through Crowdsourced Traffic Data

The city of Madrid, like many other major cities worldwide, grapples with the challenge of traffic congestion. With half a million cars entering and exiting the city daily, managing traffic flow and reducing congestion is a significant issue. Waze, a crowd-sourced navigation and traffic app with over 100 million active users globally, sought to address this problem. However, while Waze had access to a wealth of user-generated data, it needed a way to connect with governmental organizations and municipalities to leverage this data effectively. The challenge was to transform the raw, open data into actionable intelligence that could be used to improve traffic management and provide citizens with accurate, real-time traffic information to plan their daily commutes.

Leveraging IoT for COVID-19 Preparedness in Rural Communities

During the COVID-19 pandemic, the Center on Rural Innovation (CORI) was faced with the challenge of analyzing disparity levels across the United States to identify areas in greater need of support. The pandemic had a significant impact on rural communities, and there was a pressing need to understand the extent of this impact. The challenge was not only to gather relevant data but also to analyze and present it in a way that would provide clear insights into the situation. This included identifying school districts impacted by the lack of broadband availability and counties facing significant employment risks due to the pandemic.

Spatial Data: Revolutionizing Out-of-Home Advertising

Clear Channel Outdoor, one of the world's largest outdoor media companies, faced a significant challenge in their operations. They were using mobile location data to link the physical and digital worlds, identifying devices exposed to their Out-of-Home (OOH) panels and collecting these insights within their RADAR® product. However, they encountered a problem in making this data easily accessible to advertisers for campaign planning. The challenge was to create a solution that would allow advertisers to easily access and utilize the insights derived from the mobile location data collected by Clear Channel Outdoor. This was crucial for the advertisers to plan their campaigns more effectively and reach their target audience more efficiently.

Leveraging Location Data for Enhanced Financial Services: A Case Study on Enigma and CARTO

The financial technology (fintech) sector faces a significant challenge in recognizing the existence and availability of location data and its potential benefits. The sector has a wealth of internal datasets and monitoring systems that could be enhanced with the integration of location data. Particularly in the field of anti-money laundering, the combination of location data with financial data could be instrumental in aiding law enforcement and identifying illegal activities. However, the awareness and understanding of how to incorporate this data into day-to-day operations remain a significant hurdle. Additionally, the potential of location data extends beyond anti-money laundering, offering opportunities in marketing and credit risk management by providing insights into where people live and the communities around them.

IoT in Coffee Supply Chain Traceability: A Case Study on Enveritas

Enveritas, a non-profit organization, was founded in 2016 with the mission to eradicate global poverty in the coffee sector by 2030. A significant part of this mission involved tracing the journey of coffee from its source to the consumer. This process not only required identifying the geographical locations where the coffee was grown and processed, but also understanding the social, environmental, and economic conditions prevalent in those places. The challenge was to create a comprehensive and dynamic visualization of the complex types of coffee based on the ECX classification system. The organization needed a user-friendly solution that could provide a clear and accessible representation of the coffee supply chain.

Enhancing Geospatial Analysis with CARTO and Google BigQuery

Google's BigQuery was facing a challenge in the field of geospatial analysis. The traditional hardware and software used for geospatial analysis were limiting the potential of BigQuery. The constraints of the existing infrastructure were hindering the scalability and performance of geospatial analysis. Additionally, the process of integrating geospatial data was cumbersome and time-consuming. Users were often required to perform administrative tasks and reference tables, which were considered 'boring' but necessary. The challenge was to enhance the geospatial analysis capabilities of BigQuery and make the integration of geospatial data more efficient and user-friendly.

Hodges Ward Eliott's Rapid Data-Driven Map Creation with CARTO

Hodges Ward Elliott, a real estate company, was facing a significant challenge in creating data-driven maps, a fundamental aspect of their daily operations. The process was labor-intensive and time-consuming, often taking the graphics department 3-4 days to produce a single map. The introduction of the data science team and the use of open-source tools like Leaflet improved the situation, but it still took four hours to create a high-quality map. The company was looking for a solution that could further streamline this process, reduce the time taken, and make it easier to create and iterate on these maps.

Leveraging IoT and Data Visualization for COVID-19 Response: A Case Study of i-sense and CARTO

i-sense, an Interdisciplinary Research Collaboration (IRC) funded by the Engineering and Physical Sciences Research Council (EPSRC), aims to build digital sensing systems to identify and prevent outbreaks of infectious diseases and antimicrobial resistance. However, they faced a challenge in interpreting the daily or weekly data related to the COVID-19 pandemic response published by Public Health England, National Health Service (NHS), and the Office of National Statistics (ONS). The data was often difficult to understand, making it challenging to evaluate the effectiveness of the system in place.

Leveraging Geospatial Analysis for Business Expansion: A Case Study of Instacart

Instacart, a grocery delivery and pick-up service, was facing a challenge in its business expansion strategy. The company's approach to connecting retailers with customers was conservative, limiting its growth potential. The company was not fully utilizing the potential of its app to connect customers with their preferred grocers, which was affecting customer engagement and spending. Furthermore, the company was not fully capitalizing on the opportunity to help its retail partners reach new customer bases. The challenge was to find a way to be more aggressive in their growth strategy, increase customer engagement and spending, and help retailers reach new customers.

Leveraging Geospatial Analysis for Investment Decisions: A Case Study of Jefferies

Jefferies, a global investment banking firm, was grappling with the challenge of making informed investment decisions and advising their clients effectively. Traditional data strategy and research were not formally integrated into most of their research, often being treated as an afterthought. The firm was also struggling with the time-consuming process of assembling information, which could take months or even years. Furthermore, the firm was trying to shift from making assumptions to making decisions based on grounded fundamental observations. The challenge was to find a key question that drives the key variable that a human is deciding is incredibly important, and shift it from being something that was previously assumed, to something that probably still has some assumption around it but can at least be based on a grounded fundamental observation.

Mapping Segregation: MIT’s Atlas of Inequality

The challenge revolves around understanding the social segregation in cities, particularly in the United States. The traditional understanding of segregation is based on geographical locations, often characterized by the stark contrast between rich and poor areas within close proximity. However, this perspective fails to consider the 'why' behind segregation, focusing solely on the 'where'. The challenge is to challenge this traditional notion of segregation by using high-frequency location data to understand the reasons behind segregation. The data gathered from 11 of the most populated metropolitan areas in the US revealed that on average, around 75% of the people we interact with daily live more than 15 kilometers away. This suggests that to understand segregation, we need to go beyond geographical locations and start considering the reasons behind it.

Case Study: CARTO for City Development Vizonomy: Assesing Climate Risk

Vizonomy, a computer software company, was faced with the challenge of making natural risk assessments and hazard mitigation plans accessible to a wider range of stakeholders. Traditionally, these assessments were only available to a select number of government stakeholders who could afford the costs associated with the process. As a result, few communities were able to develop a plan and thereby become eligible for federal resources should a natural disaster occur. Additionally, the traditional risk assessment process was built on a static model, where datasets quickly become out-of-date and new assessments cannot be developed easily without external assistance.

Case Study: CARTO for Media & Agencies - Tecnilógica Uses Location Intelligence for A Viral Visualization

Tecnilógica, a digital agency, wanted to leverage a timely, newsworthy event to generate brand awareness and drive traffic to their platform. The Ashley Madison data leak provided an opportunity to create a data-driven visualization that would attract a global audience. However, the challenge was to clean up the dataset, removing any identifying information to protect users, and then find a way to visualize the data in a compelling and insightful manner.

CARTO for Telecoms & Big Data: Crowdx Analyzing Big Data for Even Bigger Customer Returns

The telecommunications industry has limited visibility of the end customer quality experience. Assessing mobile phone users' needs is challenging due to barriers of access to actual, accurate customer experience related information. The two standard methods of collecting network quality information, through a drive test and by collecting data within the network from nodes, are either costly and inefficient or only provide an estimation and a very limited network centric view of the customer experience. Telecoms struggled to analyze trends and variances in traditional formats. In some cases, they lacked pertinent information to make timely decisions and take corrective action.

Case Study: CARTO for Social Engagement - Spotify, Increasing Brand Visibility with Data-Driven Visualizations

Spotify, a digital music service with over 100 million active users, sought to increase its brand visibility and user traffic through data-driven visualizations. After an original data-driven visualization increased traffic to the music platform by 2 million in just 11 days, Spotify aimed to repeat that success. However, this time the map needed to be customized with Spotify branding to underscore the company’s global reach and further brand awareness, while driving even more user traffic to the platform. The new visualization needed to embody Spotify as a brand, and provide insights on distinct musical preferences.

Case Study: CARTO for Media - Le Télégramme Leverages Data Visualizations for an Engaging Online Presence

Le Télégramme, a French regional daily newspaper, was looking for a new way to visualize data in a timely manner to increase brand awareness and drive more traffic to its platform. They wanted to use visualizations that could be customizable and allow for easy adoption by non-coders. The collapse of the traditional business model of print newspapers has led many publications to establish themselves with original reporting, rather than just commentary or summaries of reporting from other publications to attract and retain readers and advertisers.

Smart Site Planning for the Healthcare Industry with Location Intelligence

As Sanitas, a healthcare provider based in Spain, grows and diversifies its offerings, it needs to ensure it's expanding in the right places and in the right ways. Being a part of Bupa’s massive operation, which touches 190 countries, finding locations best suited to new healthcare centers is a complex endeavor—as is uncovering the healthcare needs of local populations. Sanitas prides itself on serving clients throughout every stage of life; doing this well across a growing variety of facilities requires a wealth of demographic and location information—as well as the means to organize and understand that data in actionable ways. To fulfill its mission, Sanitas needed location intelligence that could provide dual functions: helping the company make wise choices for future development, and enabling the company to provide exceptional care to local demographics.

Using Location in Real Estate Market Analysis Applications

JLL, a global leader in real estate services, was looking to scale their Gea solution in key countries such as the US, Australia, the UK, Italy, and France. The Gea platform brings together thousands of data types on Real Estate assets to ensure accurate valuations and analyses are provided to their clients. Connecting to their Big Data Cloud infrastructure (JLL DataHub) would be fundamental in order to ensure high-quality, automated data pipelines could support frequent updates to the billions of data points that would be available in the solution. However, they faced challenges in localization for different markets, handling big data, and ensuring the final application would be user-friendly for consultants with more commercial profiles.

Driving Spatial Insights for Outlet Network Optimization

Renault needed national level georeporting for local and regional analysis of their network. The goals of the Renault France team are threefold: optimization, standardization, and a more granular analysis. With 4000 outlets across the country, Renault’s presence in France is extremely significant. Each regional manager is looking for the opportunity to look into coverage levels, local competitive landscape, and outlet by outlet performance data. Having a unified system where their regional leaders can look at their own data to make decisions around resourcing and geomarketing, while standardizing language and process via a single platform across the country, would help meet KPIs for sales and aftersales outlets.

Unlocking Consumer Insights at Scale with Cloud Native Spatial Analytics

Faraday, a marketer's choice for consumer prediction infrastructure, faced several challenges in predicting the future of an ever-evolving industry. The task required a holistic approach to gathering, visualizing, and segmenting data from a variety of sources. The task became increasingly difficult if the objective was to make these predictions with minimum or no requirements for user technical expertise. Faraday needed a way to visualize vast amounts of data to bring customer personas, segments, and predictions to life and drive faster business decisions. Another challenge was ensuring that these large volumes of data or the level of sophistication of each analytical use case did not impact the speed of the end-user experience. Faraday ingests data from 200 integrations into BigQuery. Therefore, in order to take its predictive analysis solution to the next level, Faraday needed a BigQuery- native Location Intelligence platform capable of seamlessly analyzing and visualizing the vast amounts of consumer household data stored there.

How ING use spatial analysis to drive Residential Real Estate decisions

ING’s marketing team wanted to provide an added-value service that would allow consumers to select their neighborhood with more location-based context, based on their own individual needs and characteristics. They noticed that their clients were often focusing on key property information such as square meters, the number of rooms, recency of renovation, or even how much sun it gets, rather than focusing on the location. They wanted to create a neighborhood selection solution, that would allow their existing and potential clients to find the perfect neighborhood for them, based on their needs, preferences and budget. The challenge was to gather and present information in a clear way for consumers, allowing the user to gain new insights from high volumes of spatial data in 5 cities, going beyond simply displaying Points of Interest on a map. The solution also needed to work seamlessly on mobile devices, creating a design that would ensure the mobile experience would be just as intuitive for users.

Workplace Social Distancing with Indoor Mapping Software

Perkins and Will, an interdisciplinary, research-based architecture and design firm, faced the challenge of ensuring the safety of their employees as they returned to work amidst the COVID-19 pandemic. The firm needed a comprehensive guide to facilitate the transition back to the workplace, taking into account factors such as employee readiness, distance analysis, and new protocols. The firm had to support the return of 2,600 employees across 21 studios in North America. The process of planning resource allocation in the workspace needed to be three times faster to meet the demands of the situation.

Full Stack Geomarketing App Development

Anagraph, a Montreal-based company specializing in building custom geospatial solutions, identified a gap in the market for small, online businesses. Many of these businesses lacked information about the market and tools to provide insights into buyer personas. Geomarketing solutions have been around for decades, but due to complexity and cost, they have long been accessible only to large enterprises. Anagraph saw an opportunity to bridge this gap and provide a greater range of retail insights to smaller operations, allowing online retailers to build a more comprehensive geomarketing strategy.

Identifying Real Estate Investment Opportunities with Location Data

Grupo Lar’s asset management and development team were looking for ways to use new types of data, and in particular location data to understand potential investment opportunities - considering more spatial factors in their decision-making. Spain is Grupo Lar’s main market, where it has consolidated a dominant position in the real estate sector, combining investment, management, and promotion. As an independent company with a sole focus on real estate, Grupo Lar is able to establish a strategy based on the best investment opportunity moments, thanks to its extensive knowledge of the sector as well as its ability to access local resources. This position allows them to gather large amounts of insights on a daily basis. Therefore, the main challenge of this project was to provide a solution that enabled them to analyze and visualize all their business KPIs in a simple and intuitive way, while processing the data in real time, in order to streamline the decision making process.

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