Data makes the world go round. Sourcing consistent and precise streams of geographic information is crucial to enhancing your planning and decision-making procedures. On your own, this task can seem daunting and require significant investments of time, energy and resources. Instead, working with a trustworthy geographic data provider can streamline your operations and get you back to focusing on the areas of your business that need your attention.

dataplor is a world-leading data provider known for delivering premium global market insights to help businesses optimize their operations. Our meticulous attention to detail and commitment to verifying our data will place our services at the top of your list. We give you what you need to learn more about the world and make decisions that propel you toward success. 

All the Information You Need Is at Your Fingertips

Don’t risk your global efforts with outdated or incomplete data. dataplor seeks to give you an in-depth representation of the world around you using thorough collections of global market insights. We collect data from more than 250 million locations in 200 countries and territories, giving us one of the largest data coverages in the world. Our places datasets are constantly growing with point of interest (POI) information complete with detailed metadata, making it easy to find the data you’re looking for. 

Our experts leverage advanced technologies to support our work, including image recognition, artificial intelligence and language models. These tools help us collect a wide range of information regarding consumer behaviors, statistics and geographic information tied to locations and brands. Gain more information about your POIs without sacrificing your time, leaving all the heavy lifting to dataplor. 

Trust High-Quality Global Places Data 

Optimize your global market insight with information you can trust. At dataplor, we understand how vital your data quality is to your business and strive to provide a comprehensive solution. Our global places datasets are updated in as close to real-time as possible through our advanced machine-learning capabilities. This ability allows us to provide companies with an exhaustive supply of billions of detailed data points to fuel business strategies.

dataplor has built a state-of-the-art quality control system to foster the reliability you deserve. We constantly validate data to identify duplicates and resolve discrepancies through AI-driven automation and an in-house team of highly trained experts, scientists and analysts. Our professionals are positioned worldwide, leveraging their local expertise and language proficiency to review data and flag errors. Approach new investments and expansions confidently while knowing you’re tackling every decision with a stockpile of accurate data. 

Get Started With Our Service Today

Partner with a leading geographic data provider to maximize your business’s potential. At dataplor, we have years of experience working with companies of all shapes and sizes, providing unrivaled information to fuel successful decision-making and problem-solving activities. Our large global places datasets offer valuable metrics your business can use to satisfy customers, scale your operation, and take advantage of unique market opportunities. 

Contact dataplor online to find the answers to all your questions and discover how our service can assist your business.

When businesses need to make location-based decisions, they often turn to point-of-interest data. POI is the backbone of many modern tools and data sets, so high-quality data is crucial, but gathering this information can be challenging. Understanding how to collect and compile POI can help you make informed choices when obtaining and using the data for your organization.

What Is POI Data?

Points of interest are any attraction or service people may find exciting or valuable. Some points-of-interest examples include businesses, historical landmarks, parking lots, ATMs, and churches. You access POI data whenever you use a navigation app to get directions on your phone. It shows where these places are and can include metadata with more information, such as the type of business.

Point-of-interest data, meaning compiling this information into a large data set, informs many diverse business processes. Also called place data, POI data usually derives from geographical coordinates. The locations can be permanent, like landmarks and buildings, or temporary, such as a music festival or pop-up restaurant.

Most POI data uses the North American Industry Classification System to identify industry sectors. Business, research, and government applications use NAICS codes. These can tell you if a listing is for a gas station, a retail store, a theater, or something else. When you look at a place listing, this standardized code helps quickly identify its purpose.

How Is POI Data Used?

Businesses use POI in myriad ways, from creating routes to analyzing the competition. POI data can be crucial to location-oriented operational tasks, particularly when combined with GPS tools and geofencing capabilities. Here are a few common ways different industries leverage POI data.

• Telecommunications: The telecom industry heavily relies on POI data to offer insights into service coverage and performance. POI data can inform these maps and provide details for new project decisions, such as where to place cellular towers.
• Finance and insurance: These risk-averse businesses often use POI data to assess different locations. When insuring a new building, they might look into how close it is to a flood zone or an area with high crime rates before determining pricing.
• Real estate: Since real estate depends heavily on location, POI data often informs decisions like where to build new developments or which listings might meet someone’s needs. For instance, a list of nearby amenities comes from POI data.
• Consumer applications: POI data informs many consumer uses, like navigation apps or finding nearby businesses.
• Marketing: Advertisers can use POI data to target specific audiences within an area by leveraging POI data and geofencing.
• Logistics and transportation: Transportation industry businesses rely on POI data to inform navigation tasks, like planning and improving routes. Most organizations with fleets use POI data, such as logistics providers, waste management facilities, and the postal service.
• Retail: Retail stores can use POI data to assess new expansions in a specific area, which can help with performance evaluations and trade area analyses.
• Governments and public services: Governments can also use POI data when evaluating services within an area. They might look for gaps in service offerings or areas where public infrastructure could improve.

POI offers exceptional versatility, especially if incorporated into sophisticated tools designed for these applications.

Where Does POI Data Come From?

With so many business-critical applications, organizations must consider POI data’s source to ensure its quality. Data providers can use varying methods to collect and verify their information. Typical sources include websites, government databases, and human input. These are often prone to errors, such as inaccurate website listings and out-of-date information.

POI data also varies significantly due to a changing landscape. Businesses might close or open, and some owners forget to update their contact details. Buildings and roads are frequently under construction, and areas could get rezoned. Consistent updates and verifications are crucial to ensuring data accuracy.

Addressing these challenges is one of the reasons it’s so vital to find the correct POI data provider. A partner must take steps to verify the information’s accuracy and continuously update it if they find errors or inconsistencies.

Things to Consider When Acquiring POI Data

Getting quality POI means working with a dependable POI provider. If you’ll be working with POI in business, you must consider several aspects of the data and how a company manages it.

• Accuracy and update frequency: Ask your POI provider how they keep their listings correct and current. For example, at dataplor, we use a proprietary system to collect near-real-time updates. In contrast, some companies only update their systems quarterly or annually.
• Verification strategies: Similarly, consider how the company verifies its listings. Look for methods like human verification, error detection systems, artificial intelligence, and machine learning.
• Global coverage: Depending on how you use POI data, you may need global information. Global place data can be particularly challenging to verify due to language differences and limited international resources for United States-based companies. Your POI provider should take special measures for collecting and verifying international data, like enlisting regional experts and using AI data collection. The provider can also simplify global data by using the same data schema for each country.
• Completeness: A data provider might support detailed attributes for each listing, but it doesn’t help you much if there are many blank spaces. Look for a provider with high fill rates to ensure thorough metadata and insights from each listing.
• Modern features: Technologies like AI and machine learning have transformed the world of data collection, and a POI provider should use them to help identify errors or collect information.

Dive Into POI Data With the Industry Leaders at dataplor

High-quality POI data is critical as the foundation of many tasks and business decisions. Some of the world’s largest companies use dataplor’s global, dynamically updated location data to make informed choices. We offer unmatched accuracy and coverage, backed by proprietary technologies and local human validation. Enjoy unique insights with qualitative metrics to learn more about consumer behaviors and brands worldwide.

Whether you need to outpace your competition, boost revenue, expand your services, or meet another goal, we can help. Reach out to a data expert today to learn more about dataplor!

Geographic information systems (GIS) have changed how companies from nearly every sector navigate the global market. These systems grant organizations increased competitive intelligence by enabling them to layer, map, manage, and analyze different types of data at the click of a button.

Among the greatest insights made possible by GIS platforms are those rooted in point of interest (POI) data. Having the right POI data is particularly important for two types of businesses: those that provide GIS navigation services (including Google Maps and Apple Maps) and those that leverage such services to make customers’ day-to-day lives easier (think Uber or DoorDash). But how?

In this article, we’ll answer this by first diving into how GIS mapping works. We’ll then discuss how accurate POI data helps companies increase customer satisfaction, avoid costly errors, and scale globally. Along the way, we’ll cover the true costs of bad GIS data and why dataplor is an industry-leading provider of location intelligence for GIS services of all kinds. 

What is GIS?

GIS visually layers one or more datasets so that users can analyze each in isolation or together. In other words, GIS platforms link these layers to a map by combining location data with various forms of descriptive data. 

These maps provide interactive business insights that raw data cannot generate on its own. GIS map layers allow organizations to see and toggle between different relationships, patterns, and geographic contexts found in their data. By analyzing GIS layers in this way, companies can unlock operational know-how and smarter decision making in real time. 

How does location intelligence support GIS platforms?

Though they enable users to visualize more than one type of data, geographic information systems run primarily on location intelligence. This data is the backbone of GIS mapping, since it allows these systems to feature layers focused on specific geographic contexts. 

For example, a GIS map could contain a polygon layer for France, an additional one for Paris, then yet another for popular tourist and shopping neighborhoods, such as the Champs Elysées or Marais. The same map might also contain additional layers that detail clusters for demographic skew for these sought-after destinations.     

From a business standpoint, this GIS data is crucial for capturing market share. Through it, users are afforded granular insights about points of interest, including their addresses, hours of operation, websites, and phone numbers. As a result, companies can use GIS platforms that contain POI layers to boost competitive advantage, operational savings, and customer satisfaction.   

To better understand this, let’s zoom out to consider what GIS providers themselves stand to gain with POI data. GIS mapping companies such as Apple Maps need up-to-date POI layers so that users can access and navigate their way to and from any point of interest, including restaurants, shops, parks, and other popular landmarks. 

Imagine, for example, that a tourist using Apple Maps wants to visit a new streetwear brand’s flagship store on the Champs Elysées after a morning of site seeing around the Arc de Triomphe. If the platform has an accurate POI layer, they’ll be able to find reliable transportation to the store and arrive at a time that they know it to be open. Each experience like this leads to repeat use and supports consumer confidence in the GIS provider.

Companies that rely on GIS mapping and navigation also benefit from POI integration—so much that they’ll pay premiums for access to platforms that run on the right geospatial data. Remember our tourist? Hungry after a day of sightseeing and shopping, they decide to order from the hippest restaurant in the Marais using UberEats. Whether the app is relying entirely on another GIS or has integrated additional POI datasets to optimize its algorithms, customer satisfaction hinges on data accuracy: the courier will need the correct addresses, hours of operation, and phone numbers to make sure that the meal delivery is executed seamlessly. 

What are the costs of bad GIS data?

Unfortunately, not all data is created equally. While free or out-of-the box data solutions might be less expensive, they’re often out-of-date and lead to unexpected spending down the line. POI datasets that are missing address details, contain inaccurate details about hours of operation, or suffer from duplicate records need to be enhanced if they’re to be reliably integrated into GIS platforms. And on top of all that,  much international POI data is simply inaccurate.

The results can be disastrous when GIS platforms integrate bad data. Imagine that Apple Maps has the wrong Parisian arrondissement listed for the streetwear brick-and-mortar on the Champs Elysées or incorrect hours of operation for the restaurant in the Marais. Errors like these cost time, money, and have the potential to do irreparable damage to brand image and consumer confidence. For GIS providers or the companies that rely on their services, these consequences might also stifle efforts to expand globally. 

Mapping international growth with dataplor

To avoid these costly errors, it’s important to only use POI data from vendors that 1) specialize in POI data, 2) streamline their places datasets using multiple sources, 3) provide metadata and other indicators for every record, and 4) know the value of local sources.

Thankfully, dataplor checks every one of these boxes. As an industry leader in location intelligence, we offer best-in-class POI data that GIS companies of all kinds can mobilize to gain truly global competitive intelligence. That’s because we use a winning combination of technology—including proprietary AI and machine learning—along with human verification to ensure that your mapping and navigation is always accurate.

Ready to take your GIS mapping to the next level with POI data? We’d love to hear from you!  

Many businesses use Google Places API to request location data and imagery about points of interest and other locations. The appeal is obvious; with products like Maps and Earth, Google has insight into location data around the globe. But for businesses hoping to use location data to expand, refine ad targeting, or conduct market research, Google Places API is not as cost-effective or accurate as it seems. 

Many users quickly discover that Google Places API is more costly and restrictive than they’d hoped. In fact, when you dive into the details of Google Places API pricing, it’s clear that this solution is in fact a barrier to developing location intelligence at scale.

 To help you make the best decision for your needs, this article will focus on the benefits of location intelligence, how Google Places API pricing works, and the true costs and restrictions of Google Places API. We’ll then dive into alternatives to the Places API.

The benefits of location intelligence

Companies can capitalize on geospatial insights regardless of their industries. From third-party logistics to quick-service restaurants and real estate, location data is fueling growth for businesses of all sizes and types. This data enables companies to conduct more comprehensive market research and optimize upstream supply chain opportunities and expansion plans. It also opens the floodgates for highly targeted and effective advertising.

When researching site selection, location intelligence reveals the physical footprint of competitors in any given market, as well as point of interest (POI) data that aids in the evaluation of complementary businesses or attractions that could generate customers. To increase the odds of acquiring these customers, companies can use location data to create geo-targeted mobile advertising campaigns and purchase out-of-home (OOH) ad space to deliver relevant impressions.

The hidden costs and restrictions of Google Places API 

‍

With so many tantalizing business use cases for location data, companies are eager to start collecting and acting on it. One popular platform is the Google Places API, which seems on the surface to be an easy, cost-effective way to unlock the benefits of geospatial insights. However, the Places API has significant weaknesses—and hidden costs.

Users of the Places API receive a $200 credit each month toward their requests. According to Google Places API pricing, that’s equivalent to 28,500 maploads per month. But not all queries are created equal, as the Places API uses a stock keeping unit (SKU) system to classify different services and their corresponding rates. As a result, companies can rack up bills that are much higher than the monthly credit, depending on which type of Google Places API data they’re accessing—maps, routes, or places.

Because market research often requires multiple calls to the API to collect the full scope of required information, companies in the midst of expansion are most at risk of overspending on the Places API. For example, Passenger Coffee, a regional brand in central Pennsylvania, could plan to expand with new distribution partners and a physical cafe in Philadelphia. Passenger’s team would likely be searching for information beyond basic place attributes, such as contact information or operating hours—all of which would require individual calls to the API. If they use the Places API to pull POI datasets for research on site selection, competitive presence, and potential distribution partners, they could quickly end up exceeding the $200 monthly credit. This could prove challenging for smaller brands, where every dollar counts.

Additionally, limitations of the Google Places API can make this research even more challenging. Storing data in any capacity for more than 30 days is a violation of Google’s terms of service (TOS). So, the monetary investment could be significant for a smaller CPG brand, yet the data they collect will vanish quickly in the shadow of the Google TOS. Similarly, tech giants like Google have the power to change their licensing at will. This leaves companies at the mercy of a large corporation, adding an unnecessary element of risk to any strategy driven by Google data.

On a more technical level, data from the Google Places API can’t be used to build iterative data products, and cannot be transferred for use in other company initiatives. The service doesn’t account for duplicate records nor does it provide tailored customer service for the Places API product. While this API is a step up from open source data that is often outdated and rife with errors, unpredictable charges and limitations to scalability makes the Google Places API a poor fit for many businesses. 

Trusting dataplor as a partner for truly global intelligence 

‍

The point of this article is not to rake Google Places API over the coals. Google is a trusted data source for millions of users around the world. But it’s simply not the best fit for companies that need high volumes of accurate, comprehensive location data, and a more tailored customer experience as they gather global intelligence. 

dataplor is in many ways the opposite of Google Places API. Our data is meticulously curated and vetted for accuracy by humans around the world. Global POI datasets updated in near real-time ensure that companies are getting their hands on the most up-to-date and enhanced insights as they pursue expansion plans. 

Companies may be able to extract raw location data from Google, but only dataplor can categorize U.S. and international locations by type, such as restaurants and hospitals. This includes 13,000 brands and chains identified by 35 unique attributes in 5,200 categories. dataplor POI datasets contain 200 million POIs in more than 200 countries and territories.

Our competitors have a narrow focus on the U.S. and Canada, but dataplor’s capabilities are global — that’s always been our identity. The extent of our solutions-focused services, our commitment to working with customers on their datasets, and the quality of our international data make dataplor the singular global partner for location intelligence. 

One of the most pressing challenges for companies hoping to capitalize on location data is data quality. A dataplor analysis of open-source Mexican data, for example, found that more than 70% of point of interest records contained inaccuracies. For example, a business’ record might have included an incorrect address or multiple different addresses. These inaccuracies may sound minor, but at scale, they can lead to poor decisions that cost companies using location data for logistics, site selection, and advertising millions of dollars.

This is why the most rigorous location data companies don’t just use machines to collect data at scale; they also use machines — and people — to verify it. Dataplor, for example, hires local experts to further validate the accuracy of data that has been collected by AI call bots and deduplicated with machine learning.

But what exactly does human validation add to the location data collection and verification process? What is the industry standard, how does human validation exceed it, and for what sort of scenario is human validation most useful?

Here’s how human validation helps dataplor provide the most accurate possible POI data.

‍

How most location data companies collect information

Businesses and third-party providers are increasingly relying on geospatial intelligence to power their predictive modeling, expansion plans, and evaluations of market trends and competition. Location data companies provide this placed-based intelligence by compiling location information from a wide range of sources. This includes anonymized and aggregated data gleaned from mobile devices, applications, and POS and ad services. The result is rich datasets available for a wide range of potential use cases.

Across the industry, location data companies employ machine learning technology and AI to identify and analyze location data. Oftentimes, companies advertise the fact that they also engage human capital — but the industry standard for doing so isn’t always clear. 

Relying too much on human capital can result in overexposure to human error; in other cases, human validators add wasteful, imprecise, or inefficient complications rather than bolster existing tech. This can happen when companies neglect an enterprise approach that quickly and effectively integrates human capital with ML and AI processes and emphasizes consistent data management standards. 

In other words, human validation can be an essential part of the location data quality enhancement process. But most companies use it minimally or optionally. For example, they might allow academics to use their data for free and point out issues and errors. But this isn’t a proactive approach; it relies on the possibility that academics will find mistakes and correct them. Other companies hire a very select group of people to walk a small area and gather on the ground data. But that data is often interpolated to other areas, which is a highly assumptive, inaccurate approach.

A better approach would be to start with scalable, high-coverage, high-accuracy data and improve it even more with experts who are employed directly to systematically upgrade it. 

‍

Why dataplor uses human validators

Technology drives 90% of dataplor’s approach to data collection, and human capital supplies the final 10%. What does this look like, and why does it lead to data that is more accurate and usable at scale? 

In short, human capital plays the essential role of fine-tuning tech-based data collection, ensuring that information is consistent and accurate. For example, many countries have different standards for information like zip codes, phone numbers, and street names. Plus, translation issues can lead to inaccuracies in data, like when an AI analysis might mistake a grocery store for a hardware store because of differences in local language or dialect. Dataplor’s human validators anticipate these issues for their local areas and use proprietary tools to fix inaccuracies and tag data based on its quality. 

The result is a combination of the power of machine-driven data collection at scale with the knowledge and innovation of local experts to ensure quality.

How human validation improved the accuracy of cafe identification in Japan 

For an example of a human validator increasing data accuracy, take Nel Ferrer, a regional operations manager at dataplor. Nel came to dataplor after working in the tech and finance industries, where he specialized in cross-cultural collaboration among data-oriented teams. He runs a multicultural group of validators focused on POI-related issues. One of their key contributions, Nel says, is “ensuring that AI is correctly tagging information in different places and that this information is 100% correct.” Specifically, much of his team’s current focus is on “how local culture affects the POI address structure,” which they do by providing what he calls a “human touch” that makes sure that AI is “perceiving the environment as consistently and correctly as possible.” In this way, Nel’s validators are quite literally AI’s eyes on the ground. 

Nel’s role also includes training and auditing the ongoing work of his validators. Training is one on one and walks validators through the process of understanding what to expect and how to handle various scenarios when in the field. Nel works in constant communication with his team to answer questions and solve problems as they arise. He also provides an additional level of review to the data collection and enhancement process by reviewing his team’s work weekly on an individual level. For example, he’ll choose five to ten random POI locations and make sure they are correctly tagged and free from duplications. 

Asked about a recent instance where his team was able to fix an inaccuracy, Nel recalls an example in Japan, where cafes with the word “cat” (“neko”) in their business name were being mislabeled as pet stores. 

This kind of advanced training of and detailed fixes from human capital add up to accurate, trustworthy, and actionable datasets. Location data can be consistently tagged and cross-checked, and automated identification processes can quickly correct for mistakes via validator feedback. 

The result is location data with global reach and local distinction. Dataplor’s approach to leveraging the additive benefit of human validators like Nel and his team make it possible to provide on-point geospatial intelligence at the scale that international businesses need.

There has never been a better time to capitalize on mobile marketing. As of this year, 86% of the global population uses a smartphone—a whopping 6.92 billion people. In the United States alone, Americans spend an average of nearly five and half hours per day on their mobile devices. 

Advertisers poured $336 billion into mobile ads last year, feeding a booming industry and buying access to an unprecedented level of consumer targeting. To realize the potential impact of mobile ads, siphoning location data from individual devices and their proximity to points of interest (POI) in the outside world is an essential requirement. In fact, location data underpins the entire mobile marketing machine. 

For brands who are looking to reach highly targeted audiences on a global scale, we’ll explore different forms of mobile advertising and the role location data plays in making them all possible. In particular, we’ll focus on the location data-driven mechanisms of geofencing marketing, covering its benefits and use cases for mobile advertisers.

What is geofencing marketing?

Mobile advertising offers targeting on a granular level by displaying advertisements on a consumer’s smartphone or other personal devices. Operating on data extracted from a consumer’s mobile device, this form of marketing allows advertisers to connect with their target audience based on signals that include a consumer’s behavior and geographic location. This individualized targeting ensures that advertisers are reaching the right people and optimizing their ad spend.

There are myriad ways to reach consumers on their mobile devices, including display, interstitial, native, and video ads. Depending on the app, consumers may also be served in-app advertisements. However, location-driven advertising takes mobile ad impact a step further. Geospatial insights enable companies to deploy geotargeting, geofencing, and even geo-conquesting campaigns that can attract new customers and boost return on ad spend (ROAS). 

Geotargeting allows advertisers to reach consumers based on the location of their mobile device. This targeting occurs on a high level, such as the device’s city, zip code, or IP address. It is a more general approach to location-based advertising that helps advertisers target consumers within a specific market. It’s important to understand that geotargeting and geofencing are not the same.

Geofencing advertising is an approach to mobile marketing that is confined to spatial boundaries in the physical world, placing an invisible perimeter around a specific point of interest (POI) and serving ads on devices within it. Similarly, geo-conquesting uses spatial boundaries around a competitor’s location to target devices—which belong to customers—within a given perimeter. 

In the case of geofencing marketing, POI data is critical. POI is a specific category of location data that reveals information about physical places, including stores, restaurants, and landmarks. This helps advertisers build footprints for their target audience, identifying POI that are relevant to their brand or product and running ad campaigns nearby. 

How geofencing benefits advertisers and brands

Advertisers benefit from geofencing by delivering messages to the consumers they want to reach, based on that consumer’s proximity to a physical place. For example, a CPG brand could use geofencing to serve ads to shoppers in a grocery store. This ensures that ad spend is being dedicated to consumers who have a greater likelihood to convert. 

Given the current state of the economy, some advertisers may need to run campaigns to offload excess inventory. A fashion retailer may decide to run a geofencing campaign within the spatial boundaries of their store, giving a discount or limited offer to customers in real-time and helping to sell the overstocked product. 

Additionally, geofencing can help advertisers outperform competitors by way of geo-conquesting, which applies the principles of geofencing to a competitor’s physical location. Through geo-conquesting, brands can serve ads to potential customers whose devices are seen in proximity to a competitor’s space in hopes of winning their business with tantalizing promotions. 

How to make the most of geofencing

If Popeyes Louisiana Chicken, an American quick-service restaurant (QSR) chain, decided to run a series of geofencing advertising campaigns in southeast Asia, gathering location intelligence would be an essential first step. This includes POI that its target customers might frequent—for example, a shopping center in Jakarta, Indonesia—as well as competing QSR locations in the larger region. 

However, knowing where these complementary and competing POI are located would not complete the puzzle for Popeyes. The QSR chain would also be interested in evaluating activity around these POIs over time, helping them understand which geofenced areas to prioritize for ad spend.

By gathering this location intelligence, Popeyes increases the likelihood that their geofencing advertising campaign will yield the desired results—generating new or repeat customers, and even luring customers away from their competitors. 

Let dataplor unlock tailored data and advertising 

While geofencing appears at the surface to be a no-brainer marketing strategy, its success hinges on the quality of the location data informing it. This is especially true for international markets, where POI data is notoriously inaccurate, and for companies who rely on open source data, which is susceptible to errors and outdated information.

Circling back to Popeyes’ plans to run geofencing campaigns in southeast Asia, low-quality location data could result in thousands, if not millions, of dollars in wasted ad spend. For example, If Popeyes thinks it is targeting a series of American retail brands who share common customers with the QSR chain, but those physical retail stores shut down months ago, Popeyes is paying a hefty price tag to target data that doesn’t exist.

Fortunately, there is a solution for brands who want to shore up their investment in geofencing and mobile marketing. dataplor serves as a global location intelligence partner, using polygons that are more in tune with client demand and tailored to customer needs. These polygons are drawn using dataplor’s proprietary AI and machine learning approach, making them both scalable and competitively priced.

Additionally, dataplor offers the most accurate international POI data on the market, positioning the platform as the only industry player that can provide truly global polygons for geofencing and geo-conquesting campaigns.

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.

dataplor Partners with StateBook to Provide Comprehensive Economic and Location Data for Every US Community

dataplor, the top global geospatial data company specializing in points of interest, and StateBook, the leading provider of comprehensive, visualized economic market insights in the United States, are partnering to provide their customers with even more powerful location intelligence. 

Retailers, CPG brands, financial institutions, tech companies, third-party logistics providers, and more rely on dataplor to understand where competitors and complementary businesses are located, select new sites, and identify markets ripe for expansion. Thanks to the StateBook partnership, dataplor will provide more users with tailored datasets that unlock insights into strategic investment, project viability, and risk mitigation across multiple geography levels and over time.  

The partnership with dataplor enables StateBook to add best-in-class POI data to its existing complement of  32 billion data points, enabling its customers to access insights and trends around the nation’s workforce, wage rates, industries, infrastructure, taxes, demographics, and more. 

“The partnership with dataplor equips our customers with the deepest, most accurate geospatial view of US markets available,”

said Calandra Cruickshank, founder and CEO of StateBook,  adding that StateBook also recently added real-time mobility insights from cell phones to its arsenal of data, along with risk, resilience, and impact data. StateBook’s customers span real estate investors and developers, Fortune 500 companies, government agencies, insurance companies, and other technology companies. 

“We’re excited to formally kick off our partnership with StateBook,” said Geoff Michener, dataplor CEO and co-founder. “POI data has never been more relevant to growth as businesses across verticals aim to identify the surest possible paths to expansion in a tough macroeconomic environment. With dataplor’s POI data and StateBook’s socioeconomic data insights, brick-and-mortar businesses will be able to confidently select the sites where they are most likely to grow.”

dataplor sets itself apart from other geospatial data companies through its exclusive focus on POI data and its industry-leading data verification process. Much location data is inaccurate, owing to poor open-source data verification methods and differing records across sources. dataplor employs machine learning to deduplicate records, AI call bots to confirm local business information, and local human experts to provide clarity in ambiguous cases. 

Over the past year, dataplor has grown to cover more than 100 million POIs in over 200 countries and territories. Its coverage of POIs in the US is second to none. 

As the leading provider of trusted socio-economic data for location intelligence, StateBook enables its customers to gain information advantage with actionable insights. Fortune 500 companies, real estate investors, banks, insurance companies, and communities use StateBook data to identify the most strategic opportunities for investment, reduce risk, increase resiliency and optimize for success. StateBook also licenses its data to enable technology companies to deliver timely, flexible, intuitive market data to their customers.

For companies that want to scale globally, having the right data is crucial. But inaccurate insights often come with a hefty price tag. Years ago, for example, IBM estimated that bad data was costing businesses upwards of $3 trillion per year. A more recent study by Gartner reports that companies lose around $12.9 million annually due to flawed analytics.

These figures are a wake-up call for organizations looking to cash in on location data. While these datasets provide global insights about consumer buying habits, market trends, and competitor activity, they’re often inaccurate—particularly on the international level.

To harness the power of geospatial data at scale, it’s important to have records that are updated regularly. Unfortunately, it’s difficult to find data providers that do so. Let’s dive into how and when location data providers tend to update their datasets, the benefits of data that’s updated in near real-time, and how dataplor provides its customers with just that.

How and when do location data providers update their records?

While inexpensive or free open-source location data may seem like a good deal for companies upfront, the cost of using inaccurate, biased, or outdated data sources can be astronomical. The US Census Bureau, for example, runs the census only once every 10 years. Similarly, open-source data source OpenStreetMap has succumbed to corporate bias in its data. These issues create significant risk for companies who are using these sources to make critical business decisions.

While governments provide a valuable public service by gathering and publishing data about their constituents, the slow-moving wheels of bureaucracy simply cannot meet the needs of fast-moving, rapidly growing companies today—at best, these providers update their data every 90 days. Relying on location data from public providers is an unreliable route for businesses. 

On the other hand, openly-sourced data can be updated in real-time by “volunteer” contributors. However, exclusively collecting user-generated location data leaves these sources vulnerable to inaccurate and biased information—especially in international markets. In tech, the expression “garbage in, garbage out” refers to the reciprocity of input and output: if flawed information goes in, then the natural result is that flawed data will come out.

This doesn’t mean that open-source data providers can’t fix inaccuracies and adjust for bias. They can—but it isn’t a quick process. These providers tend to update faulty data manually, which proves a timely and labor-intensive effort that, again, cannot keep pace with the demand of fast-growing companies. Coupled with the potential for a significant loss on investment based on low-quality data, companies should not lean on open-source data providers to make important decisions.  

What are the benefits of data updated in near real-time?

Companies need to strike a balance between the frequency of location data updates and the quality of that output. This can be achieved through the use of location data that is updated in near real-time, made possible by a geospatial data provider that offers both comprehensive collection and rigorous verification processes.

When armed with location data that is updated in near real-time, businesses can unlock accurate competitive intelligence about customers and competitors, make smarter site selections, and develop targeted marketing. The key is point of interest (POI) data.

A POI is any physical site that might be of interest to individuals, companies, and decision makers, including brick-and-mortar stores, restaurants, and malls, but also national parks, monuments, and other landmarks. POI data helps companies see a clear picture of the market landscape in the area they’re targeting.

For example, Chik-fil-A is investing $1B into market expansion in Europe and Asia. Each market is unique by country, region, and city, and Chik-fil-A needs up-to-date, accurate location intelligence to evaluate competitors and market opportunities, as well as make decisions about site selection. 

Let’s say that, as Chik-fil-A homes in on expansion plans in Spain, the company believes that the Catalonia region and, specifically, the city of Barcelona will be a strong market for a QSR location. The expansion team knows that buildings designed by architect Antoni Gaudí draw large numbers of international tourists. 

The team wants to choose a nearby site that will bring in the most business. However, they need to evaluate the presence of competing QSRs and their proximity to the POIs that Chik-fil-A is targeting. The team can’t learn about the footprint of their competitors through 10-Ks, as no company publicly shares its sales data, nor about how those competitor QSRs are growing over time. Location data, however, makes it possible to compare similar—and highly accurate—location datasets over time, allowing the team to estimate how many people are visiting a competing location each month. This intelligence paints a picture of the QSR market landscape in Barcelona and enables Chik-fil-A to be more strategic about site selection.

Additionally, POI data has beneficial applications for Chik-fil-A’s marketing team. By discovering areas with tourist attractions, shopping, and other relevant landmarks, Chik-fil-A can better identify opportunities for out of home (OOH) advertising, such as billboards or digital signage, as well as geo-targeted digital advertising. Location data helps the marketing team carve out geographic sections of Barcelona where their advertising could be most effective. 

What makes dataplor’s process and product so unique?

Opening new QSR locations in multiple international markets is a large and expensive feat. Companies like Chik-fil-A can’t afford to hedge a billion-dollar bet on location data that is low-quality, out of date, or flatly incorrect. Because international location data is notoriously unreliable, companies should partner with a trusted geospatial data provider to get the insights they need and increase their odds for success. 

dataplor’s enterprise-level approach excels beyond industry standards, factoring in the needs of rapidly growing companies and the demand for high-quality, verified location data. dataplor’s data collection and enhancement processes are based on a machine learning observation system, which actively monitors records to detect changes to a business’ physical address, hours of operation, phone number, website, and more. 

Additionally, dataplor is the only geospatial company that specializes in international markets, employing a global network of human validators in more than 200+ countries and territories. Using best-in-class tech to optimize global data collection and enhancement, and leveraging humans for last-mile verification, dataplor offers the most accurate and up-to-date location intelligence on the market.

If you’re ready to scale your business globally, find out how you can use location data to ensure your success in international markets. Contact us today at contact@dataplor.com.