Unprecedented Access to Over 200 Million POIs in 200 Countries & Territories for Comprehensive Places Data

dataplor, the leading provider of Point of Interest (POI) data for enterprises worldwide, has announced its latest expansion, with coverage now extending to over 200 million POIs across 200 countries and territories. This is the most extensive coverage offered by any provider in the industry.

dataplor’s success lies in its multi-step data collection and verification process. By working with local experts to vet geospatial data, the firm closes gaps in machine errors with boots-on-the-ground verification. dataplor’s frequent and dynamic updates enable users to gain improved visibility into poorly mapped business geographies that are not accessible through other providers. 

Empower Your Data-Driven Decisions with Accurate Location Intelligence at a Global Scale

Accurate and comprehensive POI data is essential for organizations seeking to expand their business operations in developing markets. This data allows businesses to make informed decisions about site selection, optimize their operations, tailor their marketing strategies to specific audiences, and identify opportunities for expansion in new markets.

Tracking places attributes such as the location, open close status, operating hours, classification, dwell times, and more, dataplor paints a picture of hyperlocal markets to reveal broader trends. POI data can uncover holes in local brick-and-mortar economies, highlighting regions where retailers or consumer packaged goods companies can increase sales and marketing efforts. POI data also reveals which chains and brands are growing or receding in certain areas, painting a clear picture of global competitive intelligence.

dataplor is putting businesses on the map in international markets and lighting the way for companies looking to establish themselves abroad. The world’s largest tech, CPG, and financial services companies rely on dataplor to understand global markets by processing comprehensive data, which leads to a better understanding of the target area and enables more effective market analysis.

Sustainable Expansion with Uncompromised Data Quality: dataplor’s Strategy for Continuous Growth

With its comprehensive coverage and meticulous upkeep, dataplor is set to continue growing while maintaining its position as the industry leader in POI data. The company plans to extend its coverage by 20% by the end of 2023, ensuring accuracy across all locations with the support of an expanding team. dataplor’s commitment to maintaining accurate information in diverse markets is evident in its success, with the world’s largest tech, consumer packaged goods, and financial services companies relying on its data for global growth strategies.

Location data is one of the most powerful tools businesses can use to gain insights into customer behavior and make informed strategic decisions about logistics, site selection, advertising, growth opportunities, and efficiency. But location data can only make a difference when it’s accurate and on point. 

Poor-quality location data can lead to missed opportunities, wasted resources, and incorrect insights. In this post, we’ll discuss the three essential criteria to consider when evaluating location data quality, so that you can confidently use location data to drive your business forward.

✓ Credibility: How credible is the data you use? Location data can fail to meet this criteria in a number of ways, from data collected without proper consumer consent to data marred by collection devices or processes with technical issues. The key here is to know a) if the source of the data is credible and b) if the processes used to collect it are trustworthy.

✓ Quality: Are you using high-quality data? It’s vital to make sure that the data you use has high confidence scores, is free of duplications, and is up to date and accurate. 

✓ Usability and applicability: Are you confident assessing what your data can (and cannot) tell you? Do you know where and how to leverage it to drive growth? The essential point here is to identify where location data can boost your business and what the best practices are for evaluating what it’s telling you. 

Let’s take an in-depth look at each of these criteria and see how they can help your business leverage trustworthy and accurate location data.

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The importance of credible data

Data credibility is all about trustworthy sources and methods. For example, if data is not anonymized or protected appropriately, it may be compromised, leading to data breaches or misuse. Given this, how can you be sure that your data vendors are engaging in best practices by protecting consumers’ privacy and avoiding biased or cherry-picked datasets?

One way is to verify your data vendor’s privacy practices by ensuring that they are transparent about their collection sources and methods. For the datasets in question, have consumers been asked for their consent to be tracked? Some mobile apps, for example, may collect location data in the background without explicitly informing users or receiving their consent, potentially exposing sensitive information about their daily routines and behaviors and raising legal concerns. 

Moreover, data privacy can become a heated public issue that puts pressure on vendors. As consumers become increasingly aware of the potential risks and ethical concerns associated with location data, they are more likely to scrutinize the practices of location data vendors and demand greater transparency and accountability. This scrutiny, in turn, can lead to media attention and regulatory action. 

For these reasons, businesses should check that their data vendors employ ethical and transparent standards and engage in best practices to ensure that they have a data partner they can rely on. 

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The value of high-quality data

Credibility is about assessing the trustworthiness of the data source and its methods; data quality is about the caliber of the datasets themselves. Are there errors, duplications, or inaccuracies that can lead to problems down the road, like showing a distorted picture of a city or neighborhood? 

One area where quality can be a big issue is international location data. For example, consider the case of a QSR that is looking to expand from the U.S. to Mexico and turns to geospatial intelligence to inform its decision-making. As the QSR looks for sources of data, some important questions to ask include: does the data come with metadata, confidence scores, or similar indicators? Does it contain inaccuracies and need to be enhanced? For instance, when dataplor recently sampled INEGI records for Cancún, we found that 91% contained some form of inaccuracy. Only by catching these errors ahead of time could the QSR avoid wasting time and resources. 

While it’s possible for companies like the QSR to fix this data themselves, it’s usually more cost-effective to buy accurate data upfront. One way that dataplor ensures the accuracy of its international data is by using trained human validators with local expertise to work in tandem with its AI processes. Validators anticipate and catch issues like cultural differences in address structure or language variations like wordplay in business names. This real-time, on-the-ground feedback mechanism enables dataplor to identify duplications and errors, enhance their data, and offer clean and actionable datasets for business to use and realize results out of the gate. 

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The power of actionable data

Once you’ve established that your data is credible and high-quality, the final step is determining what you can — and cannot — do with it. It’s essential to have processes in place, and/or to work with a data services provider, who can help you understand what the data is actually telling you, so that you can leverage it for maximum effect. 

For example, let’s consider a retail company that is looking to expand its business internationally. They have access to data from their existing stores, but is this data of the appropriate scale? Are there enough data points to draw meaningful conclusions about consumer behavior and preferences in a new market? Additionally, consider whether the data provides global coverage. A business looking to start a new location in the Palermo neighborhood of Buenos Aires needs to be sure the datasets it’s using accurately reflect the area. The business can’t just rely on guesses about international locations based on more complete data in markets like the US and Canada that are easier to cover. 

Furthermore, companies need to be honest about their goals for using data. What do they hope to achieve with the data? Do they need additional datasets to complete their insight puzzle? For instance, a retail company might have point-of-interest (POI) data, but they may require additional mobility and demographic data to make optimal decisions about site selection for new stores. By understanding what the data can and cannot tell them, and what additional data they might require, companies can make better-informed decisions that facilitate growth at scale.

Location data is one of the most valuable tools a business can have to shape decision-making about the future, map the competition, and find or enhance pathways to growth. But much of the location data available for purchase is of poor quality and can be more damaging than helpful. By evaluating the credibility, quality, and actionability of location data, companies hoping to use it to grow can set themselves up to do just that.

dataPlor will join hundreds of the world’s top CPG brands, retailers, and technology providers at the 2023 Shoptalk Conference in Las Vegas, NV. Shoptalk, which will be held at Mandalay Bay from March 26–29, brings more than 10,000 industry professionals together to build relationships and learn about the most exciting innovations in retail.

While e-commerce continues to thrive after its explosive growth during the pandemic, in-person retail is still more deeply ingrained in day-to-day consumer life. According to research compiled by Zippia, nearly 40% of consumers make at least one in-store purchase per week, compared to 27% who make an online purchase. 

As brick-and-mortar shopping continues to dominate, dataPlor enables brands and retailers to create geo-targeted advertising experiences; discover beneficial brand-retail partnerships; conduct regional, national, and international site selection research; and gather competitive intelligence through the power of location data. 

Location data provides insight into consumer behavior, points of interest (POI), and market competition that both brands and retailers need to make critical business decisions and design high-ROI advertising campaigns. From a smaller, regional company to a top 10 international brand, location data can help any CPG or retail business grow at scale by enhancing market research, advertising, and expansion strategy. 

An apparel retailer would need precise location data to grow abroad, for example. They’d need to know where customers, competitors, and other supply chain partners are—and to gain this knowledge, they’d need international data that is comprehensive, accurate, and up to date. 

In this example, dataPlor’s POI data can provide a clear picture of both the client and their competitors’ presence in international markets, help them identify new distribution channels, and enable them to allocate resources to gain market share and decrease waste.

dataPlor would love to meet with Shoptalk attendees who are curious about using location data to scale their retail operations. Stop by dataplor’s booth #1719, or pick a time that works best for you to meet with us at Shoptalk.

By Geoff Michener, CEO and co-founder of dataPlor

As the martech and customer data industries have grown, it has become more challenging to differentiate one business from another. Go to the website of any martech or data firm, and you’ll find similar claims of actionable insights and powerful technology. How are buyers supposed to tell solutions apart?

The same applies to the location data industry, where it is common to claim to provide global scale, granular insights, and top-notch precision. But surely, not every location data company can cover every market and every type of data asset in that market. Every company has strengths and weaknesses. So, what really differentiates one location data company’s strengths from another’s?

Three capabilities for which buyers should vet location data companies are technology that can scale, validation of data quality, and the combined use of human and artificial intelligence to supply the most accurate possible information. Here’s how buyers can think about each of those criteria when evaluating location intelligence solutions.

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Technology that can scale

Many, if not most, location data companies pledge to provide international, if not global scale. But how do buyers evaluate these claims, kicking the tires to determine whether the firm actually can provide high-quality data across diverse markets?

First, buyers should question what location data companies mean when they say they have data across markets. What kind of data? Is it mobility data purporting to show where people are? If so, what does the company know about those people? Can it combine mobility with demographic information? Does it have permission to share their data? Is the data anonymized but still actionable?

If the firm offers places or point of interest data, what can it tell the buyer about those places? For example, let’s say a company claims to be able to show all the Taco Bell locations in Mexico. Is its data limited to that one company, or can it show complementary locations such as competitors and suppliers instrumental to the QSR business? How does the firm’s location intelligence open up growth opportunities?

Finally, what proof does the company have that its data is accurate in a given market? Acquiring data on places or people in international markets is feasible; verifying it is considerably harder. This brings us to the second major question those evaluating location intelligence services should be asking vendors.

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Validation of data quality

As much as 70 to 90% of international location data is inaccurate. While the US has fairly extensive local business information, data in most other countries is not nearly as comprehensive. On top of that, location data vendors are incentivized to chase scale, which can lead to short-changing buyers on accuracy. To that end, buyers need to be on the lookout for low-quality data, especially when faced with guarantees of global scale.

One sign that a location data company is being honest about the accuracy of its data is that it supplies confidence scores to indicate just how sure it is of the quality of different types of data across markets. For example, location data quality is likely to vary from developed to developing markets; how long a vendor has been in a market could also be a factor. If the company can tell you how certain it is about a given data set, that’s a sign that the firm is doing the work required to help you make informed decisions and wrestle with the uncertainty that comes with data-driven decisions.

Another factor to consider is whether the company has external validation of the quality of its data. The firm should have past clients who can attest to superior quality in international markets. Here, it is important to distinguish between claims about data quality in developed economies and those abroad. Mapping the US and Canada with a high degree of accuracy is relatively achievable; doing the same in Latin America and Southeast Asia requires a different level of verification, more extensive partnerships, and local expertise. Vet the provider on each market, and don’t assume quality in one assures it in another.

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Combination of human and artificial intelligence

Buyers should also evaluate the verification techniques location data providers employ. Some vendors rely too much on manual processes, surveys, and academics, introducing an unnecessary amount of human error. Others rely only on public data gathering with no confirmation from local experts. The most effective approach strikes a balance between humans and machines.

For example, dataPlor, which specializes in point of interest or POI data, takes a data-rich approach whose accuracy is amplified by last-mile human verification. 

The company deploys AI call bots that call businesses in local languages to capture and check information. It uses machine learning to deduplicate and shore up the accuracy of data as well as deep learning image recognition to process data like store signs for another layer of verification. Finally, dataPlor employs local human experts in every one of its some 100 national markets to provide final verification. 

Buyers of any complex tech-driven solution know that vendors sometimes make claims that are too good to be true. This practice is rampant in location intelligence, where the imperative to scale globally leads many providers to claim to offer data on markets where they cannot ensure top quality. By focusing on not only quantity but also quality and interrogating providers’ data verification processes, organizations in the market for location intelligence solutions can be sure that the insights to which they are purchasing access are as premium as data providers promise.

By Geoff Michener, CEO and co-founder of dataPlor

As people the world over slowly return to their respective new normals, they’re once again on the move. Whether headed downtown for their first concert in years or finally taking that postponed vacation abroad, consumers are relying on and generating invaluable location data. To get to that concert, for instance, they could use Uber, whose app is powered by location data. After getting dropped off, they might open Google Maps to search for a highly rated dinner spot in the vicinity, a move made possible by point of interest (POI) data.    

 But these user experiences are only half of the story. Location data isn’t merely something that we can toggle on and off with our iPhones. For business development and marketing professionals, it’s the key to unlocking growth.

Certainly, companies like Uber and Google provide intuitive examples of how this data can be put to use. Less apparent, however, is how it can personalize the user ad experience, enable a smarter approach to site selection, and serve as the basis of engaging, location-based entertainment. Let’s think about how these perhaps less obvious use cases illustrate the value businesses can generate from location data. 

Personalize the customer’s ad experience

For marketers, location data is among the most direct means of nurturing relationships with customers by providing relevant and helpful information. For example, taking this route allows marketers to serve consumers with relevant out of home ads that result in less spend and higher ROI. This creates a more fulfilling experience by providing consumers with tailored offers at the perfect moment.   

What are the approaches that a company can take to location-based advertising? Location data can drive mobile and geotargeting, geofencing and geo-conquesting, or beacons and proximity marketing to drive customers away from the competition. They can use digital or traditional OOH inventory, including billboards, vehicle signs, and digital screens in elevators and other locations, to reach those customers.

Let’s say that the big-box company Target wants to send ads to users of its proprietary app that point them toward a nearby store. With mobile targeting, these ads can be sent directly to consumers’ devices and made context specific. Relatedly, geotargeting can determine users’ locations and serve them messaging accordingly. These options, for example, could empower corporate to run a sale on AC units during a heatwave and to serve related ads to customers in affected regions that direct them to convenient points of sale.  

Location data is also an effective tool for driving customers away from the competition. With mobility and POI data, Target can use geofencing to create boundaries at pools or other popular swimming locations. Once customers enter those areas, they’ll learn about the sale and think about buying an AC unit after taking a dip. Geo-conquesting can then dissuade them from making their purchase elsewhere; by creating a boundary around competitor lots and reaching consumers there, Target can show their deal to be the better one.

Once inside a Target location, customers can benefit from beacons and proximity marketing, and Target can help brands reach shoppers. Based on their location in the store, customers might receive highly targeted messaging on the Target app by way of devices on specific aisles that reinforces their reason for coming in the first place. Perhaps, for example, customers discover other ways to cool off such as discounted Super Soakers. With a location data-led strategy, Target can encourage customers in hot regions to buy an AC, steer them away from competitors, and allow brands to reach in-store shoppers, generating ad revenue to boot.

Sharpen site selection  

Location data also allows brick-and-mortar businesses to get smart about site selection. Regardless of their sector, companies can turn to this data to make sure that site decisions yield the highest possible ROI. 

While this data can of course help larger franchises, companies in more niche markets also stand to profit. Imagine that a high-end fashion brand from New York City wants to open a brick-and-mortar location in Los Angeles. With the right data, they can evaluate factors such as weather, mobility rates, and dwell times in specific neighborhoods to accelerate and fine-tune the decision-making process.

From there, the company can analyze POI data to determine possible competition and nearby, complementary locations. Having reviewed this data, the brand might choose L.A.’s trendy Arts District—a hotbed for their target audience—as a site. Not only that, but they would also be able to choose a storefront situated just next to a complementary POI (such as a highly sought-after coffee shop), an opportunity that would not have been visible without accurate geospatial data.

Create engaging location-based experiences

While these two use cases show how businesses can use location data to drive sales, geospatial information can also be an engine for the development of products and services themselves. This is especially true in the fast-growing industry of location-based entertainment (LBE), which often makes innovative use of emergent VR and AR technologies.

Nintendo’s Pokémon Go, for example, is an LBE experience that depends greatly on location data. One of the most successful mobile games ever created, Pokémon Go allows players across the globe to use the app to explore the world around them to discover digital creatures, which are placed in relation to specific POIs. These users might find a coveted Pokémon near their local pool, in a Target parking lot, or below one of the LA Arts District’s impressive murals.

Together, these cases show that when used wisely, location data represents an innovative—and even fun!—means of creating value. But the catch is that for location data to be an effective foundation for business strategy, it needs to be accurate. And much location data, especially abroad and in developing markets, is far from it. 

So, when you’re thinking about using location data to drive growth, be sure to consider a wide range of use cases. Be sure, also, to vet the accuracy of your data. The success of your location data-driven strategy depends on it.

By Geoff Michener, CEO of dataPlor

When the average marketer or business development professional thinks of location data, they likely think of mobility data, which shows where prospective customers go, helping marketers serve them the much-vaunted right message at the right time. But point of interest data, which shows where businesses and other brick-and-mortar locations are, can be just as crucial to acquiring new customers and driving growth.

For example, let’s say a mobility company like Uber wants to know how to most efficiently get people from A to B in the United Arab Emirates, Walmart wants to open new locations in Brazil, or P&G wants to optimize its distribution strategy in India. All of these growth strategies require comprehensive and accurate POI data, which is generally not available internationally, especially in developing economies.

Three ways POI data can help grow businesses across verticals include identifying new market opportunities, building strategic partnerships, and eliminating inefficiencies. Let’s dive into how POI data can power growth across industries and consider why leveraging it to grow internationally is not yet the norm.

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Use POI data to identify new markets

One of the most common uses of POI data is surveying a new market to determine where competitors are and how much room there is in a given city or neighborhood for a business to expand. For example, a quick-serve restaurant like McDonald’s might use POI data to examine expansion opportunities in Hungary. To do this, corporate will need to understand where fast food chains are located, how dense their locations are, and how POI data stacks up to mobility and demographic data to identify ideal opportunities.

A location data-driven expansion strategy can get much more granular than a high-level overview of where competitors are, though. Many POI data providers claim to provide comprehensive information on international markets but only tell a chain like McDonald’s where competing chains have locations. A more comprehensive dataset can tell McDonald’s where local restaurants are so that it can avoid trying to compete in locations where very similar local fare will crowd it out of the market.

Marketers can also pair POI data with other forms of location data, such as mobility and demographic data, to make optimal decisions. McDonalds’ might use mobility data at competitor locations to deduce which zip codes are ripe for a new entrant. Similarly, demographic data might indicate where the composition of the population is ideal for a McDonald’s location, saving the company money and yielding stronger ROI on new locations.

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Build strategic partnerships 

When many companies think of POI data, they think primarily of identifying and edging out competitors. But understanding where complementary businesses are is often just as key to developing an international growth strategy.

Consider the case of a logistics company like Uber. The ride-hailing company needs a highly accurate understanding of places to foster a positive user experience. But as the food delivery portion of its business grows, it also needs to know where restaurants, supermarkets, and rival delivery services are to understand its opportunities.

Another example of a vertical highly dependent on complementary business POI data is CPGs. A beverage brand needs to know not just where its likely customers live but also where it can partner with distributors to maximize market penetration and limit logistical costs. Beverage and other CPG brands routinely use POI data to build strategic partnerships and sharpen their growth strategies.

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Eliminate inefficiencies when growing abroad

In industries with high overhead like third-party logistics, eliminating inefficiencies can make the difference between a highly profitable and low-performing company. Last-mile delivery and trucking companies often use POI data to reduce the frequency of inaccurate deliveries, deliver a better service to their customers, and minimize spend on trucks, raw materials like gas, and personnel.

Data-led companies, such as those in search, customer intelligence, and finance, also have a lot to gain from highly accurate international places data. For example, customers depend on listings sites like Google and Yelp to find local businesses. Organizations depend on banks and investment firms to provide the best possible strategic insight to help them make extremely costly decisions. 

Without high-quality POI data, data-dependent businesses like tech firms and banks risk disappointing their customers and end users, compromising the integrity of their services.

Watch out — most international POI data is highly inaccurate

If it sounds like the promise of POI data is too good to be true — why wouldn’t every organization with global ambitions tied to brick-and-mortar locations be buying access to data that helps them understand those locations? — there is a catch. Contrary to what many US-focused location data providers claim, publicly available international places data is sparse, and very few location data companies can provide access to highly accurate data across markets, especially in developing countries.

POI data companies build up their datasets by searching publicly available databases and relying on human intelligence to fill in the gaps. Market leaders follow up on this through several verification tactics, including AI-driven data collection, machine learning data deduplication, and the enlisting of human experts, including academics and locals, to provide last-mile confirmation. But most location data companies rely on the same, limited methods they use in the US to compile international intelligence, with the result being international POI datasets that are up to 70 to 90% inaccurate. 

To assess whether location data companies offer accurate POI data in specific international markets, ask them what data verification steps they take, what they have done to understand that market specifically, how their processes differ across regions, and whether they have hired human experts to fill in the gaps in a given market. 

Making sure your international POI data is accurate is key — because if you’re getting incomplete or inaccurate data about international places, you might as well not have spent money and time acquiring access to international POI data in the first place.

Today, data is driving consumer choices and business decisions on a global scale. For many brick-and-mortar businesses, location data is part of this mix, helping them track competitor and customer behavior. Armed with this information, companies can navigate international markets fluently and identify prime opportunities for expansion.

But how exactly can location data increase a business’ competitive intelligence and inform an effective international expansion strategy? 

 To answer this, let’s take a deeper dive into how location data can help brick-and-mortar companies take over markets where competitors are sparse, expand near complementary sites to win on efficiency, and find prime audiences where competitors suffer from a mismatch.

Gaining market share with mobility and point of interest (POI) data

 Any retailer with a physical footprint that wants to increase its market share can do so with the help of mobility and POI insights. With this data in hand, businesses can visualize how competitors’ performance and footprints evolve over time. This empowers them to make data-driven decisions about market opportunities as soon as they arise. 

Let’s say that Nike wants to open a new flagship store in Istanbul. Before thinking about site selection, they’d do well to analyze mobility and demographic data across the Turkish capital to suss out other brands’ brick-and-mortar locations. This would tell them not only about competitors’ market share, but also provide important details about the customer base of each. When coupled with other datasets, information like this would unlock further insights, such as how visit rates ebb and flow in relation to factors such as time of day and weather.

Once Nike has a holistic understanding of the competition, they can better select the right location for their new flagship. Drawing on location data about their other stores in the region and those of their competitors, the brand can pinpoint where complementary points of interest will lead to satisfactory sales.‍

Using POI datasets to boost efficiency

QSRs also stand to win big with global location intelligence. In 2012, Burger King reentered the French market after 15 years away. As they continue to expand throughout the Hexagon, they need to know where their competitors have locations and how they’re performing. 

Location data provides important insights in this regard: How many McDonald’s, Quick, or KFC restaurants are within walking distance of the Eiffel Tower, for example? How quickly are these same companies opening or closing locations along the beaches of the Riviera? Or, finally, how well are these brands represented in neighboring Belgium—and would Burger King’s expansion plans be better suited there?

 By answering these questions, data will enable Burger King to capture more of the French market. In addition to choosing new sites that give them an edge over other multinational and local actors, they can increase efficiency by assessing how close potential sites are to other points of interest, such as distribution hubs, subway or bus stations, and residential areas from which they might source their workforce. This can lead to advantages in efficiences to drive higher margins in addition to boosting sales.

Finding the right audience with demographic data 

Another industry for which location data can increase competitive intelligence is telecommunications. Imagine that Verizon, for example, is thinking about expanding into Canada. Choosing Toronto as a case study, they might begin by developing a clearer picture of regional demand and competition with the help of complementary geospatial datasets. 

With these analytics at their fingertips, the telco could conduct trade area analysis by mapping competitors’ coverage in the city or determining the availability of broadband and 5G across demographics. This might allow Verizon to identify an underserved customer base—such as international college students—with a high likelihood of subscribing.   

After discovering this market opportunity, Verizon might move to build infrastructure. There, location intelligence will help them to make tower-location choices that maximize their new network’s coverage and connectivity while minimizing interference risks, maintenance issues, and environmental impact.

Why bad data is counterproductive

While these examples highlight how global location intelligence can give brands the chance to outperform the competition, working with the wrong data can lead to costly decisions that hand competitors an advantage. 

This happens all too frequently in the international market, where accurate location data is hard to come by. Often, international records are incomplete, inaccurate, dated, or compromised by duplicate entries that are difficult to correct. Acting on this data comes with a price: companies that rush to decisions with error-ridden data tend to make incorrect assumptions about competitors, invest in the wrong markets, and make predictions that cost both money and time.

 Nevertheless, for businesses that take their time to choose the most accurate data available, international growth is more than a pipedream—it’s a location that can be found on the map.

As organizations around the globe attempt to keep up with consumer and business trends, more and more are turning to location data to stay ahead of the pack. Armed with demographic, mobility, and POI (point of interest) datasets, leaders from every industry are looking to develop intelligence about customers and competitors that will help them scale internationally. 

It’s no secret that the global insights provided by accurate geospatial data are a valuable asset. Where demographic data can offer dynamic customer profiles, mobility data makes it possible to map buying and transportation habits as well as time spent in brick-and-mortars. For its part, POI data gives brands a holistic view not only of competitor locations but also of complementary or high-risk sites of interest capable of driving visits and sales (or not). 

But there’s a problem: a huge percentage of international location data is inaccurate. Let’s zoom out to better understand the true costs of poor-quality data before considering a case study that illustrates what can go wrong with location data—as well as how to fix it.

Learning the cost of bad data 

Imagine that a quick service restaurant (QSR) wants to use POI datasets to increase their international competitive intelligence. They know that this kind of data has the potential to increase distribution efficiency, inform strategic decision making about site selection, and allow them to reach new markets and customers.

What’s less obvious, however, is where the QSR should look for accurate datasets. POI data has numerous sources, ranging from state governments to private companies. Some of these provide their datasets for free, while others make them available for purchase either directly or through a location intelligence platform. 

Presented with these options, it’s critical that the QSR (and every company using geospatial datasets, for that matter) follow best practices when selecting a data source. The stakes are high if they don’t: inaccurate data can result in lower customer retention as well as significant over- or under-investment. These losses of money and time could set company strategy back months (or even years) and risk eroding shareholder confidence.

Case study: Developing location intelligence in Mexico

 Let’s get even more specific and imagine that Taco Bell wants to take another swing at setting up shop in Mexico, one of the world’s most open and attractive markets for international brands. After past difficulties, the QSR decides that this time will be different—in part because they’ve got international location data on their side. 

Taco Bell knows that they can develop insights on the basis of the four main attributes of POI records. These include location, function (or place type), contact information, and franchise information. They source this data for free from the Instituto Nacional de Estadística y Geografía (INEGI), which organizations the world over rely on for information about local businesses in Mexico.

With these data points in hand, Taco Bell plans to develop an insight-forward strategy to site selection in Cancún that avoids locations already rife with local dining options and maximizes proximity to complementary businesses.

But, again, there’s a problem. 

A study by dataPlor shows that from a random sample of roughly 1,000 INEGI business records, 80% of the data about Mexico POIs is inaccurate. For starters, these records’ contact information is highly unreliable: in the sample, 50% of listed websites were incorrect, and 81% of phone numbers were missing or incorrect. Three percent of these records were stuck with the wrong address, making them functionally invisible to anyone working with INEGI’s dataset.

Often, international POI records are incorrect because they have been pulled from multiple sources. Left unverified, this data patchwork—whose points could come from erroneous in-person observations or out-of-date information from websites such as Google Maps or TripAdvisor—might never be corrected.   

Whatever the reason for these inaccuracies, the study above highlights how unreliable international location data can be and points to the complications that can arise when sourcing it for free. Indeed, were Taco Bell to craft growth strategies on the basis of these datasets, a lot would go wrong. In Cancún, for instance, 91% of the INEGI records sampled contained some form of inaccuracy. Making decisions with this data would likely cause the company to misunderstand their competition, set up shop in suboptimal locations, and end up courting the wrong audiences. For example, they might fail to see an after-hours market opportunity in a bustling downtown neighborhood or might otherwise open a store on a street full of popular mom-and-pop options.  

In order to avoid mistakes like these, organizations looking to win internationally need to scrutinize the sources of their location data. When vetting a source, it’s critical to make sure that 1) they in fact specialize in geospatial data, 2) they collate multiple inputs for each record, 3) they offer metadata and other indicators that ensure records’ accuracy, and 4) they rely on human sources for reliable, in-person verification. Also, it’s important to consider whether they enhance their data to ensure its accuracy—something from which INEGI’s datasets would surely benefit. By following these simple steps, companies can harness the power of location data and become truly global leaders.

As companies wonder how best to accelerate growth at scale, many are turning to POI (point of interest) data to fuel their journeys. This information about where entities are located promises industry leaders an opportunity to rethink strategy, increase efficiency, and discover new markets. 

But what exactly is POI data? And how can it increase business intelligence? In this buyers’ guide, we’ll answer these important questions, highlight the risks of using low-quality datasets, and explain how to vet POI data for quality.

What is POI data?

POI data is a specific category of geospatial data. A point of interest is any physical site that might be of interest to individuals, companies, and decision makers. These include brick-and-mortar stores, restaurants, and malls, but also national parks, monuments, and other landmarks. 

Every POI record has a set of core attributes: location (address and/or latitude and longitude coordinates), function (or place type), contact information (phone number, website, etc.), and brand information (where applicable). Such records might also contain hours of operation, activity, or reviews.

 POI data has numerous sources, ranging from state governments to private companies. But before jumping into where to find the best POI data on the market, let’s review some examples of how it can benefit your business. 

How can POI data create value for my company?

 POI data can support organizations of all stripes. With it, leaders are able to conduct location intelligence at scale to generate actionable insights.

For example, POI data can allow your business to make smarter decisions about site selection. Any company looking to expand internationally can lean on this information to develop a holistic vision of a target area, region, or country. With the right datasets, brands gain insights about competition as well as about complementary POIs that might boost ROI.

POI data also makes it easy to hone your marketing. Whether your advertising is being run in-house or by an outside firm, POI datasets make it easier to reach new customers and nurture existing relationships. With the insights afforded by this type of location data, it becomes simpler to land on an effective marketing strategy, be it one that relies on mobile targeting, geotargeting, geofencing, or geo-conquesting.  

What are the risks of using bad POI data?

As these examples underline, smart location insights can supercharge business decision making. But not all POI data is created equal; indeed, POI records—especially international datasets—are often riddled with errors. And working with poor, incomplete data can be costly.

For starters, POI data is often burdened with restrictive licensing terms. These terms can make it near impossible to use a third party’s dataset across multiple platforms and use cases. Restrictions like these kneecap growth opportunities and turn costly data into a dead asset.

Companies that use faulty data also risk being priced out of current and prospective markets. If the geospatial snapshot provided by a POI dataset is inaccurate, it’s easy to overlook or overestimate competition. By extension, this makes it difficult to operationalize other kinds of location data and their insights about consumer behavior and demand. 

These risks lead to poor outcomes, which make for very real losses of time and resources. Once a business realizes that they’re working with bad data, it can take months to correct course, find new vendors, and amend strategy. If a site’s been chosen—or, worse yet, ground broken—on the basis of poor data, there might not be a way for the business to recover.

The end result of these missed opportunities and damaging outcomes is even harder to bounce back from, as both lead to the erosion of confidence and trust among stakeholders. This can set a company back years, lead once-loyal customers to defect to competitors, and at times even permanently stifle growth. With that in mind, let’s take a look at how to avoid these risks by choosing the right places data and finding the best POI data provider.

What makes for a good POI database?

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So, what exactly constitutes good POI data? 

The quality of any places dataset rests on its accuracy, coverage, scale, and recency. The data that you buy should be transparent about four elements: (1) the variety of its sources, (2) the coverage and depth of each of its records, (3) those records’ accuracy, consistency, and completeness, and (4) how up-to-date it is.

It’s also important to ask follow-up questions about a given POI purchase before committing. What, for instance, makes the data in question unique? How is this POI data being presented? Can it be combined with other datasets for additional insights? And finally, how might it be used to drive business strategy?

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What criteria should I use when picking a POI data provider?

While free POI datasets can be found online, they’re ill-suited to the needs of leaders looking to capture market share. Imagine: if you and your competitors are looking at the same free data, it is impossible for either of you to gain a competitive advantage.

So, it’s crucial to choose the best possible partner when buying POI data. To do so, be sure that the provider under consideration checks five boxes:

✓  Confirm that your data provider specializes in POI data. While other providers might offer POI as part of a larger package, only POI-focused ones are able to dedicate the time and resources to ensuring that their data is accurate, unique, and actionable.

✓  Partner with a data vendor who streamlines places datasets from multiple sources. The best POI records are collated and streamlined from multiple reliable sources, transforming a variety of inputs into a single source of truth. Providers that don’t do this will leave your company vulnerable to messy or redundant data, which will waste precious hours of your IT teams’ time to clean up.

✓  Look for vendors who provide metadata and other indicators for every record. This information gives buyers the signal that the seller has done due diligence in verifying their data.

✓  Choose a provider who knows the value of local sources. While providers often look to a variety of sources and tools to double-check their records, human validation is often required to guarantee the integrity of places data. While a number of sellers tout academic opinion or AI as proof that their datasets are accurate, those that don’t consult local experts are leaving gains in accuracy on the table.

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✓  Pick a vendor that’s not under public media pressure or scrutiny for their practices. In addition to being a boon for businesses, location data can be dangerous if used or sold nefariously. As such, buyers need to be vigilant not only about the integrity of their POI data, but also about the integrity of the vendors from which they buy it. Doing so provides peace of mind about the long-term health of your buyer-vendor relationship. You don’t want to be cited in a media story for working with privacy-unsafe providers.

Why you might choose dataPlor for POI data

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dataPlor is dedicated to providing you with best-in-class POI data. As places data is our sole focus, we’ve been able to concentrate on verifying our roughly 125 million records from over 70 countries. This is in sharp contrast to much of the competition, whose international data is often an afterthought—and, as a result, up to 70-90% inaccurate.

The global scope of our product is made possible by tools that allow us to go multiple steps further than the competition in verifying our information. Machine learning, AI, and deep learning, for instance, help us to gather the most up-to-date signals from any area. Our process also taps local experts to drive our data’s industry-leading accuracy. 

Thanks to this process, we’re able to constantly upgrade our existing countries and records to equip you for growth. All of which means that you’ll have the data that you need to stay ahead of the competition.

Artificial intelligence is transforming the way location data companies collect, process, and analyze data. With the help of AI, these companies are able to gain a deeper understanding of their customers and the areas in which they operate, which in turn enables them to make more informed and efficient business decisions. 

AI’s greatest value is that it can outperform humans in many functions at macro and micro levels. At the macro level, AI can analyze enormous amounts of data quickly, making it possible to find trends and make predictions that humans would miss. At the micro level, it can sift through minute details and eliminate data duplications and other errors that might pass by human eyes. 

At the same time, the key to leveraging the power of AI is a multifaceted approach that makes use of its strengths while anticipating and accounting for its weaknesses. Black swans are the classic example. England only has white swans, so an AI trained there would conclude that every swan must be white, and consequently miss on all black ones. This points to a weakness of AI: it only knows what it has been shown, and, unlike humans, it struggles to fill in the gaps between things it does not already know. The consequences can be serious, as in the example of a self-driving vehicle that failed to brake because it identified a pedestrian and their bike as unknown objects.

That’s why dataplor employs a multifaceted approach to AI that maximizes its abilities while avoiding potential drawbacks. Below, we explore three ways in which dataplor’s deployment of AI gives it a competitive edge: drawing on deep image processing and data enhancement; analyzing data at scale to develop predictive models; and leveraging the power of computer-human synthesis.

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How dataplor uses AI to analyze images and collect and enhance data

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In addition to being worth a thousand words, pictures provide key clues as to when a business opens or closes, what kinds of beverages a restaurant offers, or whether a new location is viable in a developing area. AI systems have given new life to photos because of their capacity to analyze huge amounts of them and identify important information. Called deep image processing, this application lets dataplor draw untapped location intelligence from the multitude of photos publicly available online, photos taken by in-house human validators, and images gleaned from alternative sources and partners like dash cam footage.  

Imagine you’re a CPG executive tasked to undertake a global search for restaurants in major cities in need of a soft drink provider. Dataplor’s AI-based deep image processing can pour over photos taken by individuals from inside restaurants in Sydney, Kolkata, Bogotá, or San Antonio, scanning pics of menus, counters, door stickers, soft drink dispensers, and so on, looking for a brand name that signals a provider or lack thereof. A global CPG company can then use this intelligence to identify new business opportunities.

Another application of AI is data collection and enhancement. Dataplor uses AI call bots to get ground signals from across the globe at minimal cost. Language-trained bots call businesses to ask about hours or services, providing detailed local business data at scale. For example, AI callbots can find out a business’ hours or verify whether those hours are correct. In tandem, machine learning systems dig through this data to eliminate duplications, minimizing inaccuracies and rendering the data immediately actionable for a wide range of use cases.

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How dataplor uses AI to help businesses predict the future

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One of the more promising applications of AI is predictive analytics: the use of historical data, machine learning, and statistical algorithms to identify the likelihood of future outcomes. Location data companies use predictive analytics to forecast future customer behavior and make data-driven decisions. For example, a company can use predictive analytics to identify which neighborhoods are likely to see the most growth in the coming years and invest resources accordingly.

Photos also come into play with predictive analytics, this time as a source of historical data. An AI analysis of storefront photos from one year might reveal lots of construction and development that disappears from photos five years later, signaling a downturn not yet evident in standard economic indicators. 

AI can also crunch location data to create predictive models that help businesses make better decisions: for example, helping a retail store to determine the best location to open a new branch or aiding a hotel in predicting seasonal demand for travel accommodations. 

In addition, location data companies like dataplor use machine learning to analyze satellite imagery of a particular area to identify patterns in land use, such as the location of buildings, roads, and infrastructure. This can be used to gain insights into population density and land values, which real estate companies can use to identify areas with high potential for development or investment.

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How dataplor’s computer-human synthesis gives it a competitive edge

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Despite these benefits, AI systems can be biased or make errors, and humans are needed to detect and correct these issues. One way to do this is for humans to provide domain expertise. For example, a human with expertise in location data for the retail industry can help ensure that the AI system is properly analyzing the information it receives and making predictions that are relevant to the industry. An AI might mistake a distressed brick warehouse in the Allston area of Boston as a sign of economic decline, for example, when it’s actually a newly renovated coworking space.  

For these reasons, dataplor uses human-computer collaboration to anticipate and improve the performance of its AI systems. Specially trained human validators, with expertise across cultures and languages, act as eyes on the ground and provide real-time feedback that AI systems can use to improve. Validators spot challenges like wordplay in business names or cultural differences in how addresses are formulated and make sure that data is tagged and processed accurately.  

For all these reasons, AI will keep growing as a centerpiece of the location data industry. The edge, however, will go to the companies that know how to identify its limits and leverage human-AI collaboration to succeed.