Feb 09, 2023 /
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Jan 23, 2023 / 18 min read
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.
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.
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.
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.
Jan 23, 2023 / 12 min read
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.
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.
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.
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.
Jan 23, 2023 / 8 min read
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.
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.
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.
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.
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.
Jan 23, 2023 / 8 min read
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.
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.
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.
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.
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.
Jan 23, 2023 / 12 min read
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.
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.
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.
Jan 23, 2023 / 19 min read
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.
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.
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.
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.
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.
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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?
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.
✓ 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.
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.
Jan 23, 2023 / 12 min read
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.
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.
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.
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.
Jan 23, 2023 / 8 min read
dataplor partnered with a top 10 global CPG company, a multinational drink and brewing business with more than 600 beer brands in 150 countries.
CPG companies depend on precise location data to grow abroad. They need to know where customers, retailers, and other supply chain partners are. This is a tall order in normal times — before partnering with dataplor, the beverage company had discovered that available data on distribution channels in the alcohol industry was fragmented and not frequently updated, especially in emerging economies. The client’s market, customer, and competitive intelligence team tried to maintain its own location data and found itself constantly having to turn around and source it again to avoid errors.
Then, COVID struck. If sourcing accurate and up-to-date point of interest data was hard before the pandemic, it became all but impossible during that period of historic disruption. This was especially the case in developing countries such as Brazil and Mexico, where the beverage company suspected opportunities for growth but could not begin to map out how to plot its expansion.
The client needed international location data that was comprehensive, accurate, and up to date. The market, customer, and competitive intelligence team also needed a way to gauge confidence in the data so that, when they brought their conclusions about growth opportunities to the firm’s leadership team, they could prioritize regional opportunities and know how much to trust their assessments.
dataplor’s POI data allowed the client to better understand their coverage and their competitors’ coverage in international markets, identify new distribution channels, and more strategically allocate resources to gain market share and decrease waste. The client team developed a multi-pronged approach to its market analysis that uses dataplor data to locate distributors and understand spatial relationships between distributors, customers, and prospective customers.
With dataplor Places data, the client now commands reliable and up-to-date information about its customers and supply chain partners. Much more easily than before, the client can identify where to focus its expansion efforts, targeting distributors who will maximize market penetration based on current gaps in market coverage and demographics.
The client can aggregate confidence scores as well as demographic, brand, and POI data to more strategically distribute its products and increase sales. And the market, customer, and competitive intelligence team now delivers detailed reports to leadership to identify growth opportunities.
Contact dataplor to learn more about how location intelligence can drive growth for your brand. Let’s add a contact us button here to capture leads.
Jan 06, 2023 / 8 min read
Whether they’re looking to improve site selection, add precision to marketing, or gain insight about the competition, companies of all kinds—from real estate to retail to logistics—are dedicating considerable spend to location intelligence.
As demand for this data continues to rise, supply has followed suit. What’s more, now that the risks of free location data are more widely understood, brands are gravitating towards third-party providers whose data will grant them the granular insights needed to grow at scale without sacrificing accuracy.
But not all providers are created equal. From the collection process to the final product, each offers a different path to location intelligence. Two features that distinguish dataplor from other location data companies are its combined use of AI and human expertise as well as its solution-forward approach to client satisfaction.
By relying on technology and human expertise, dataplor has crafted the most rigorous and global data collection process in the geospatial data business.
Where other providers suffer from an overreliance on human sources and outdated technology (which can lead to errors and inconsistencies), dataplor employs a combination of AI, machine learning, and deep learning for collection. Thanks to our significant investment in these tools, we’re able to use AI call bots to gather up-to-date information, deep learning and image recognition to expand and verify datasets, and machine learning to update attributes and identify issues and patterns in the data.
For example, we might establish a record with a call bot by confirming a restaurant’s phone number and operating hours. From there, we can use image recognition tools to verify on-the-ground information and, if it’s a chain, identify other locations. This enables us to avoid inaccuracies, such as including the record for a location in San Antonio, Texas in a dataset focused on Austin.
Once this data has been collected, dataplor knows precisely when and how to improve it. This is largely thanks to our multipronged approach to enhancement. Before a dataset containing the record above makes its way into a client’s hands, for instance, it will have gone through multiple stages of refinement. These could include dataplor’s custom-built machine learning technology, which eliminates duplicate records (a major issue with POI data), as well as our proprietary enhancement engine, which automatically identifies incorrect geocodes, addresses, and other faulty location attributes.
In addition to leveraging cutting-edge technologies, we know the value of human sources. Our network of over 100 validators does the important work of last-mile verification in more than 200 countries. Each of these local experts has been trained to identify and improve POI records by ensuring that our AI machine learning tools are correctly tagging information. This allows us to offer our customers industry-leading accuracy on an international scale—a veritable rarity in today’s location data market.
While many companies in the geospatial data market boast a wide array of products and services, dataplor remains focused on places data alone. This enables us to ensure the quality of our product instead of spreading ourselves thin across different paths to location intelligence.
This focus also provides dataplor’s customers with a solutions-focused resource rather than a one-size-fits-all headache. Some providers, for instance, only sell out-of-the-box data products that come as-is, which buyers need to somehow adapt to their specific needs and contexts. Unfortunately, this can lead to wasted hours, unexpected costs, and misleading insights.
By contrast, dataplor offers customers the solutions they need when they need them, such as custom data collection and tailored datasets. To do so, we rely on our POI know-how to identify hard-to-find places or data points; recently, for example, we worked with a major manufacturing company in India to deliver a dataset focused on competitors’ EV charging stations, gas stations, parks, libraries, and other infrastructure complementary to transportation. This helped the client determine how to plan out their EV charging network and gain market share in the electric vehicle space.
Because dataplor deals in POI data alone, we’re able to work with clients across global regions to solve problems and explore opportunities as they arise. Put otherwise, with dataplor, customers can rest assured that they won’t be left guessing with an unwieldy dataset—instead, they’ll be provided with the ongoing support necessary for truly global POI data-driven intelligence.