MANHATTAN BEACH, Calif. — Dec 18, 2024

dataplor, the leading global geospatial data company specializing in Point of Interest (POI) data, is excited to announce it has joined the Esri Partner Network (EPN) as a Bronze partner. Esri is the global market leader in geographic information system (GIS) software, location intelligence, and mapping. This collaboration aims to provide customers with enriched geospatial insights, combining the strengths of both companies to empower businesses with advanced tools for strategic decision-making worldwide.

Retailers, CPG brands, financial institutions, tech companies, and third-party logistics providers rely on dataplor for comprehensive and precise POI data to assess competitive landscapes, select new sites, and identify markets ripe for expansion. The EPN partnership with Esri will further empower these users with sophisticated location intelligence, enabling them to unlock deeper insights into market trends, investment opportunities, and risk mitigation strategies across various geographies and timeframes.

With a global network of 3,000 partners, Esri’s EPN partners help customers around the world utilize the benefits of ArcGIS software and location intelligence. For over 50 years, Esri partners have supported users with business solutions, implementation services, and dynamic content by leveraging GIS technology across every industry.

“The partnership with Esri represents a significant step forward in our mission to provide the most accurate and actionable geospatial data available,” said Geoff Michener, founder and CEO of dataplor. “Combining Esri’s ArcGIS software with dataplor’s high-quality POI data offers businesses unparalleled insight into where to focus their efforts and investments to achieve sustainable growth.”

dataplor is distinguished in the geospatial data sector by its exclusive focus on POI data and its rigorous data verification processes. While much location data is plagued by inaccuracies from unreliable open-source data and inconsistent records, dataplor employs advanced machine learning algorithms, AI-driven call bots for local business validation, and local human experts to resolve data ambiguities.

In the past year, dataplor has expanded its database to over 350 million POIs across more than 250 countries and territories, offering unmatched global coverage. Leading Fortune 500 companies, real estate investors, financial institutions, insurers, and local communities leverage dataplor’s data to identify strategic opportunities, mitigate risks, and drive success. By partnering with Esri, dataplor will further enhance its ability to provide businesses with powerful location intelligence solutions to optimize their market strategies.

Contact media@dataplor.com for more information.

Foot traffic data, also known as mobility data, shows how many people visit a specific place. This includes retail stores, fast-food restaurants, and many other public areas. The data collected gives businesses valuable insights into the number of people that visit a location and when. It plays a significant role in helping businesses uncover visitation patterns, tailor marketing efforts, and improve customer experiences.

The growing role of data analytics has changed how foot traffic data is measured. Originally, basic counting methods and turnstiles collected footfall data. But, with the rise of powerful tools like AI and machine learning, businesses can now track movements and analyze visitation patterns. This blog will explore recent foot traffic trends, their impact on industries, and how businesses can use these insights for more informed decisions.

How Foot Traffic Data Complements Location Data

Foot traffic and Point-of-Interest (POI) location data work together to provide a full picture of business landscapes. Foot traffic data provides information about consumer behavior, such as how many people visit a location and when. In contrast, POI data provides comprehensive details about locations, including their names, categories, hours of operation, and geographic coordinates.

When combined, these datasets offer businesses many advantages. For example, a retailer can use POI data to find nearby competitors and foot traffic data to see how often consumers visit them. These insights help businesses make better decisions about locations, marketing, and inventory, driving more visitors and boosting sales.

The Rise of Foot Traffic Analysis

Foot traffic analytics have grown significantly over the years. While early methods relied on manual counters and basic systems, businesses today use AI-enabled sensors, Wi-Fi tracking, and computer vision to monitor and measure foot traffic. These advancements have enhanced the accuracy and quality of demographic data, allowing businesses to gain deeper insights.

Key Industries Using Foot Traffic Data

1. Retail

The retail industry uses foot traffic data to understand shopper behavior, improve store layouts, and measure marketing effectiveness. Store locations with high foot traffic counts often suggest higher revenue and brand visibility.

• Return of In-Store Shopping: Following the pandemic, many shoppers are returning to in-store shopping. Recent reports expect in-store foot traffic to surpass pre-pandemic levels by 2025.
• Seasonal Trends: Consumer visitation data shows significant spikes during seasonal periods, giving stores prime opportunities for targeted marketing.

2. Commercial Real Estate

For investors and property managers, foot traffic data helps determine property value and smart investment opportunities. Understanding these traffic patterns allows for better planning and targeting.

• Development Planning: Real estate developers lean on foot traffic data to determine the scope of potential projects. This is important for ensuring there is high ROI.

3. QSR (Quick Service Restaurants)

Restaurant foot traffic data helps QSRs pinpoint ideal locations for expansion and refine operational strategies. Insights on a specific location can also help determine optimal staffing and inventory levels.

• Customer Decision-Making: High visitation amplifies the convenience of fast food in consumer decision-making. This data enables QSRs to enhance customer convenience and plan operations efficiently.
• Trends in Orders and Delivery: Food delivery has largely replaced in-restaurant dining with mobile and drive-thru orders. QSR businesses can analyze this data to streamline their services.
• Operational Efficiency: Brands like McDonald’s use foot traffic data to predict busy seasons, ensuring necessary staffing and inventory levels.

4. CPG (Consumer Packaged Goods)

CPG brands benefit from analyzing foot traffic to identify stores with high visit counts. Knowing the locations that attract the most visitors helps CPG companies decide where to place their products for the most impact.

• Retailer Performance: Foot traffic data enables CPG companies to pinpoint frequently visited retail stores, allowing them to collaborate with retailers on optimal product placement and promotions. By focusing on popular locations, brands can better address stocking and distribution efforts, driving brand visibility and increasing sales.
• Comprehensive Competitor Analysis: Gathering insights about foot traffic at nearby competitors allows your business to identify opportunities for site selection and areas for improvement.

Factors Behind Foot Traffic Data Collection

By leveraging foot traffic data, businesses gain valuable insights into consumer patterns, operational efficiency, and market trends. Understanding the factors that drive this data collection helps businesses adapt to changing environments and make informed decisions.

1. Consumer Behavior: Analyzing how consumers interact with physical store locations is crucial for businesses that are looking to provide high quality experiences.
2. Urban Development: The growth of urban areas increases visitation, emphasizing the need to analyze foot traffic.
3. Technological Growth: The rise of loT devices, artificial intelligence, and machine learning has made footfall data more comprehensive.

Key Foot Traffic Insights

Consumer Behavior and Shopping Patterns

Foot traffic data offers valuable insights into consumer trends and shopping patterns. By analyzing visitation data, businesses can identify the busiest locations and understand how these patterns affect sales. This also allows businesses to align staffing and inventory in high-traffic locations, improving the overall customer experience.

Seasonal and Trend-Driven Shifts

Seasonal trends can also have a significant impact on foot traffic patterns. During holidays, back-to-school seasons, and events like the 2024 Paris Olympics, businesses can adjust their efforts to accommodate more visitors.

Leveraging Foot Traffic Data for Business Success

Optimizing Store Inventory

Businesses across various industries can leverage foot traffic to optimize inventory planning. By identifying high-traffic locations, businesses can allocate inventory to the stores that see the most visitors, aligning stock levels with consumer demand. For example, a grocery store chain may stock popular products in stores with higher foot traffic to increase visibility and sales potential.

Targeted Marketing & Promotions

Foot traffic insights help businesses improve marketing by using strategies like geo-targeted ads and personalized outreach. By understanding popular visiting times, companies can plan promotional events for maximum impact. Real-time, high-quality foot traffic data also contributes to staffing and operational efficiency. It does this by helping businesses predict peak times, cut costs, and enhance the customer’s experience.

Site Selection Strategies

Consumer visitation data supports location strategy and site selection. This allows businesses to identify high-density areas for potential store openings. It also helps pinpoint areas with high growth potential. For example, a coffee shop may use foot traffic data to open new locations near busy transit hubs.

Privacy & Ethical Considerations

When collecting and using this data, it is important for businesses to respect the privacy of consumers. Some key ethical considerations include:

• Anonymization: Verifying that data is collected in a way that doesn’t reveal consumers’ personal information is key.
• Compliance: Adhering to privacy regulations such as the General Data Protection Regulation (GDPR) is essential to maintaining consumer trust. This also safeguards visitor’s personal and identifiable information. By prioritizing these compliance measures, businesses can responsibly use foot traffic data while respecting privacy.

The Future of Foot Traffic Analytics

The landscape of foot traffic analytics is evolving, driven by advancements in technology and the constant demands of businesses. Emerging trends will continue to shape how foot traffic data is collected, analyzed, and applied across various industries.

Artificial Intelligence & Machine Learning

It’s no surprise that artificial intelligence and machine learning have completely transformed how foot traffic data is collected. These technologies allow businesses to predict future visitation data and draw patterns with reliable accuracy. AI tools analyze past visitation data, weather, and events to help businesses optimize operations. These predictive measures help businesses to stay ahead of consumer trends, giving them a competitive advantage.

Virtual Reality

As digital and physical spaces collide, foot traffic data will contribute to hybrid environments. With the rise of virtual reality, businesses can track virtual foot traffic and compare it to real world footfall. The combination of these data sources can provide a comprehensive view of customer behavior.

Smart Cities

In smart cities, foot traffic data will be key for planning and improvement. Smart city planners can analyze movement and busy areas to improve transportation initiatives and allocate resources better. It can also help make strategic decisions around safety and designing efficient spaces. This data provides the high levels of insight needed to build sustainable, smart cities.

Start Leveraging Foot Traffic Data for Business Insights

Understanding a geographic area’s foot traffic pattern is no longer a choice for businesses; it is a strategic necessity. By studying patterns and leveraging insights from mobility data, businesses can optimize operations, improve customer experiences, and drive profits.

Foot traffic data helps businesses understand when and where customers engage, enabling better decisions around store placement, staffing and stocking logistics, marketing efforts, and more. Whether you’re a retailer, investor, property manager, QSR owner, or CPG brand, utilizing foot traffic data can help you stay ahead of the competition.

Want to learn more about how location data can impact your business?

You can learn more about our resources here. Partner with dataplor today and experience all the benefits on a real-time basis!

Geospatial intelligence, or (GEOINT) refers to the integration of geographic information and technology—such as satellite imagery, remote sensing, and Geographic Information Systems (GIS)—to derive insights from spatial data. GEOINT captures data from the Earth’s surface to support decision-making across various industries. 

Originally used by the intelligence community, including the U.S. military and national security agencies, GEOINT now plays a vital role in helping businesses worldwide to make informed decisions, ranging from disaster response to market entry.

The National Geospatial-Intelligence Agency (NGA) was initially known for delivering intelligence products and analysis through aerial imagery and mapping services. However, with the rise of big data, geospatial intelligence has expanded beyond its traditional use. Today, many industries leverage geospatial data to turn complex data into actionable insights, transforming business practices and decision-making.

The geospatial analytics market is expected to reach $141.9 billion by 2028, making GEOINT a key focus for businesses worldwide.

This article covers the basics of geospatial intelligence, its sources and applications, and how businesses are using it to stay ahead. We’ll also explore how dataplor fits into the geospatial arena and how you can benefit from our solutions. 

What is Geospatial Intelligence?

Geospatial intelligence is the combination of geographic data, spatial analysis, and information management to produce insights.      

These insights are used by decision-makers worldwide to understand and respond to complex situations, from national security to business strategy.

Here are some of the main components of geospatial data: 

1. Geographic Data: The foundation of GEOINT is geographic data, which is information about the Earth’s surface and its features. This data is gathered through various means, such as satellite imagery, aerial photography, remote sensing, and even social media check-ins, providing a rich, multi-dimensional view of any area.
   
2. Spatial Analysis: Once geographic data is collected, spatial analysis processes it to uncover patterns, trends, and relationships. Using mathematical and statistical techniques, spatial analysis reveals how different geographic elements interact. This approach is applied in areas such as identifying the best locations for business expansion, imagery intelligence, and analyzing environmental changes.
   
3. Information Management: Geospatial intelligence involves managing large volumes  of data. Information management systems organize, store, and process GEOINT data to ensure it is accurate, accessible, and actionable. High-resolution datasets, mapping software, and cloud-based tools also allow analysts to interpret information quickly and provide real-time insights to decision-makers.

However, it’s important to understand the difference between geospatial intelligence and Geographic Information Systems. Although they are closely related, GEOINT and GIS serve different purposes. 

• GIS is a tool for collecting, storing, and visualizing geographic data, often in the form of maps or layered visual representations. 
• Geospatial intelligence, on the other hand, takes GIS data and applies spatial analysis and information management techniques to generate insights for strategic decision-making. 

In essence, while GIS is a system for handling geographic data, GEOINT is the analysis and intelligence derived from that data.

The Benefits of Geospatial Intelligence

GEOINT gives you valuable insights to change how you approach strategy, operations, and risk management in your business. 

By combining geographic data, spatial analysis, and information management, GEOINT enables informed decision-making based on real-world, location-specific information.

Here are the main advantages:

1. Better Decision Making: GEOINT gives you high-resolution, data-driven insights into market conditions, customer behavior, and operational performance across geographic areas. You can use these insights to choose the best locations for new store locations, evaluate supply chain logistics, and allocate resources where they’ll have the most impact.
   
2. Better Operational Efficiency: GEOINT helps optimize routes, plan resource allocation, and anticipate customer demand based on geographic patterns. For logistics and supply chain companies, this often translates to reducing fuel costs and delivery times. In utilities or telecommunications, it supports coordinating infrastructure repairs and upgrades based on location-specific needs.
   
3. More Market Understanding: Geospatial data provides a 360-degree view of your market, revealing demographic trends, spending patterns, and competitor locations. With this data, you can tailor your marketing campaigns, identify high-potential markets, and personalize customer experiences based on location-specific characteristics.
   
4. Risk Management: Geospatial intelligence helps you identify and manage risks related to natural disasters, market volatility, and security threats. Using tools like remote sensing and satellite images, you can anticipate weather-related disruptions, assess regional stability, plan for contingencies, and protect your assets and workforce.

For industries where location knowledge is crucial—such as retail, real estate, logistics, and finance—geospatial intelligence gives you a competitive edge by turning geographic information into a strategic advantage.

The Role of Location Data in Geospatial Intelligence

It’s no surprise that reliable location data is the foundation of geospatial intelligence. 

The value of GEOINT lies in the accuracy, depth, and relevance of the geospatial data, which turns raw location information into actionable insights for decision-making.

By providing real-time, location-specific data, you can make strategic decisions based on a comprehensive understanding of geographic and spatial factors.

Geographic Features

Geographic features like terrain, elevation, water bodies, and infrastructure form the foundational layer of geospatial intelligence. 

This data, sourced from tools like GIS, helps businesses and analysts visualize physical environments and gain a deeper understanding of the geographical context surrounding operational areas.

Remote Sensing Imagery

Remote sensing imagery captured from satellite or aerial platforms provides an up-to-date, high-resolution view of specific locations. 

You can also obtain intelligence and insights across various sectors, from urban planning to crop health to disaster impact. 

Remote sensing helps businesses track changes over time, which is invaluable for industries such as real estate, agriculture, and national security.

Location Data

Location data from devices, social media, and GPS allows businesses to analyze real-time customer behavior, movement patterns, and population density. 

By using location-based insights, businesses can personalize marketing, optimize site selection, and refine customer engagement strategies based on where their target audience lives, works, and spends time.

These three types of geospatial data collectively enhance the overall geospatial intelligence picture, providing valuable context, insights, and the ability to predict future trends. 

By combining these data types, you can make GEOINT a powerful tool for location-specific decision-making.

Empower Your Organization with dataplor’s Geospatial Data

Whether through open source or other means, geospatial intelligence (GEOINT) is a valuable asset for businesses looking to incorporate location-based insights into their strategic decision-making, operational efficiency, and market understanding. 

In today’s data-driven world, where precise and actionable information is more crucial than ever, the importance of quality geospatial data continues to grow.

dataplor’s geospatial data solutions help organizations to maximize the value of  GEOINT, offering high-resolution, real-time insights tailored to their business needs. From site selection to customer engagement, decision-makers can rely on dataplor’s solutions and tools to stay informed and ahead of the competition.

Ready to elevate your organization with cutting-edge geospatial intelligence? 

Explore dataplor’s data solutions and see how we can support your growth. 

Reach out today for a consultation or request a data set sample to experience the dataplor advantage firsthand.

The proposed $24.6 billion merger between grocery giants Kroger and Albertsons has already found itself under intense legal scrutiny, with trials recently concluding in both Washington state and Colorado. Set to make their rulings in mid-November, these states, along with a federal case, are weighing the merger’s potential impact on competition, consumer prices, and jobs.

In Washington, where nearly half of the state’s grocery market could be affected, Attorney General and Governor-Elect Bob Ferguson argues that the merger would stifle competition and raise prices, eliminating daily battles between Kroger and Albertsons stores over promotional pricing, product quality, and even store hours. Meanwhile, in Colorado, Attorney General Phil Weiser echoes these concerns, challenging the adequacy of Kroger and Albertsons’ proposed divestiture of 579 stores—a plan intended to maintain market competition.

While Kroger and Albertsons contend that the merger is essential to competing with Walmart’s market dominance, regulatory authorities, labor unions, and consumer advocacy groups remain skeptical. dataplor’s analysis of Point-of-Interest (POI) data offers insights into what this merger could mean for the future of grocery shopping and mergers in the U.S.

Where the Overlap Matters Most

With over 4,000 Albertsons locations across the U.S., our analysis reveals that 37% of grocery stores within a 5-mile radius of an Albertsons are Kroger-branded. As we expand the radius to 10 miles, Kroger’s share increases to 40%. These figures represent more than just proximity; they signify a potential consolidation that could fundamentally reshape competition and limit options for millions of shoppers. This overlap is particularly pronounced in states like Arizona, California, Colorado, Oregon, and Washington, where both chains have substantial footprints and where the greatest number of divestitures are planned if the merger goes through. 

Compounding this, 30.3% of Albertsons locations have Kroger as the only other major grocery competitor within 5 miles—excluding smaller bodegas and specialty stores. If the merger is approved, these stores would lose their only direct competitor, potentially leading to fewer choices, higher prices, and diminished market innovation for local consumers.

What’s at Stake for Consumers?

As legal battles unfold, the data tells a compelling story about what could change for consumers. Combined, Kroger and Albertsons would control 13-18% of U.S. grocery sales, depending on the metric used. This level of consolidation could reduce competition in markets where the two already have strong footholds, particularly in states with significant overlap. Critics, including the Federal Trade Commission (FTC), caution that removing this competitive tension would lead to higher prices, reduced consumer choice, and potential job losses.

If approved, the merger will redefine grocery shopping for millions. dataplor’s data-driven analysis sheds light on potential shifts in market dynamics and the impact on local competition. As we await court decisions that will determine the fate of this deal, understanding market concentration and geographic overlap has never been more critical for businesses navigating change.

Want to uncover the data-driven insights that power strategic decisions? Contact dataplor to explore how our expertise can guide your understanding of market dynamics and help you seize emerging opportunities.

The digital world is constantly developing and evolving, and in today’s competitive landscape, many businesses struggle with the challenges of limited data insights. Relying on raw data alone often leads to gaps in understanding customer preferences and missed opportunities in targeted marketing.

Customer expectations evolve with time, making it vital to seek out deeper data insights from your existing data to maintain an edge. This is where data enrichment becomes essential, allowing you to enhance the quality of your datasets and make informed decisions that drive the growth of your business.

A recent study reveals that businesses investing in data enrichment experience a 20% improvement in their ability to understand customer behavior and preferences. This enhanced understanding can empower you to refine your marketing strategies, tailor your messages, and optimize the efforts of your sales teams, leading to positive and measurable results.

In this article, we’ll review the fundamentals of data enrichment, explore how dataplor’s POI data enhances the data enrichment process, and provide some real-world use cases and examples of how businesses have achieved success using enriched datasets.

What is Data Enrichment?

Data enrichment is the process of adding additional information to existing datasets to improve their quality, accuracy, and completeness. For instance, if you’re evaluating customer data, you may want to include elements such as appending external data or first-party data, contact data, or third-party data—such as demographic data, contact information, and firmographic data—to existing customer data. 

By enriching raw data with high-quality information from various data sources, you can turn incomplete or outdated data into actionable insights, improving the overall value of your data management and validation processes. Data cleansing tools can help you ensure that this enriched data is accurate and ready for use in making business decisions.

Here’s why data enrichment is important for your business:

1. Complete Market View

With data enrichment, you can achieve a complete view of your market by combining existing data with external data sources. This allows you to track competition, identify new opportunities, and validate business decisions.

Having enriched datasets allows you to analyze trends in real-time and make informed decisions based on comprehensive insights.

2. Better Customer Segmentation

Enriching customer profiles with demographic and firmographic data enables you to segment your audiences more precisely. This deeper understanding of customer needs and preferences allows for more effective targeted marketing efforts.

It ensures that your marketing teams can deliver personalized messages that resonate with distinct groups, improving the overall customer experience.

3. Optimized Marketing Campaigns

By enhancing CRM and marketing databases with high-quality data, you can optimize your marketing strategies. 

For instance, enriching customer data with insights from POI data allows you to craft campaigns that are more aligned with consumer preferences, ultimately boosting conversion rates and enhancing customer relationships.

4. Smarter, Data-Driven Decisions

With enriched datasets, you can make data-driven decisions based on comprehensive, quality data. This helps fine-tune sales efforts, improve workflows, and boost overall efficiency.

Having access to data enrichment tools like dataplor ensures that every piece of information is validated, accurate, and ready for action, providing a solid foundation for strategic decision-making.

How POI Data Supercharges Data Enrichment

POI (Point-of-Interest) data refers to detailed information about specific physical locations such as restaurants, retail stores, landmarks, and other notable places. This data offers insights into various locations, their operating details, and surrounding areas. With POI data, you can gain a deeper understanding of geographic contexts, which play a crucial role in targeted marketing and customer segmentation efforts.

POI data is a valuable source of information that enables you to enrich your current datasets with comprehensive details about surrounding locations. The result is a more detailed and holistic view of your customer’s environment.

POI data can be used to enrich existing data by adding valuable contextual information that offers you a statistical edge. Here’s how:

1. Business Longevity and Local Attractions

By integrating POI data with your customer data, you can gain insights into the longevity of businesses in a given area and nearby attractions that add value to the location.

Understanding which types of businesses have a long-standing presence—such as high-end retail stores or family restaurants—can help you craft targeted marketing messages and refine your outreach efforts. Utilizing these insights into established businesses and popular attractions can elevate your marketing strategies and boost conversion rates.

2. Competitor Proximity and Complementary Businesses

Understanding the proximity of competitors or complementary businesses around a given POI can help you tailor your sales and marketing strategies. 

For example, if a competitor is located nearby you can create lucrative offers to lure customers or offer complementary services that enhance your customer relationships. This information, combined with third-party data, enables informed decisions and allows you to stay ahead in competitive markets.

Incorporating POI data into your data enrichment process not only enhances the quality of your datasets but also gives you deeper insights into market trends, customer behavior, and local competition. This enriched data is particularly valuable if you’re focused on enhancing third-party logistics, helping you optimize delivery routes, and improving operational efficiency.

By leveraging this additional information, you can make smarter, data-driven decisions and optimize your sales teams and workflows accordingly.

Examples of Data Enrichment with POI Data (Use Cases)

To illustrate the impact of POI data on data enrichment, let’s explore some real-world examples from various industries. From optimizing site selection to enhancing marketing strategies, various businesses across different sectors have successfully leveraged enriched datasets to gain a competitive edge.

These use cases demonstrate how you can harness the power of POI data to make smarter decisions, improve operational efficiency, and unlock new growth opportunities.

1. Tensorflight: Optimizing Real Estate Site Selection

Tensorflight, a tech company specializing in property analytics for the insurance industry, turned to dataplor to enhance its real estate data for more accurate property evaluations. By integrating dataplor’s POI data with their proprietary models, they were able to improve the geocoding accuracy and refine building occupancy and tenant type classifications.

This enriched data helped Tensorflight provide insurers with more detailed property insights, ensuring that building replacement costs and occupancy risks were calculated with greater precision. The collaboration led to significant improvements in data-driven decision-making for property insurance, enabling more accurate real estate assessments.

2. IKEA: Streamlining Delivery for Better Customer Service

IKEA, known for its large-scale furniture retail operations, significantly improved its last-mile delivery efficiency by leveraging geospatial data and investing in advanced software to optimize delivery routes. 

Through route planning and monitoring of truckloads, IKEA Retail Denmark was able to pilot a program that reduced delivery times and operational costs. This focus on data-driven logistics resulted in streamlined processes and improved customer satisfaction by ensuring timely deliveries.

Empower Your Data Enrichment Journey with dataplor

In today’s data-driven and competitive world, staying ahead requires making timely, well-informed decisions. Data enrichment is no longer a luxury you can conveniently ignore; it has become a necessity for gaining a deeper understanding of your customers, optimizing market strategies, and driving operational efficiencies.

By enriching existing datasets with high-quality, accurate information, you can unlock the true potential of your business data. And that’s where our advanced and comprehensive POI data comes into play.

With our comprehensive location data, we provide you with the tools you need to stay competitive in your industry. Whether you’re enhancing customer profiles, improving decision-making, or optimizing workflows, dataplor’s POI data delivers valuable insights that can propel your business forward.

Request a sample to explore our POI data solutions and learn how our data enrichment services can take your business to new heights. 
Learn more about us and start your journey with dataplor to harness the power of data enrichment.

In today’s competitive business landscape, understanding your competitors’ strategies and predicting their next move offers a significant advantage. This practice, known as competitive intelligence, involves gathering and analyzing data to uncover insights into the strengths, weaknesses, and potential business strategies of your direct competitors. 

Competitive intelligence has become an essential tool in today’s market, enabling businesses to make informed decisions and develop strategic plans to outpace both direct and indirect competitors. A thorough competitive intelligence analysis provides actionable insights that empower  sales, product, and marketing teams to refine their strategies and stay ahead.

In this article, we’ll cover the fundamentals of competitive intelligence, ethical data sources, and how to integrate competitive intelligence into your broader business strategy. Additionally, we’ll explore emerging trends in competitive intelligence to help sharpen your competitive insights.

Foundations of Competitive Intelligence

Competitive intelligence involves systematically gathering, analyzing, and applying data to anticipate competitors’ strategies. This process helps identify new market opportunities, reduce risks, and craft well-informed strategies that give you a competitive edge.

Effectively leveraging competitive intelligence is key to driving growth and staying ahead in the market. 

Leveraging Location Data in Competitive Intelligence

Location data is a valuable resource for businesses looking to enhance their competitive strategies. By utilizing these insights, companies can identify trends, optimize operations, and make more informed decisions. Whether it’s understanding competitors’ strategies or improving operational efficiency, location data offers a deeper understanding of the market and uncovers new opportunities for growth.


Here are some specific ways location data can contribute to competitive intelligence:

1. Market Penetration and Expansion:

• Identify Untapped Markets: Discover regions where your competitors have a limited presence or are underserved.
• Optimize Store Locations: Analyze competitor store locations to determine the best areas for expanding your own retail footprint.
• Assess Market Saturation: Determine the level of competition in specific markets to avoid oversaturation.

2. Supply Chain Optimization:

• Evaluate Supplier Locations: Assess the geographic distribution of your competitors’ suppliers to identify potential sourcing opportunities.
• Optimize Logistics Routes: Analyze transportation patterns to optimize your supply chain and reduce costs.

3. Risk Mitigation:

• Monitor Competitor Expansion: Track your competitors’ geographic expansion plans to identify potential threats to your market share.
• Assess Natural Disaster Risks: Evaluate the location of your facilities and supply chains in relation to natural disaster zones.

Future Trends in Competitive Intelligence

The competitive landscape is evolving rapidly due to constant technological advancements. To stay ahead, businesses must harness the power of real-time data and predictive analytics. dataplor’s ability to provide accurate and timely location data offers a unique advantage in this area.

• Real-Time Insights and Predictive Analytics: By using location data, businesses can create predictive models that forecast future trends, such as where customers are moving, where competitors are expanding, and new opportunities in the market.
• Interactive Dashboards: Location data can be used to create interactive dashboards that visualize complex data, helping decision-makers identify trends, anomalies, and opportunities.
• Geospatial Analysis: Businesses can use location data to conduct in-depth geospatial analysis, visualizing customer distribution, analyzing competitor market share, and identifying optimal locations for expansion or marketing campaigns.

As businesses strive to gain a competitive edge, real-time location data will continue to play a pivotal role. dataplor’s offerings provide the foundation for businesses to leverage this valuable asset. By harnessing the power of real-time insights, predictive analytics, and enhanced visualization, companies can make informed decisions, anticipate market trends, and stay ahead of the competition.

Partner with dataplor for Cutting-Edge Competitive Intelligence

In today’s dynamic business environment, leveraging competitive intelligence is essential for making informed strategic decisions. Accurate location data can be the competitive intel needed to help you stay ahead of your rivals.

dataplor provides comprehensive location data to enhance your business’s competitive intelligence capabilities. By utilizing our expertise, you can gain a clearer view of your competitors’ strategies and stay ahead in the market.

Get in touch with us today to explore how our advanced competitive intelligence can drive your success.

In today’s environment, location data plays a pivotal role in shaping industry strategies and driving decision-making. By leveraging location data, companies can uncover new opportunities, streamline operations, and rapidly navigate complex global markets.

Location data is critical not only for chief data officers, geospatial data scientists, and global growth officers, but also for a wide range of professionals across various industries. According to a MarketsandMarkets report, the global location analytics market is projected to grow from $20.6 billion in 2023 to $38.5 billion by 2028, driven by the increasing adoption of location intelligence among leading Fortune 500 companies.

In this article, we will examine the role of location data in business strategy, the rising significance of this data among industry leaders, and how top companies are leveraging our services to unlock new markets for global growth.

What is Location Data?

Location data is geographic information that pinpoints the physical location of a place, person, or object on earth. It can help businesses decide where to expand, optimize operations, or target specific markets.

Location-based data is gathered through multiple methods and incorporates a range of metrics: machine learning, advanced image recognition, artificial intelligence, large language models, and global human validators.         

With dataplor, businesses can get the spatial data they need to drive their strategic decision-making.      

Tools and Technologies for Visualizing Location Data

Managing and analyzing location data requires advanced spatial analytics tools and Geographic Information Systems (GIS). These tools are designed to process vast amounts of geographic data, transforming raw data into actionable insights that strengthen business decisions.Spatial analytics tools like Esri and CARTO are well known for their capabilities in location data visualization.

Source: Esri

Caption: The above is a visual representation of dataplor’s data on Esri’s platform.

Esri’s ArcGIS offers an entire suite of tools for location information, mapping, spatial analysis and data visualization. Businesses can integrate multiple data sources, analyze geographic trends and points of interest (POI), and make real-world decisions based on spatial patterns.  

Source: CARTO

Caption: The above is a visual representation of dataplor’s data on CARTO’s platform.

CARTO offers robust location intelligence solutions that enable users to visualize and analyze spatial data effortlessly. Their cloud-based platform supports real-time data processing, making it ideal for businesses that require up-to-date insights.

Applications Across Industries

Location intelligence is a powerful asset across many industries, offering a range of use cases.

From retail to telecommunications, companies leverage this data to gain a competitive edge, make informed decisions, and unlock new opportunities. Here’s how various sectors use different types of location data to achieve success.

Retail

For retailers, location data is key to site selection. By analyzing demographic and geographic data, companies can identify prime locations for new stores that maximize profitability and effectively reach their target audience. 

For example, by understanding their local competition, retailers can position themselves in high-demand areas to outpace their competitors and increase revenue.

Third-Party Logistics (3PL) 

In third-party logistics, location data can help optimize route planning and delivery times. Companies like Uber use this data to streamline last-mile fulfillment, ensure on-time deliveries, and reduce operational costs. 

Accurate location data allows logistics providers to plan the most efficient routes, reduce fuel consumption, and improve overall service levels while boosting revenue.

Real Estate

Real estate companies use location data to make better investment decisions. By analyzing property values, surrounding amenities, and future development plans, they can find high-potential areas to invest in. 

This data also helps assess a location’s risk factors so companies can make informed decisions that yield long-term returns.

Telecommunications

Location data is key to network expansion and optimization in the telecom industry. Companies use this data to find areas of high demand for services, allowing them to reach new audiences and provide coverage. 

By analyzing geographic trends, these companies can place cell towers and other infrastructure in the right places to maximize service quality and customer satisfaction.

Enhancing Business Operations and Customer Experience

Location data is key to business operations and customer experience. By using this data, companies can refine processes, better target customers, and gain a competitive edge.

Here’s how:

• Improving Business Operations: One major advantage of location data is its ability to optimize supply chains. Businesses can use this data to identify the most efficient delivery routes, helping to cut down on both delivery times and costs. For example, a company can analyze geographic constraints to ensure timely deliveries and minimize disruptions. This level of precision in supply chain management means better resource allocation and higher operational efficiency.    
  
• Competitive Intelligence: In today’s competitive landscape, understanding where your competitors operate and how they perform is essential. Location data gives insights into competitor activity, helping businesses identify market gaps and capitalize on them. By analyzing the locations of competing stores, companies can strategically position themselves in underserved areas, capture market share, and attract new customers.
  
• Operational Enhancements and Customer Experience: The advantages of location data extend beyond internal operations. Ride-sharing companies like Uber use location data to optimize driver routes, ensuring timely and efficient service for customers. Similarly, food delivery services use this data to connect customers with nearby restaurants, resulting in quicker deliveries and a better dining experience.

Challenges and Solutions in Location Data Usage

As businesses opt in and rely on location data for decision making, they face several challenges that can impact the quality and reliability of the data. Understanding and addressing these challenges is key to using location data successfully.

Data Accuracy

One of the biggest challenges with location data is ensuring accuracy. Inaccurate or out-of-date data can lead to bad decisions and costly mistakes. 

While open source data such as OpenStreetMap (OSM) is attractive due to its low cost, the quality can vary significantly. This can result in issues like fabricated or wrong points of interest, missing data points, and outdated information.  

These inaccuracies can derail your business strategy, making reliable data essential for any organization.

Privacy

With more companies using location data, concerns around data privacy are growing. Organizations must navigate complex regulations and ethical considerations to ensure that the data they use respects individuals’ privacy rights. 

This means implementing robust data governance and adhering to GDPR regulations, which dictate how location data is collected, stored, and used.

Integration Complexity

Integrating location data from multiple sources is a big challenge. Different data sets can use different formats, schemas, and standards, so combining and analyzing the data is challenging. This complexity prevents organizations from getting a complete view of their operations and market.

OSM Data vs. Paid Data

Open source data sources like OSM offer but often lack consistency and timeliness. If you buy location data from dataplor, these challenges are solved. Paid data providers ensure accuracy, quality, and up-to-date information through advanced technology and rigorous quality control measures. 

For example, dataplor’s proprietary machine learning algorithms and human validation processes ensure that the data is precise, current, and reliable.

How dataplor Solves These Challenges

dataplor offers real-time, globally scaled location data that solves businesses’ biggest challenges. The data we provide is accurate, current, and validated by local experts to meet the highest standards.

By combining technology with human expertise, dataplor provides a robust solution that helps organizations overcome the challenges of using location data to confidently make informed data-driven decisions.

Fuel Data-Driven Decision Making with dataplor

When used effectively, location datasets can unlock new opportunities, optimize operations, and drive long-term growth. 

Using accurate, real-time location data, companies can make informed decisions that improve customer experiences, streamline logistics, and get ahead of the competition. Integrating location data into your business can offer significant advantages, from finding the best retail locations to optimizing supply chains.

Working with dataplor gives you location intelligence solutions tailored to your needs. With a focus on data accuracy and quality, dataplor provides your business with the necessary tools to navigate complex markets and make impactful decisions

Contact us to learn more or request a sample today. 

Let’s get started.

Technology is transforming the way companies are growing, especially through the use of location intelligence. At dataplor, we empower many of the world’s largest companies by delivering real-time, accurate location data. This valuable geographic information can be used to fuel advanced technologies and data systems across a number of industries.

By integrating diverse data sources into their business intelligence systems, companies can elevate their location analytics and data visualization capabilities. This enriched business data enables more effective marketing campaigns and resource optimization, ultimately leading to better decisions across the board.

What is Location Intelligence?

Location intelligence is gained by analyzing geospatial data collected from various sources like GIS, satellite imaging, drones, census reports, and more. While this process can be time-consuming and complex, dataplor simplifies it by streamlining the data collection and delivering relevant point-of-interest data directly to your business. With this information in hand, your data analysis team can easily identify marketing and retail opportunities as well as new possibilities across various industries.

Applications of Location Intelligence Solutions

Location data holds the key to unlocking smarter business strategies across various sectors, including retail, healthcare, real estate, insurance, and more. This data-driven approach facilitates informed decision-making and maximizes revenue through several use cases:

• Mapping: Location intelligence enables GIS and other spatial data technologies to create intuitive dashboards and visualizations, helping to identify patterns and potential relationships within and between locations.
• Site Selection: Insights from geographic data allow organizations to assess the pros and cons of potential real estate locations, optimizing their site selection for competitive advantage.
• Competitor Investigation: Gain a deep understanding of the competition to identify market opportunities and areas to target.
• Supply Chain Optimization: Use location data to optimize logistics, distribution, and inventory management.
• Risk Assessment: Identify potential risks by considering factors like ROI forecasting and fraud prevention.
• Customer Segmentation: Customize product promotions and offerings tailored to consumer demand based on location data. 
• Real Estate Valuation: Determine property values based on location-specific factors like proximity to amenities, schools, and transportation.
• Urban Planning: Forecast which areas are most likely to see substantial growth, and predict the best areas for development.

Comprehensive Location Insights From dataplor

As a leading provider of global location intelligence, dataplor utilizes advanced machine learning, image recognition, and artificial intelligence technologies to examine vast arrays of data across more than 250 million locations in over 200 countries and territories. This commitment to quality ensures that you can make well-informed, data-driven business decisions. Our international team of analysts and human validators leverage their local experience and language fluency to maintain high data accuracy, reduce errors, and identify duplicates in real-time.

Reach Out to Our Experts for More Information

Partner with a provider committed to giving you a competitive edge. dataplor is your global partner in location intelligence, helping your business optimize customer experiences, improve supply chain functionality, and enhance financial services through our accurate Point-of-Interest (POI) data. Our industry-leading quality control procedures ensure you receive highly accurate and relevant location data that you can trust. Contact our experts to learn how our data can strengthen your business strategy and support data science initiatives, urban planning, CRM, IoT applications, pricing strategies, and more.