Most companies don’t have a location data problem. The data and the signals exist. The real challenge is turning millions of rows of POI and foot traffic data into something a business can actually act on. 

Enter Emily Lisle, Head of Product at Dataplor. In her role, she builds solutions that deliver global location data to clients, helping them cut through market complexity and make more confident, growth-driving decisions. Emily got her start in the industry helping a festival app better understand fan behavior through movement data. That experience eventually led her to Dataplor, where she was encouraged to “try out new methods, explore new opportunities, and actually take the time to build something new.”

Emily joined Matt Forrest on Spatial Stack to discuss global POI data, the broken workflow between raw location data and real business decisions, and how AI is starting to change the equation. Here’s what stood out.

How Dataplor Turns Raw Data into Real Answers

One of the most honest moments in the conversation was when Emily described what Dataplor often hears from prospects: “We really want to use your data, but we don’t have the capacity or the technical skill on our team to do that.” 

To fill that gap, the Dataplor team launched a SaaS platform with the goal of helping people understand what questions they should be asking. That meant:

• Pre-built analyses (like same-store year-over-year foot traffic) that answer well-defined questions without requiring raw data access
• Flexible filters so users can define specific groups of POIs based on any attribute or segmentation. (Not just “Starbucks vs. Caribou Coffee,” but “Starbucks locations that opened before a certain date in a specific metro, compared to all other coffee shops that opened in that same window”)
• Easy CSV export for users who want to do their own downstream analysis in Excel

The result is a platform that serves two kinds of users at once: the non-technical strategist who wants to go straight to an insight, and the data-savvy analyst who wants to pull clean, filtered data quickly.

Why Global Data Is Non-Negotiable for International Companies

Emily made it clear: if you’re analyzing Starbucks and you only have US data, you have about 50% of the picture. For any Fortune 1000 company with international operations, domestic-only foot traffic data is incomplete and actively misleading for competitive or financial analysis.

But operating at global scale introduces challenges: consistent schemas across wildly different markets, meaningful quality benchmarks for regions as different as Egypt and Connecticut, and mobility data that behaves very differently depending on the country’s privacy landscape. 

Dataplor’s POI data started with strong coverage in Latin America (with actual boots on the ground to collect data) and has since expanded to a genuinely global footprint, covering more than 250 countries and territories. When it came time to layer in foot traffic data, going global wasn’t optional. 

A Real Use Case: Finding the Right Distribution Partner in Mexico

One of the more concrete examples Emily shared involved a Consumer Packaged Goods (CPG) company using the platform for a market expansion analysis.

The company was evaluating which retail partners to prioritize in a new market in Mexico. The intuitive assumption was that more locations equals more reach, exposure, and more opportunities to move product.

The data told a different story. When they compared foot traffic across several brands—including Costco and some more regional players—they found that Costco, despite having far fewer locations, delivered higher total audience exposure than the regional brands combined. They had fewer doors, but each door received much more traffic.

That’s the kind of insight that changes an actual business decision. And it came from a market analyst using the SaaS platform directly, not from a data science team running a custom model.

Where AI Fits In (And What It Can’t Fix) 

The conversation ended with a look at where things are heading. Emily was clear that AI’s biggest role is helping users understand which graph they should be looking at and what it means for their business. That layer of personalization, she noted, is something you just can’t get to with standard reports and maps.

Dataplor is actively building toward an AI layer in the platform that can generate reports and synthesize answers to open-ended questions, but the ground truth layer has to underpin all of it. As Emily put it, you can’t just run an AI system over your entire dataset and assume it’s working. Dataplor maintains an international team of validators doing heavy manual review precisely because that foundation of trust becomes more important, not less, as AI gets more involved.

Listen to the full episode here. If you’re ready to see what global location data can do for your business, let’s talk.

Every year, Shoptalk brings together thousands of retail leaders, emerging brands, and technology innovators to talk about where the industry is heading. This year’s conference was squarely focused on AI; how it can expand what their teams are capable of, how it can solve long-standing challenges in merchandising, demand planning, and customer engagement, and how to move past the hype and into real impact. 

Our team made the trip to Las Vegas to meet with players across the retail landscape firsthand, and while AI was impossible to ignore, some of our most energizing conversations kept coming back to the same question: what’s actually happening in the real world? 

Here’s what stood out.

1. CPG Distribution and Demand Visibility Is the Conversation

If there was one theme that ran through nearly every meaningful conversation we had, it was that brands are dealing with shifting demand across channels and formats, and most don’t have a clear view of how that plays out in physical retail. In fact, many conversations kept coming back to a surprisingly common concern: brands not knowing whether they’re in the right places, reaching the right customers, or growing in the right direction.These are exactly the kinds of long-standing retail challenges that Dataplor is solving with better data and smarter tools. 

2. Brands Know Where They Sell, But Not If Those Are the Right Stores

This was the most common “aha moment” we saw on the floor. Brand after brand had solid sales data, but when it came to whether they were optimally distributed or how to make a compelling case to a retailer for expansion, the answer was essentially a gut feeling. One version of this came up repeatedly: “We know where we sell, but we don’t know if those are the right stores, or how to convince retailers to put our product in more locations.” That’s exactly the gap location intelligence fills. Instead of relying on static store lists or retailer conversations alone, teams can look at real-world foot traffic patterns and retail density to prioritize where to go next, and make a data-backed case when they get there.

3. Brick and Mortar Is Having a Quiet Resurgence

For a conference where AI was the headline act, physical retail held its own in a surprising way. In our one-on-ones, a renewed focus on brick and mortar repeatedly surfaced. Many of the brand-side buyers we spoke with were almost relieved to shift the conversation to physical retail strategy. It’s not that AI and digital don’t matter, but the physical world hasn’t gone anywhere, and teams are starting to feel the gap between their digital sophistication and their visibility into what’s actually happening in stores. That energy was hard to miss. Which brings us to our final point…

4. The Physical World Is the Missing Layer in the AI Conversation

A significant portion of the attendees we met represented the digital and e-commerce sides of their businesses. This points to something important: the brands that are winning aren’t thinking about online and offline as separate problems. AI is reshaping how retailers think about decisions, and those decisions require robust, quality, and comprehensive data. The brands we spoke with are increasingly looking for ways to ground their strategies in real-world behavior, not just digital signals. Understanding the physical landscape—where demand is moving, where products should live, where to expand next—is a critical input to any well-rounded retail strategy. Talk to us to learn more.

Movement data is data about how people move through the physical world. It’s derived from anonymized location signals off mobile devices, and it’s used across retail, commercial real estate, finance, and a growing list of other industries.

If you’ve ever seen a report that says a store received 15,000 visits last month, or that a shopping center’s traffic is up 8% year over year, that came from movement data.

Foot Traffic, Trade Area, and Other Common Terms

A lot of terminology in this space gets used interchangeably. Here are the ones worth knowing.

Foot Traffic: This is the most common term. It refers to how many people visit a physical location such as a store, restaurant, mall, or city block. Retailers have used this phrase for decades. In Europe you’ll often hear footfall instead.

Mobility Data: A broader, more encompassing  term. Foot traffic tells us how many people showed up at a specific place, while mobility data also covers where they came from, what else they visited, and how travel patterns shift over time. Think of foot traffic as one slice of the bigger mobility data picture.

Trade Area: This is the geographic region a location draws its visitors from. The old way was to draw a radius on a map. Now, movement data lets you build trade areas based on where visitors actually live and work, which often looks very different.

Dwell Time: How long someone stays during a single visit. Dwell time is useful for understanding the nature of visits and benchmarking across competitors.

Visits vs. Visitors: People tend to mix these terms up. Visits = total trips, including repeats. Visitors = unique people. Someone who goes to the same coffee shop every weekday generates five visits but counts as one visitor. Most datasets report visits.

How Does Movement Data Actually Work?

The short version:

• Anonymized location signals are collected from mobile devices at scale
• Signals are cleaned, normalized, and matched against databases of real-world places (points of interest, or POIs)
• When a device’s signal falls within a known location’s footprint, that’s recorded as a visit
• Since no provider can see every device, statistical models scale the sample up to represent the broader population. While most models use a simple ratio based on the number of devices seen in the area, Dataplor uses additional factors like online popularity and modeling off the POI brand and category to arrive at a more accurate estimate of foot traffic.

What Can Brands Do With This Information?

Site Selection: Evaluate potential locations based on traffic and visitor profiles

Competitive Benchmarking: Compare your traffic against competitors

Portfolio Monitoring: Track trends across your own locations

Investment Research: Use foot traffic as a leading indicator of company performance

Urban Planning: Understand commuting patterns and pedestrian flows

Product Distribution: CPG companies can discover which stores to prioritize

Common Questions About Movement Data

Is this data tracking individuals?

No. The location signals come from devices where users have opted into location sharing. The data is anonymized and aggregated, so no names and no personal identifiers ever enter the Dataplor ecosystem. The goal is to understand patterns at a location level, not to track specific people.

Does this work outside the US?

It depends on the data provider. Many started offering US-only data and have limited international coverage. Device panels and POI quality vary significantly by region. If you operate globally, don’t just ask whether your provider has data in a country, ask how deep it is and how accurate the foundational POI data is.

Who uses this?

Retail and restaurant brands, commercial real estate firms, financial services (from hedge funds to insurance carriers, and everything in between), marketing agencies, CPG companies, tourism and economic development organizations, and increasingly tech companies that use POI and traffic data as a foundational layer in their own products.

What should I look for in a provider?

• Geographic Coverage: Does it cover the regions and categories you care about, and how deep does it go?
• POI Quality: Visit data is only as good as the map of places it’s matched against.
• Methodology: How is the modeling done, and how reflective of the real world is it?
• Update Cadence: How often is the data refreshed, and does that match your workflow?

Getting Started

Regardless of what you call it, the goal remains the same: turn human movement into action for your business to both accelerate growth and mitigate downside risk. Talk to us to learn more.

Inside growing organizations, big questions surface constantly. Where should we expand next? Which markets are saturated? How strong is competitor presence outside North America? What does commercial activity actually look like on the ground?

The answers to those questions influence capital allocation, hiring plans, and long-term strategy. And they often fall on teams that are expected to deliver clarity quickly.

Professionals who consistently deliver confident, defensible answers are those who get noticed. Visibility builds trust with executive teams, and trust leads to larger initiatives, broader ownership, and career acceleration.

The challenge is not identifying the questions, but accessing the right intelligence to answer them with clarity and conviction.

Global Expansion Decisions with Incomplete Visibility

Commercial real estate groups evaluate development opportunities across continents. Buy-side investment firms analyze business density as a signal of economic momentum. Retail, quick service restaurants, and CPG brands assess store networks and competitive saturation before committing capital.

Yet the data supporting these decisions is frequently fragmented.

Many organizations remain heavily concentrated in domestic markets, as a result of global data being inconsistent or difficult to validate. Some datasets are incomplete or outdated; others lack transparency in how the data is sourced and refreshed. Filtering and segmentation capabilities can also be limited, restricting how deeply teams analyze markets. And even when the data is available, integrating it into existing workflows often requires manual effort.

In the end, you’re still responsible for strategic answers, but without the infrastructure to confidently stand behind them. That uncertainty shows. Recommendations become cautious. And cautious recommendations rarely earn visibility with leadership or open doors to bigger opportunities.

(Read: Turn Global Location Data into Confident Decisions.)

Why a Global Platform is a Game Changer

Stitching data together manually may fill gaps temporarily, but it doesn’t create a scalable foundation. A centralized platform does.

Most professionals responsible for expansion and strategy are not engineers. They’re real estate leaders, investment analysts, and go-to-market teams who want the ability to explore markets without submitting technical requests or waiting on manual exports.

A global location intelligence platform removes that dependency. It provides a structured, standardized environment where users can analyze and visualize global POI data directly. Instead of relaying information from multiple sources, you can generate insights yourself, apply filters, compare regions, and validate assumptions in real time.

That independence changes how you show up in the room. When you can walk into leadership meetings with clear validation and well-supported analysis, you move from reacting to requests to influencing direction. And in growing organizations, the people who shape direction are the ones promoted to lead it.

From Insight to Influence

Dataplor’s Global Platform delivers comprehensive, standardized places (i.e. point-of-interest) coverage across hard-to-source markets well beyond North America. Designed for both technical and non-technical users, the platform scales with international growth and reduces the friction that often slows strategic momentum.

Specifically, the platform empowers you to:

Validate market opportunities with speed: Assess commercial density, brand presence, and category distribution across countries within a single environment.

Map competitive landscapes dynamically: Compare competitor footprints across regions using standardized global taxonomies.

Optimize go-to-market strategy in real time: Align expansion, territory planning, and partnership efforts with up-to-date, structured intelligence.

Enable cross-functional visibility: Share consistent data across investment, strategy, operations, and executive teams.

Executives respond to clarity, speed, and confidence. When you consistently bring those qualities into strategic conversations, you stop being viewed as a contributor and start being recognized as a strategic leader.

The right platform does more than power your team’s growth. It accelerates your own.

Ready for a demo?