Mobile Location Data: Powerful Insights or Expensive Noise? (The Difference Is in the Details)

In the commercial real estate industry, data-driven decision-making has become essential. Among the various data sources available, mobile location data (MLD) has emerged as a powerful tool, but one that requires careful understanding and application. I talked about this many years ago, not long after this data became mainstream in #CRE, but it's time for an updated look.

The Evolution of Location Analysis

Traditional location analysis relied on simple geographic boundaries: the 1-3-5 mile radius ring, the 10-minute drive time, or hand-drawn polygons. While these methods were straightforward, they contained a fundamental flaw: they assumed everyone within these boundaries was a potential customer or visitor.

While this was the best data available at the time, this assumption created significant distortions. When you pull demographic data from a three-mile radius, you're including populations that may never interact with your property. The result? Decisions based on irrelevant information.

The Promise of Mobile Location Data

Mobile location data represents a significant advancement over traditional methods. Instead of making assumptions about theoretical boundaries, MLD shows actual movement patterns and visitation behavior. This granular view of human activity offers unprecedented insights into:

Trade area definition: Understanding where your actual visitors come from, not where you think they come from
Path to purchase analysis: Tracking customer journeys and behavior patterns
Brand affinity mapping: Identifying where your visitors go before and after visiting your property
Competitive analysis: Understanding foot traffic patterns across multiple locations

When used correctly, mobile location data provides a more accurate picture of your property's actual draw and appeal.

The Critical Challenges

However, MLD isn't without it's issues (no data set is) and comes with significant challenges that must be understood before drawing any conclusions:

1. The Visitation Trap

Perhaps the most dangerous misuse of mobile location data is treating raw visitation numbers as performance indicators. In other words, the attention grabbing "Visitation Ranking" of sites.

Consider this scenario: Property A reports 1 million visitors while Property B reports 2 million. Does this mean Property B is performing better? Not necessarily—and often, not at all.

Research comparing visitation data to actual sales performance for major retailers has shown some correlation, but it is by no means definitive. Although it varies by retail type, in many cases, locations with higher ranked visitation numbers produced average to below average sales. Why? Because visitation numbers lack context. Without understanding who these visitors are, what they're doing, and how they're interacting with the space/property, these numbers are meaningless.

While this data can create very generalized performance information (mostly directional trends), it is by no means accurate enough to produce definitive performance analysis.

2. The Context Problem

Numbers require context to be meaningful. Mobile location data vendors often present various visitation counts and rankings without the necessary framework or end-user education to interpret them accurately. This creates a dangerous dynamic where decision-makers fixate on impressive-sounding numbers that may not translate to real-world value.

Visitation data, when presented in isolation, becomes what data scientists call a "zombie statistic"—a number that spreads widely, gets cited out of context, and refuses to die despite being misleading.

3. Data Privacy and Quality Concerns

The mobile location data landscape is undergoing transformation driven by regulatory changes, technological shifts, and sample size challenges that directly impact data quality.

The Regulatory Wave

Multiple states have enacted or are considering legislation that prohibits the sale of precise geolocation data, with Oregon and Maryland leading the way by banning sales of location data that can pinpoint individuals within 1,750 feet. Virginia has passed similar legislation with bipartisan support, with advocates citing concerns about domestic violence, stalking, and consumer scams.

In 2025, states including Massachusetts are proposing requirements for separate Location Privacy Policies for entities collecting geolocation data, while California's Privacy Protection Agency has initiated investigative sweeps of the location data industry. These emerging laws increasingly focus on AI accountability, biometric data protection, and expanded definitions of sensitive information, with Maryland's Online Data Privacy Act emerging as a potential model for future legislation.

The implications for commercial real estate are significant: data providers must now navigate a complex patchwork of state regulations, each with different definitions, thresholds, and requirements.

What this means for CRE and the quality of MLD remains to be seen, but this is definitely not being talked about enough in an industry where "if you are off by an inch, you are off by a mile." (David Birnbrey, CEO of TSCG)

Sample Size and Statistical Validity

Beyond regulatory challenges, the fundamental question of sample size has become critical. Research on major mobile location datasets shows sampling rates averaging around 7.5% of the US population, with significant fluctuation over time. While this might sound substantial, the devil is in the details.

Studies reveal that minority groups, Hispanic populations, low-income households, and individuals with lower education levels show higher levels of underrepresentation, with this bias notably increasing following the COVID-19 pandemic. This means that even with seemingly large sample sizes, the data may not accurately represent your actual customer base or trade area demographics.

The accuracy challenges compound this issue. Industry research shows mobile location data accuracy averaging around 30 meters, but with significant variations by city, ranging from 21 meters in Boston to 38 meters in Chicago. Some studies have found that as much as 15% of sampled location data from certain sources was incorrect.

Even if the question isn't just about having "enough" data—it's about whether the data you have represents the population you're trying to understand. A provider might report millions of data points, but if those points systematically underrepresent key demographic groups or suffer from accuracy issues, the analysis built on that foundation becomes unreliable.

Critical Questions for Your Data Provider

Given these challenges, demand specific answers for your data provider/platform:

What is your current sampling rate, and how has it changed over the past three years?
How are you addressing demographic biases in your panel, particularly for underrepresented groups?
What is your horizontal accuracy range, and how does it vary by geography and environment?
How are iOS opt-out rates impacting your data quality and coverage? What is your iOS/Android data ratio?
What statistical adjustments or weighting methods do you apply to compensate for sampling bias?
How do you account for COVID-19 disruptions in historical trend analysis?
Are you complying with new state-specific location data regulations?

If your provider can't or won't answer these questions with specifics, that's a significant red flag.

Best Practices for Using Mobile Location Data

To leverage mobile location data effectively in commercial real estate, follow these guidelines:

Demand Transparency

As mentioned above, your MLD provider should freely share:

Complete methodology documentation
How privacy changes affect their data quality
Steps taken to address data limitations
How to account for anomalies like COVID-19 disruptions

Avoid Isolation

Never use mobile location data as your sole source of insight. Integrate it with:

Demographic and psychographic data
Sales and performance metrics
Market research
And most importantly, on-the-ground observations

Mobile location data is most powerful as part of a comprehensive analytical framework, not as a standalone solution.

Ignore Rankings and Raw Visitation Numbers

Stop fixating on visitation counts and property rankings. These numbers, without proper context, tell you nothing about actual performance or potential. Instead, focus on:

Trade area definition and accuracy
Visitor origin patterns
Cross-shopping behavior
Temporal visitation patterns

Understand Your Use Case

Before requesting any mobile location data analysis, clearly define:

What specific questions are you trying to answer?
What decisions will this data inform?
What other data sources will complement this analysis?

Data without purpose is just noise.

Educate Yourself and Your Team

Don't just pass along reports. Take time to understand:

What the data actually represents
What it doesn't show
The confidence level of the findings
How to explain limitations to stakeholders

Education transforms data from a sales tool into a decision-making asset.

Beware of "Data Defense"

One of the most insidious uses of mobile location data is what we might call "data defense"—using data to justify decisions already made rather than to inform decision-making. It's easy to cherry-pick metrics that support your preferred narrative, especially when dealing with the volume of data MLD provides.

Guard against this by:

Establishing analytical frameworks before seeing results
Welcoming data that contradicts assumptions
Remaining objective about findings, even when inconvenient

The Bottom Line

Mobile location data represents a significant improvement over traditional radius rings and drive times when used appropriately. It offers real insights into actual behavior patterns rather than theoretical assumptions. However, it requires sophistication, education, and proper contextualization.

The technology itself is neither good or bad...it's how we apply it that matters. Used correctly as part of a comprehensive analytical approach, mobile location data can illuminate visitor patterns, refine trade areas, and support better decision-making in commercial real estate.

Demand transparency, seek education, and never stop asking critical questions about the data you're using. Leverage the power of MLD in the right ways to aid in your location intelligence analysis to de-risk decision making.

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Nostalgic Retail Spotlight:

DRESSBARN

Founded in 1962 (and originally two words: Dress Barn) by Roslyn Jaffe in Stamford, Connecticut, Dressbarn began as a dedicated women’s clothing retailer, quickly expanding across the U.S. and becoming a household name.

Dressbarn went public in 1982, marking its growth on the NASDAQ, and in 2011, it rebranded under Ascena Retail Group to broaden its scope. This is when the retailer's name became Dressbarn.

In 2019, faced with declining sales and changing retail dynamics, Dressbarn made the tough decision to shutter all 650 of its physical stores—shifting focus to an online-only presence under the private equity firm, Retail Ecommerce Ventures (REV).

In late 2023 and 2024, REV was experiencing financial difficulties and was reported to be exploring restructuring options, including bankruptcy. In late 2024, a new company, Omni Retail Enterprises, acquired several of REV's brands, including Pier 1, Stein Mart, and Dressbarn.

The only acknowledgement of having stores in the past can be found on their website FAQs, in the "Under New Management" section