Digital Element Announces NAT Detector — Industry’s New Standard for Accurate IP Geolocation and Risk Intelligence.

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Author: Digital Element

Our solutions provide comprehensive IP intelligence beyond geolocation, supporting advanced applications in adtech, fraud detection & prevention, cybersecurity.

Refining the Map: Digital Element Rebuilds Big City/Small City Mappings Across 25 Countries

Posted on by Digital Element

When we introduced Small City Mapping, our goal was to help customers work at the right level of geographic granularity, mapping smaller cities and suburbs to the major metropolitan areas they belong to so teams can target key markets without manually managing every surrounding city or neighborhood.

Today, we’re introducing the first round of enhancements to our Big City/Small City Offering, delivered through the existing Small City Mapping decode file available in the Support Portal. Rather than simply expanding coverage, we’ve rebuilt how metropolitan relationships are defined across 25 priority countries using country-specific research and authoritative statistical sources.

This work reflects a significant, ongoing investment of time, research, and resources in strengthening our worldwide geographic intelligence, bringing more local context to our existing data and laying the foundation for additional refinements in future releases. That investment has enabled Digital Element to become the only provider offering Big City/Small City mapping built through country-specific research and authoritative statistical sources. We make that investment for a simple reason: better data drives better outcomes for our customers.

Refining How “Big” and “Small” Are Defined

Our original mappings were developed using a standardized methodology applied consistently across countries, providing efficient worldwide coverage and a common framework for classification.

As we’ve continued working with customers around the world, we recognized an opportunity to go further. Metropolitan regions are defined differently from one country to the next, and a single global methodology can’t always capture those local distinctions.

For each of the 25 countries included in this release, our team invested in original, country-by-country research, working directly from authoritative government and statistical sources to determine:

  • which cities should serve as major metropolitan centers
  • how surrounding cities, suburbs, and municipalities should be associated with those markets

The result is a significant refinement of our existing Big City/Small City mappings that better reflects how metropolitan regions are actually organized within each country. It’s one example of our continued investment in improving geographic intelligence as customer needs evolve.

The First 25 Countries

This round targets the countries that either have Metro-level support within NetAcuity today or where we see clear customer demand:

Australia, Belgium, Brazil, Canada, China, Denmark, Finland, France, Germany, Iceland, India, Indonesia, Italy, Japan, Luxembourg, Netherlands, New Zealand, Norway, Poland, Russia, South Korea, Spain, Sweden, United Kingdom, and Vietnam.

Additional countries already planned as this initiative expands across our worldwide coverage.

Big City/Small City Mapping Example: France

France illustrates the scale of the refinement. The previous mapping classified 320 French cities as “big.” Rebuilding the mapping against INSEE‘s Urban Attraction Areas (AAV 2020), France’s official statistical geography for urban influence zones, produced a revised list of 172 big cities, with the remainder correctly reassigned as small cities tied to the appropriate metropolitan area.

MetricBeforeAfterChange
Big Cities320172−148
Small Cities30,10630,261+155
Total Rows30,44730,447—

While every country requires a different methodology, France demonstrates the impact of applying country-specific research instead of relying on a single global approach. Similar refinements were made throughout this first group of countries using each nation’s own authoritative geographic definitions.

France was not unique. We observed similar patterns across the countries reviewed, reinforcing the value of aligning each country’s mappings with authoritative local statistical sources.

What This Means for You

No action is required to benefit from these refinements:

  • Same file, better data. There is no new file or product name — the existing Small City Mapping decode file on the Support Portal will be updated with the revised mappings.
  • Preview available. If you’d like to review the changes before they go live, we can provide before-and-after versions of the Small City Mapping decode file. Contact your Client Success representative.
  • Feature Code 93 included. If you use the NetAcuity Server with Feature Code 93 (the Decode DB), all fields sourced from the Small City Mapping decode file will automatically reflect the refined big/small mappings.

A Stronger Foundation for Every Use Case

These enhancements are about more than refining city classifications. They reflect the substantial time and resources Digital Element continually invests in its data—building geographic intelligence, country by country, through original research no off-the-shelf dataset replicates. We do that work so our customers get more accurate results from the same products they already use.

As additional countries are incorporated into this initiative, customers will continue to benefit from richer local context without changing the way they access or use Small City Mapping.

This is the first step in a broader effort to continually strengthen our worldwide geographic data—an investment that pays off where it matters most: helping customers make better location-based decisions with greater confidence.

Contact your Digital Element Client Success Representative to learn more or request a before-and-after review of the refined mappings.

Long-Term IP Intelligence for Security Investigations and Incident Response 

Posted on by Digital Element

Security teams are no longer asking “what is this IP?”

They’re asking, “what has this IP been doing over time?”

That shift changes everything.

Modern threats aren’t static. Attackers rotate infrastructure, reuse residential proxies, and blend into legitimate traffic patterns over weeks or months. A point-in-time IP lookup simply isn’t enough to support effective investigations or incident response. By the time a fraudulent transaction, cyberattack, or compliance violation surfaces, the digital trail has often already gone cold.

This is where long-term IP intelligence becomes critical, and where IP Forensics by Digital Element was purpose-built to deliver.

IP Forensics is the industry’s first comprehensive historical IP intelligence lookback service. Drawing on over 24 months of behavioral IP data, it gives cybersecurity investigators, fraud prevention teams, and legal and compliance professionals the ability to trace IP addresses across time. This uncovers behavioral patterns, detecting anonymization tools, and reconstructing digital journeys that point-in-time tools simply cannot see. 

In this post, we’ll break down how IP Forensics answers the most pressing investigation questions security teams face today.

Why Long-Term IP Intelligence Matters in Modern Security

Traditional IP intelligence focuses on attributes like location, ISP, or proxy detection at a single moment. 

While useful, this snapshot misses the bigger picture:

  • Infrastructure reuse across campaigns
  • Gradual shifts in attacker behavior
  • Persistence signals across rotating IP pools
  • Relationships between seemingly unrelated traffic

IP Forensics transforms IP data into a time-series signal, allowing security teams to analyze 24+ months of behavioral history for any IP address or batch of addresses. 

That historical depth supports:

  • Behavioral pattern detection across weeks and months
  • Threat clustering across sessions and campaigns
  • Risk scoring grounded in demonstrated historical activity
  • More stable and accurate blocking decisions

In short, IP Forensics answers not just what an IP is, but what it has been doing, and for how long.

How to Map an Attacker’s IP Infrastructure Across Months of Activity

Attackers rarely rely on a single IP. 

Instead, they operate across distributed infrastructure (VPNs, residential proxies, cloud nodes, and compromised devices). 

IP Forensics is built to expose this infrastructure by looking back across time, not just at the moment.

1. Track IP Attribute Consistency Over Time

IP Forensics reveals patterns in Autonomous System Numbers (ASNs), the shift between hosting providers and residential ISPs, and geographic drift patterns over the lookback window. Even when IPs rotate, underlying infrastructure often leaves fingerprints — and IP Forensics makes those fingerprints visible.

2. Build Temporal Clusters

By analyzing time-based activity overlaps, repeated access patterns, and shared behavioral signatures across 24+ months of data, investigators can group related IPs into clusters. This allows teams to identify coordinated attacker infrastructure rather than treating each IP as an isolated event.

3. Analyze Historical Proxy and VPN Classifications

IP Forensics reveals past proxy and VPN classifications at specific points in time, including which provider was involved and what type of anonymization was used. This lets investigators identify when an IP changed behavior, detect reuse across multiple campaigns, and determine whether masking services were active at the exact moment of an incident. For teams that also need real-time proxy and VPN detection alongside historical lookback, Nodify provides 30+ contextual data points per provider and complements IP Forensics as part of a layered intelligence strategy.

In result, investigators move from chasing individual IPs to mapping entire attacker ecosystems across months of activity.

Correlating Lateral Movement with IP Persistence Signals

Lateral movement is one of the clearest indicators of a sophisticated attack, but it’s tricky to track when IP addresses are constantly changing. IP Forensics gives investigators the historical depth to connect movement events even when attackers attempt to obscure their tracks.

Step 1: Identify Initial Access Points

Start with known suspicious IPs and use IP Forensics to surface first-seen timestamps, geographic entry points, and historical classifications at the time of initial access.

Step 2: Track Session-to-IP Relationships

Correlate user sessions across multiple IPs over time, identifying IP reuse across different accounts and timing consistency between logins. IP Forensics supports both single-IP API queries and bulk batch analysis for large-scale investigations.

Step 3: Analyze Persistence Signals

Look for IPs that repeatedly reappear across the 24-month lookback window, consistent infrastructure patterns despite apparent rotation, and behavioral continuity; the same attack patterns appearing across different IPs over time.

Step 4: Enrich with Historical Masking Intelligence

IP Forensics identifies not just whether a masking service was involved, but which provider was used and when with detailed provider insights rather than simple yes/no flags. This allows investigators to detect residential proxy abuse, surface risk signals tied to historical activity, and connect lateral movement events with confidence.

Separating Shared Networks from Malicious Clusters

One of the biggest challenges in IP-based security is avoiding false positives, particularly with shared networks. 

A single IP could represent a household, a corporate network, a mobile carrier NAT, or a proxy service. Without historical context, these can look similar in the present moment. IP Forensics provides the historical intelligence needed to distinguish legitimate network sharing from coordinated malicious activity.

Behavioral Diversity Over Time

Legitimate shared networks show varied, organic behavior across the lookback window. Malicious clusters exhibit repetitive, automated patterns that remain consistent even as IPs rotate. IP Forensics surfaces these differences by providing a time-based view of how each IP has actually behaved.

Temporal Pattern Analysis

Shared networks show natural usage cycles like activity tied to waking hours, weekends, and normal browsing behavior. Attack traffic often shows unnatural consistency or burst patterns that stand out clearly in a 24-month behavioral view.

Historical Classification Context

IP Forensics reveals how an IP has been classified over time, such as residential vs. commercial vs. hosting, and whether proxy or VPN usage appeared at specific points in that history. This classification history is what separates an informed decision from a guess.

Pro DE insight: Malicious clusters tend to prioritize efficiency and scale. Legitimate shared networks show organic variability. IP Forensics makes that distinction visible across time.

Distinguishing Botnet Traffic from Dynamic Consumer IPs

Botnets are increasingly sophisticated, often leveraging residential IP space specifically to evade detection. But even the most advanced botnets leave detectable patterns. Those patterns are most visible in historical data.

Botnet Indicators in Long-Term Data

  • High request uniformity across time
  • Coordinated timing across IPs that persists over weeks
  • Repeated actions at scale with minimal variation
  • Rapid IP rotation with consistent behavioral signatures beneath the surface

Dynamic Consumer IP Indicators

  • Irregular usage patterns tied to real human behavior
  • Mixed activity types across sessions
  • Natural geographic consistency without sudden unexplained shifts
  • Session variability that reflects organic use

IP Forensics enables detection of IPs that frequently change classification, residential IPs that exhibit automated behavioral patterns over time, and historical anomalies that point to botnet membership. The platform’s 24-month lookback window is what makes these patterns visible because botnet behavior doesn’t emerge from a single data point. It reveals itself over time.

Prioritizing Blocks Using Historical Risk Patterns

Static blocklists are reactive and often outdated by the time they’re applied. IP Forensics enables a fundamentally different approach: prioritizing blocking decisions based on demonstrated historical risk, not just current-state intelligence.

Build Risk Scores Grounded in History

IP Forensics supports risk scoring that incorporates the frequency of suspicious activity across the lookback window, the duration of an IP’s involvement in detected threats, and recurrence across multiple incidents over time. This produces risk signals that are stable and evidence-based rather than reactive.

Identify and Act on High-Risk Clusters

Rather than blocking individual IPs, investigators can use IP Forensics to identify clusters with shared historical characteristics and apply blocking rules at the infrastructure level.

Apply Time-Based Risk Weighting

Not all historical risk is equally relevant! IP Forensics allows teams to weigh recent behavior more heavily while maintaining visibility into older activity. This avoids overblocking legitimate users whose IPs may have changed hands while keeping focus on IPs with recent and sustained risk signals.

IP Forensics: Built for the Investigations That Matter Most

For organizations operating in cybersecurity, fraud prevention, e-commerce, fintech, or legal and compliance functions, IP Forensics addresses a fundamental gap in conventional IP intelligence: the inability to look back.

Incidents surface weeks or months after they occur. Fraudulent chargebacks are disputed long after the transaction. Compliance reviews require evidence from a specific date in the past. In each of these scenarios, a current-state IP lookup provides almost no investigative value, but 24 months of behavioral history changes the picture entirely.

IP Forensics is designed specifically for these situations:

  • Cybersecurity analysts use it to trace attacker infrastructure across campaigns, detect lateral movement, and identify persistent threat actors operating behind rotating IP pools. 
  • Fraud prevention teams use it to validate transaction origins, reconstruct behavioral patterns, uncover false chargeback claims, and feed historical signals into machine learning models. 
  • Legal and compliance professionals use it to support litigation, sanctions reviews, and audits with reliable historical IP geolocation and masking detection evidence.

And critically, IP Forensics focuses on network-level intelligence (not personal data) and is built to comply with global privacy standards. It reveals digital behavior without crossing the line into personal identification.

Ready to Strengthen Your Investigations with Historical IP Intelligence?

Security threats don’t operate in snapshots. Your IP intelligence shouldn’t either.

IP Forensics by Digital Element delivers the 24-month behavioral lookback that modern investigations demand, helping teams map attacker infrastructure with precision, detect patterns that point-in-time tools miss.

Make smarter, more confident decisions at every stage of incident response.

Contact the Digital Element sales team to learn how IP Forensics can transform your security and fraud investigation workflows.

Frequently Asked Questions About IP Forensics

Can IP Forensics detect VPNs and proxies that were active in the past?

Yes, and this is one of IP Forensics’ most distinctive capabilities. Most VPN and proxy detection tools operate in real time, flagging masking services as they appear. IP Forensics goes further by identifying whether a VPN or proxy was active at a specific historical moment, which provider was involved, and what type of anonymization was used. 

How does IP Forensics help reduce false positives in security operations?

False positives are one of the most costly problems in security operations. They consume analyst time, create alert fatigue, and can erode trust in automated systems. IP Forensics reduces false positives by providing behavioral context that current-state intelligence cannot. Rather than flagging an IP based solely on its present-moment classification, security teams can evaluate how that IP has behaved across 24 months. 

Is IP Forensics compliant with global privacy regulations?

Yes. IP Forensics operates entirely at the network level, analyzing IP address behavior rather than collecting or processing personal data. It does not identify individuals, track named users, or access device-level information. This approach is designed to comply with global privacy frameworks including GDPR, CCPA, and equivalent regulations in other jurisdictions. 

How to Reduce Off-Target and Invalid Impressions in Media Buying

Posted on by Digital Element

In modern media buying, precision is everything. But, it’s also increasingly difficult to achieve.

Between anonymized traffic, proxy usage, and evolving fraud tactics, even sophisticated campaigns can struggle with off-target impressions and invalid traffic (IVT). The result? Wasted budget, distorted performance metrics, and less confidence in your data.

For marketers, agencies, and platforms, the challenge is clear: How do you ensure your campaigns are reaching real people, in the right places, at the right time?

This post breaks down three critical areas:

  • The best way to validate geo for campaign measurement
  • How to filter anonymized traffic in CTV environments
  • What geo checks reduce false positives when blocking out-of-market viewers

Along the way, we’ll outline a smarter, more scalable approach to improving media quality without sacrificing reach.

The Hidden Cost of Off-Target & Invalid Impressions

Off-target impressions aren’t always obvious. Campaigns can appear to perform well on the surface while quietly delivering impressions to users outside your intended geography, non-human traffic (bots or emulators), and masked or anonymized connections.

In CTV environments, the issue becomes even more complex. Signals are limited, identity is often obscured, and server-side ad insertion (SSAI) can make validation more difficult.

Invalid impressions typically stem from:

  • Data center traffic posing as residential users
  • VPNs and proxies masking true locations
  • Spoofed devices generating fake ad requests

Even a small percentage of invalid or mis-targeted impressions can skew results, impacting attribution, optimization, and ultimately ROI.

Best Way to Validate Geo for Campaign Measurement

Geo-targeting is foundational to nearly every campaign. But validating that geo, accurately and consistently, is where many strategies fall short.

Move Beyond Basic IP Targeting

Relying on raw IP data alone is no longer enough.

Today’s digital ecosystem includes:

To improve accuracy, geo validation needs to incorporate multi-signal intelligence, including:

  • IP-based location (country, region, city, ZIP/postal code)
  • Connection type (residential vs. hosting provider)
  • Proxy and VPN detection signals

This layered approach helps distinguish between a real user in your target market and a masked or misrepresented connection. 

Validate Geo at Every Stage of the Campaign

One of the most common gaps in media buying is only validating geo at the targeting stage. A more effective approach applies geo validation across the full lifecycle of a campaign.

In the pre-bid stage, this means having to exclude impressions from non-target regions, filtering out known proxy or data center traffic, and prioritizing high-confidence geo signals. Once the campaign has moved to an in-flight status, the focus will shift to monitoring delivery patterns and detecting anomalies or unexpected geo distributions. Once the campaign finishes, the process continues by comparing intended versus actual delivery and identifying discrepancies to refine future targeting. 

This continuous validation loop ensures that geo accuracy isn’t just assumed, it’s verified.

Prioritize Presence, Not Assumption

Not all IP geolocation location data signals are equal. One of the main differences between them is whether a user is physically in a location or if they are simply associated with it. 

To reduce off-target impressions, favor real-time, presence-based geo signals over inferred or outdated location data, and refresh geo signals in longer sessions, namely in CTV.

This is particularly important for campaigns with strict geographic boundaries, such as local advertising, regional compliance, or market-specific messaging.

How to Filter Anonymized Traffic to Reduce Invalid Impressions in CTV

CTV represents one of the fastest-growing and most complex channels in digital media. It also presents unique challenges when it comes to filtering anonymized and invalid traffic.

Understand Where Anonymized Traffic Comes From

In CTV, anonymization often results from:

  • SSAI environments that mask device-level signals
  • IP obfuscation through proxies or VPNs
  • Limited access to persistent identifiers

While not all anonymized traffic is invalid, it does introduce uncertainty, and that uncertainty can lower campaign quality. 

Identify and Filter Non-Residential Traffic

A critical step in reducing invalid impressions is distinguishing between residential and non-residential traffic.Non-residential traffic (such as data centers or cloud infrastructure) is more likely to be associated with bot activity, emulated devices, and fraudulent impression generation.

Filtering strategies include blocking known hosting provider ip ranges, flagging traffic with inconsistent or missing device signals, and monitoring for unnatural traffic patterns (e.g., high-volume bursts). Strategies like these are powered by combining NetAcuity’s network classification with Nodify’s real-time proxy and VPN detection giving you a more complete picture of whether traffic is legitimate before a single impression is purchased.

Leverage Device and Network Intelligence

CTV environments require a more nuanced approach to validation.

Key signals to evaluate:

  • Device type and authenticity (smart TV vs emulator)
  • Network consistency (does the device behavior match the IP environment?)
  • Household-level patterns (does usage align with expected behavior?)

When these signals align, confidence in the impression increases. When they don’t, it’s a strong indicator that further filtering is needed.

Apply Pre-Bid Filtering for Maximum Efficiency

The most effective way to reduce invalid impressions is to prevent them from being purchased in the first place.

Pre-bid filtering enables:

  • Blocking suspicious traffic before it enters the auction
  • Reducing wasted spend on low-quality impressions
  • Improving overall campaign efficiency

When combined with post-bid analysis, this creates a robust system for maintaining media quality across channels.

What Geo Checks Reduce False Positives When Blocking Out-of-Market Viewers?

Over-filtering can be just as damaging as under-filtering.

Blocking too aggressively can exclude legitimate users, reducing reach and limiting campaign effectiveness. The key is finding the right balance between precision and flexibility.

Use Confidence-Based Decisioning

Instead of treating geo validation as a binary decision (valid vs. invalid), apply confidence scoring. Be sure to evaluate geo accuracy confidence, proxy likelihood, and network trust level for each impression. 

This allows for more nuanced actions: high-confidence impressions can be allowed, medium confidence ones can be monitored or adjusted, and low confidence ones can be blocked or excluded. 

This approach reduces unnecessary exclusions while maintaining strong protection against invalid traffic.

Cross-Validate Multiple Signals

Relying on a single signal increases the risk of false positives.

To improve accuracy:

  • Compare IP-based geo with ISP data
  • Validate time zone alignment
  • Analyze behavioral consistency over time

When signals align, confidence increases. When they conflict, impressions can be flagged rather than immediately blocked. Tools like NetAcuity and Nodify work together here. NetAcuity validates geo and network type while Nodify identifies anonymized connections in real time, reducing the risk of acting on a single, potentially misleading signal.

Account for Real-World Edge Cases

Not all anomalies are fraudulent. Legitimate scenarios that can trigger false positives include travelers using mobile networks, households with dynamic IP addresses, and smart TVs connected through shared networks

To account for these, allow for reasonable geo variance (e.g., radius-based targeting), use historical data to validate consistency, and avoid over-penalizing mobile carrier traffic.

This ensures that real users aren’t unintentionally excluded.

Why This Matters for the Future of Media Buying

As media ecosystems become more complex, data integrity becomes a competitive advantage. Without reliable validation, campaign performance metrics can’t be trusted, optimization decisions become less effective, and budget allocation becomes increasingly inefficient. 

On the other hand, organizations that invest in high-quality geo intelligence, advanced traffic validation, and multi-layered filtering strategies are better positioned to reach real audiences with precision, reduce wasted spend, and make smarter, data-driven decisions.

Where Digital Element Fits Into Your Media Strategy

Reducing off-target and invalid impressions requires more than surface-level filtering. It demands trusted, high-quality IP intelligence that works across every stage of media buying.

That’s where Digital Element comes in.

How Digital Element Supports More Accurate Media Buying

1. High-Precision Geo Targeting: Granular location data at the ZIP, city, and DMA level, helping ensure impressions are served to users who are actually within your intended market.

2. Anonymized Traffic Identification: With built-in proxy and VPN detection, NetAcuity and Nodify help identify masked or anonymized connections that can distort campaign performance and inflate reach metrics.

3. Network & Connection Intelligence: By classifying IPs as residential, mobile, or data center, you can enable smarter filtering to  reduce invalid impressions without overblocking legitimate users.

4. Scalable Pre- and Post-Bid Activation: Whether applied pre-bid to prevent wasted spend or post-bid for validation and reporting, our products integrate seamlessly into existing media workflows.

Learn more about NetAcuity

Take Control of Your Media Quality with Digital Element

By partnering with Digital Element, you gain access to trusted IP intelligence that helps ensure every impression is grounded in accurate, real-world data. IP data intelligence from Digital Element can empower your team to reach the right audiences, filter out low-quality traffic, and make better decisions based on reliable insights.

If you’re ready to take a more precise, data-driven approach to media buying, now is the time to act. Request a free consultation to see how our tools can help you reduce wasted impressions, strengthen campaign performance, and bring greater confidence to your results.

7 Benefits of Digital Rights Management (DRM) for Content Protection, Compliance, & Revenue

Posted on by Digital Element

A big part of how today’s digital content economy functions is through digital rights management, where content owners use digital asset management systems and other DRM tools to require payment for access to their material.

While DRM tools are great for locking down content, they also deliver additional benefits to organizations that use them, especially when paired with technologies like IP geolocation and VPN/proxy detection to enforce regional access and prevent abuse at scale.

This article explores the benefits of implementing digital rights management software, and how DRM tools can help organizations across a wide spectrum of industries stay compliant with licensing agreements, protect profits, and keep sensitive information out of the hands of unauthorized users.

What is digital rights management (DRM) and how does it work?

Digital rights management (DRM) is technology created to prevent illegal use, theft, and distribution of digital content. DRM protects digital content by employing multiple strategies to eliminate methods for creating duplicates of protected files or sending those files to others.

DRM solutions are multifaceted and IP holders can configure them to meet a variety of use cases. Limitations that organizations can implement via DRM include limits on sharing, printing, forwarding, downloading, saving copies, editing, or maintaining access to content outside a defined (subscription or rental) window.

Why is it Important to Protect Digital Content?

Because of the open architecture of the internet, most files by default can be downloaded and shared freely. But marketable digital assets — those designed to be sold or licensed to businesses and consumers — cannot remain marketable under these conditions. And businesses creating these assets can’t remain solvent without a way to limit transmission by requiring purchase or subscription for access to this content.

The Benefits of Digital Rights Management

For rights holders and others on the income-earning side of digital content, digital rights management delivers numerous benefits. In some cases, it singlehandedly enables profitability and allows for the continued viability of digital business models.

1. Protection of Intellectual Property

First, digital rights management protects intellectual property. In an online-first (or even online-only) world, many businesses profit solely or primarily from digital goods. The most obvious example is TV and film content.

While you can still buy movies on disc, the numbers show that most people don’t anymore. From 2011 to 2021, the total number of annual physical video transactions dropped nearly 5 billion, from 6.1 to 1.2 billion.

Consumers now pay for a digital copy of that video content, or they use streaming services they’re already paying for.

Without digital rights management, those downloaded files could be transferred, stored, given away, resold— anything you can do with pictures and video, you could do with the latest movie or Netflix series.

For businesses that rely on sales of or subscriptions to their digital content, DRM is often a necessary component of protecting that intellectual property and the profits the IP generates.

2. Prevents Unauthorized use of Content

Even in today’s market where businesses have access to DRM systems, an estimated 20% of potential revenue on video content is still lost to piracy.

DRM in most cases prevents users from getting their hands on a usable, transferable high-fidelity copy of whatever file or digital asset is being protected. And by preventing users from stealing this content, DRM reduces the possibility of unauthorized uses of that content.

3. Safeguards Income Streams

Additionally, digital rights management helps to safeguard income streams. When users cannot gain free, illegal access, they are left with a choice, to either gain legal access (which typically involves payment) or to go without.

Preventing piracy through DRM ensures more people will pay for a digital asset when they have no other easy option to access it.

4. Educates Users About Copyright and Intellectual Property

Part of the problem with online piracy is that users don’t always understand how copyright and intellectual property laws work.

Many who pirate content have a cursory understanding that what they’re doing is vaguely unethical and maybe illegal. When users run into DRM limitations, those guardrails can help to further understand what’s okay and what’s not.

5. Ensures Regulatory Compliance

Digital rights management is a valuable tool in ensuring regulatory compliance. This is a part of why users in the U.K. and the U.S. have different experiences of Netflix (and virtually every other streaming service with a multinational presence).

Copyright law and content licensing are not universal. Different countries and regions have different regulations and laws, which content distributors must follow if they are to operate in a given country. Additionally, a copyright holder could license content to one group in North America but another in the EU.

Let’s imagine a TV show produced in Australia. Let’s say a local distributor has rights to an Aussie-produced show, but Netflix purchases the rights to distribute in the U.S. Netflix operates in Australia — but it cannot show this TV show there. DRM (specifically, geolocation) is one tool Netflix uses to limit content and ensure compliance.

But DRM isn’t limited to entertainment.

Healthcare records are protected, surrounded by a litany of local, national, and global regulations. DRM can be used to lock down electronic health records and ensure compliance with the relevant regulations.

6. Enables Content Localization and Enhanced Analytics

The other less visible side of geolocation within DRM is how it benefits both customers and content providers. When distributors localize content to match a user’s location, viewers usually experience a more native-feeling and relevant experience, often coming from the right language, catalog and context. This localization helps promote higher engagement rates, stronger conversion, and fewer support and compliance issues. 

In some cases this looks like a different default language, but it could cover numerous other elements, like branding, required display information (such as content ratings), and default currency.

7. Improved Data Security

Traditional data security is entirely credentials based. If a user can establish their credentials (via one or more factors of authentication), they can access the data. If a user cannot establish credentials, they cannot gain access.

This system works better when organizations use two-factor authentication (2FA) or multifactor authentication (MFA), but it focuses on only one part of the problem with data security, access.

Data security has at least two facets, though: access and control. Credentials grant access, but what if you want finer control on what users do with that access?

Digital rights management has a role to play here. Instead of leaving files wide open for anyone with credential access to use as they please, DRM protection could limit what those with access can do with the materials they can access.

For example, imagine a sensitive company document or one with confidential information. You may need to control access, allowing various users to have differing kinds of access, with some in each of these categories:

  • Full access (can download the file)
  • Edit access (can make changes to the file but cannot download)
  • View access (can see and use the file but cannot download or make changes)

This is one relatively simple example, and DRM technologies can go even deeper and more granular as needed.

How IP Geolocation Strengthens Digital Rights Management

Digital rights management doesn’t operate in a vacuum. For global platforms, enforcing content rights depends heavily on understanding where users are actually accessing content from.

IP geolocation enables DRM systems to make real-time decisions about access, availability, and compliance without requiring invasive tracking or complex integrations.

1. Enforcing regional content rights without SDKs

One of the most common challenges in DRM is enforcing region-specific licensing agreements across devices and platforms.

NetAcuity’s IP geolocation solves this by allowing organizations to:

  • Determine a user’s location based on their IP address in real time
  • Apply access rules at the server level instead of relying on app-based SDKs
  • Maintain consistent enforcement across web, mobile, and connected TV environments

This approach reduces implementation complexity while improving coverage and accuracy across platforms.

2. Blocking geo-evasion via VPNs (without hurting real users)

VPN and proxy usage remains one of the biggest threats to DRM enforcement. Users often attempt to bypass geographic restrictions by masking their true location.

Nodify addresses this by:

  • Detecting known VPN, proxy, and anonymizer networks
  • Identifying suspicious traffic patterns without blocking legitimate users
  • Applying adaptive enforcement rules (e.g., step-up authentication vs. outright denial)

The goal is not just to block access—but to do so in a way that minimizes friction for legitimate viewers while reducing abuse.

3. Matching policy enforcement to real-world user presence

For licensing, compliance, and even event-based access, timing matters just as much as location.

IP intelligence can help organizations:

  • Validate that a user is physically present in an approved region during access
  • Align content availability windows with regional rights agreements
  • Support auditing and reporting for compliance verification

This is especially important for live events, time-sensitive releases, and region-specific distribution rights.

4. Enabling precise, location-based content localization

Beyond restriction, IP geolocation grants a level of localization with operational precision that country-only targeting cannot offer. 

With accurate location data, organizations can:

  • Deliver region- or city-specific content catalogs, instead of just country-wide defaults
  • Automatically apply relevant subtitles or language defaults, depending on the specific market of the viewer
  • Adjust pricing, promotions, or messaging, down to the viewer’s postal code or metro area. 

This kind of precision means that teams can move beyond broad regional assumptions, and have confidence in a much more precise localization, while still enforcing DRM policies behind the scenes.

Who Benefits from Digital Rights Management?

Digital rights management can be used in numerous contexts and for many purposes, so the list of groups and industries that can benefit from DRM is long and diverse.

  • Authors, composers, content creators: Anyone creating original works of intellectual property can benefit from DRM as they seek to monetize their creative works. (Implementing DRM software on the solo or solopreneur level can be logistically burdensome, though some DRM protections may be available through popular distribution networks.)
  • OTT and VOD digital media providers (streaming services): Preventing downloads and ensuring the right people access the right content is core to the business model.
  • Video games, mobile apps, and software applications: Limiting use to paying customers is vital to commercial viability in some software and video gaming contexts.
  • Businesses dealing with confidential documents, trade secrets, and otherwise proprietary or sensitive data
  • Digital music distributors and streaming services: Artists and copyright holders require payment for sales, downloads, and streams; DRM systems help contain music so that more of these streams are counted.

Of course, this is just a sample list; the real-world use cases are more numerous and more varied than those listed here.

Best Practices for Effective Digital Rights Management

Implementing DRM effectively is not always simple. You need to do it in a way that meets your company’s needs and obligations without alienating customers through poor user experience or pushing end-users to alternate (including illegal) methods.

As you build a digital rights management strategy for your business, consider these best practices:

  1. Understand your customers, both internal and external: If DRM creates an obnoxious user experience, you’ll create an incentive to bypass it.
  2. Take a nuanced approach: Not every piece of content needs to be protected, and some pieces (free lead generators and advertising-oriented content) should never be.
  3. Realize the downsides: Especially on the consumer level, DRM on fully purchased content (rather than rented or subscription-based) can create a negative experience for users and could leave them without long-term access to something they believe they own.
  4. Prioritize scalability: Put yourself in a position where your company and its DRM solutions can grow.

Protect your intellectual property with Digital Element

Digital rights management is a powerful way to protect assets, reclaim lost sales, demonstrate compliance, and keep sensitive documents secure. Core to many DRM applications is a clear understanding of where users reside geographically.

Digital Element provides superior IP geolocation data through NetAcuity and Proxy/VPN detection capabilities through Nodify that power digital rights management on a global scale. From enforcing regional licensing agreements to detecting VPN-based geo-evasion, high-quality IP intelligence plays a critical role in modern DRM strategies.

Ready to explore Digital Element’s trusted IP solutions for powerful digital rights management? Get started today with a pricing request.

Frequently Asked Questions

Does DRM only apply to streaming and entertainment content?

While DRM is most commonly associated with movies, TV, music and entertainment, it is used across many different industries. Businesses often apply DRM to protect their confidential assets like documents, trade secrets, software, and anywhere control over access, copying or distribution is needed. 

How does IP geolocation improve DRM enforcement compared to app-based methods?

Traditional DRM depends on SDKs built into individual apps to uphold regional restrictions. This adds development overhead and can result in inconsistent enforcement between platforms. IP geolocation, such as the technology NetAcuity offers, enforces access rules at the server level.  This kind of benefit means enabling consistent decisions across web, mobile, and connected TV without making things more complicated. 

Can VPN users bypass DRM-based regional restrictions?

VPN users sometimes try to hide their location to gain access to content not available in their region, but proxy and VPN detection tools like Nodify can spot this traffic in real time. This lets organizations react with adaptive methods, like a new authentication process, without impacting legitimate users. 

Does implementing DRM hurt the user experience?

A poorly implemented DRM can result in a negative user experience. If a DRM is overly restrictive on content that was obtained honestly, users who expect long-term access can become frustrated. The best DRM strategies take a focused approach to protect revenue-critical assets while minimizing friction for legitimate users.

Why Automation Without Context Is Costing You More Than You Think

Posted on by Digital Element

Automated fraud prevention is not a debatable strategy. At the scale modern businesses operate, relying on human review alone simply doesn’t hold up. The question is not whether to automate. The question is: what is that automation costing you when the intelligence feeding it is incomplete?

Every false positive is a tax your security infrastructure levies on your own customers. Most organizations have never calculated what that tax actually is.

Here are three places it shows up, and why none of them appear in a fraud report.

1. Customer Friction: The Silent Churn Driver

An unnecessary challenge, a declined payment, a locked account. These events share a common outcome: the customer doesn’t complain. They leave.

When an automated system encounters an ambiguous signal — a shared IP address, a VPN or  residential proxy connection it can’t confidently classify — it defaults to caution. That default feels safe. But caution has a price. Internationally, false declines cost retailers an estimated $443 billion per year, roughly nine times more than actual fraud losses. And 41% of consumers globally say they’ll never shop with a brand after a false decline. 

That is not a fraud metric. That is a customer retention metric. It belongs on the revenue dashboard, not the security incident report.

The problem compounds with scale. A friction rate that looks acceptable as a percentage is a significant churn driver when multiplied across monthly active users. The customer who doesn’t return doesn’t file a complaint, and the complaint that gets traced back to the security layer. The attribution gap is real, and it keeps the cost invisible.

2. Analyst Burnout: The Cost of Low-Value Triage

Security talent is the most constrained resource in enterprise operations. The median salary for an information security analyst is now over $124,000 (BLS 2024). Nearly half of all companies take more than six months to fill a cybersecurity vacancy. And a survey of over 1,000 IT and security professionals found that 79% have seriously considered leaving due to job stress, with tool sprawl and manual workflows as root causes of burnout.

What burns analysts out faster than anything else is not sophisticated threat response. It’s inconclusive automated decisions routed to manual review — low-signal flags that land in a queue because the system couldn’t resolve the ambiguity. At enterprise scale, manual review teams handle 1,000 to 5,000 orders per day. That’s not security work. That’s triage. And it consumes the same people your organization can barely hire and struggles to retain.

Better upstream intelligence resolves ambiguity before it reaches the queue. That’s not an improvement in security operations. It’s a talent retention strategy with a measurable dollar value attached.

3. Delayed Launches: The Six-Week Negotiation

This is the cost most senior executives recognize immediately, and that almost no analysis of fraud prevention addresses.

A new market, a new payment method, a new product feature. The business case is ready. The engineering work is done. And then begins the negotiation between product and fraud teams over risk thresholds — because the detection model doesn’t have enough confidence to greenlight the launch, and the fraud team can’t accept the downside risk of approving it with insufficient data.

The root cause is not risk aversion. It’s a wide confidence interval. When the intelligence layer can’t reliably distinguish legitimate traffic from ambiguous traffic, every new launch scenario is a guess. And fraud teams, appropriately, don’t approve guesses.

Infrastructure-level intelligence narrows that interval. When a system can characterize not just that a proxy is present but what that proxy represents — a corporate VPN, a residential connection, a rotating attack infrastructure — decisions become defensible. Launches move faster. 

Why Automation Makes It Worse

Modern traffic is genuinely ambiguous. Enterprise users route through shared gateways. Privacy-conscious consumers use VPNs. Residential proxy networks (infrastructure favored by fraud rings) blend into legitimate consumer ISP traffic in ways that surface-level signals can’t resolve.

Automation doesn’t reduce this challenge. It amplifies it. A system making flawed decisions at 50,000 transactions per hour produces errors at a rate no human team catches in real time. The automation isn’t the problem. The incomplete intelligence feeding it is.

What richer context provides is not more data. It’s interpretive clarity. IP intelligence that evaluates stability, device density, behavioral persistence, and proxy architecture type can distinguish a corporate VPN user from a rotating residential proxy attack, even when both appear to originate from the same metro area. That distinction changes the decision. And at scale, it changes the revenue line.

The Organizational Question Worth Asking

In most enterprises, fraud teams are measured on fraud loss prevented. They are not measured on approval rates, customer friction, or launch velocity. The team generating false positives is not the team being measured on the consequences.

That is not a people problem. It is a structural one. And it keeps this cost invisible at the leadership level until it shows up in the revenue numbers.

Accuracy is not only a security metric. It is a business efficiency metric. It belongs in the same conversation as conversion rates, customer retention, and time-to-market.

The question worth putting to leadership: What is our fraud infrastructure costing our customers, and is that a price we’ve consciously chosen to pay?

When organizations answer that question with the right intelligence layer — one that provides contextual depth on VPNs and proxies, behavioral signals on IP address activity over time, and the infrastructure context to distinguish risk from ambiguity — the business outcomes follow: better approval rates, less analyst triage, faster launches, and less friction for customers who haven’t done anything wrong.

Those are business outcomes. Own them accordingly.

Related reading from Digital Element:

Decision Friction: The Hidden Cost of False Positives

Posted on by Digital Element

Every fraud team has a version of this story: a spike in suspicious traffic, a model threshold nudged down, and a flurry of declines. The dashboard goes green. Leadership nods approvingly. The fraud numbers look clean.

What doesn’t show up in that report is what happened next: 

  • The marketing VP for a regional software company, logging in from her company VPN, is locked out of her account during a vendor renewal. 
  • The small business owner, completing a payment at checkout, declined with no explanation and decided to buy from a competitor instead. 
  • The enterprise customer who didn’t complain — instead, they just left.

Call this decision friction: the compounding cost of false positives on the customer experience and the revenue line. 

Unlike fraud losses, it doesn’t surface in incident reports. It shows up in conversion data, churn metrics, and support queues. It’s attributed to a dozen causes but rarely traced back to the security layer that caused it.

The instinct when false-positive rates climb is to recalibrate the model. 

The real problem is what the model is working with.

The Environment Has Changed. The Detection Stack Hasn’t.

Three structural dynamics are compressing the signal quality that automated systems depend on. And making the same traffic look very different from what it did five years ago.

  • Shared infrastructure is now the norm. Enterprise networks often route tens of thousands of employees through a small pool of public IP addresses. Carrier-grade NAT (CGNAT) extends this model to ISP’s where large subscriber bases are multiplexed over limited IPv4 or IPv6 space. As a result, a single IP address may correspond to one user at a given moment or represent tens of thousands of distinct users over time. Without additional context, a detection system cannot reliably distinguish between these cases. 
  • Residential proxy networks have evolved into sophisticated fraud infrastructure. Unlike datacenter proxies, residential IPs appear legitimate because they originate from real consumer devices connected through bona fide ISP subscriptions. As Google’s disruption of the IPIDEA network illustrated, these networks can reach massive scale, often by enrolling consumer devices without their owners’ meaningful awareness. The result is a fraud infrastructure that, at the IP level, looks indistinguishable from legitimate household traffic.
  • Detection stacks are still treating the presence of a proxy as a verdict. A proxy flag is context, not a conclusion. Blanket blocking of VPN or proxy traffic (without understanding what that traffic represents) alienates legitimate users while sophisticated attackers pivot to harder-to-detect infrastructure.

This last point adds a dimension that most fraud teams don’t explicitly discuss: the false positive problem is partly manufactured by adversaries. Sophisticated attackers deliberately route through residential proxies and shared infrastructure precisely because of the effect it produces — detection systems either fail to catch the attack, or they catch it by blocking thousands of legitimate users alongside it. The malicious traffic hides inside legitimate-looking signals by design. Blocking it requires collateral damage. That’s not a flaw in the attacker’s approach. It’s the strategy.

That asymmetry is not incidental. It is the strategy.

The Automation Amplification Problem

Here’s the argument that most analyses of false positives miss: in automated systems, a bad signal doesn’t produce just one bad decision. It can produce millions of them, at machine speed, before anyone notices.

Consider this example. An automated decisioning system processing 50,000 transactions per hour (not unusual at enterprise volume) with a 3% false positive rate is not making 1,500 mistakes. It is making 1,500 mistakes per hour, continuously, against customers who have done nothing wrong. That’s 36,000 legitimate users incorrectly blocked every 24 hours. The numbers are illustrative. The dynamic is not. 

The scale changes the nature of the problem. A 3% false-positive rate is not a tuning problem to iterate on. At those volumes, it is a structural failure running in production. And the customers on the receiving end don’t know that. They see a decline, a lockout, or a friction event. And then they decide whether to try again or take their business elsewhere. 

What would have changed that outcome is not a better-calibrated model. It’s a better signal feeding the model in the first place.

The same traffic, read differently. 

Consider two scenarios. 

The first: a corporate employee connects to their enterprise VPN gateway in Chicago, authenticates, and initiates a software purchase. The IP is flagged — high-activity, shared infrastructure, VPN detected. Risk score elevated. The transaction is stepped up or declined.

The second: a fraud ring testing stolen credentials rotates through residential IPs across the same Chicago metro area, each appearing to originate from a different household. The IPs are clean. No proxy checks in place. Risk score remains within normal thresholds. Transactions proceed.

The detection layer’s failure in both cases is the same: it read the surface signal without reading the infrastructure context.

This is how account takeover campaigns operate in practice. Attackers using residential proxy networks don’t look like attackers at the IP level — they look like normal residential traffic, distributed across geographies, with no obvious clustering. The signal that distinguishes them from legitimate users isn’t the IP itself. It’s the behavioral and infrastructure context underneath it: persistence patterns, IP stability, device density, and the range of locations tied to a single session sequence.

Without that context, the detection layer is left making a surface-level call. The attacker’s session and the legitimate user’s next login can look nearly identical. One proceeds. One gets stepped up or blocked. The wrong one, often enough to matter.

The Business Impact Is Hiding in Plain Sight

Industry data on false-positive costs are striking and largely absent from conversations in security operations centers.

Four cost categories compound that headline number:

Customer friction. Legitimate users locked out, stepped up, or declined. They don’t file support tickets at any meaningful rate. They leave.

Conversion drag. Every friction event at checkout introduces abandonment risk. The transaction cost is immediate and visible. The relationship cost (the customer who decides not to come back) takes months to appear in retention data and is rarely attributed to the fraud layer.

Analyst load. Inconclusive automated decisions get routed to human review teams. At enterprise volume, manual review teams handle 1,000–5,000 orders per day. That is security talent doing low-value triage instead of higher-order threat analysis.

The attribution gap. The downstream revenue loss from false positives rarely surfaces in fraud reporting. When a customer doesn’t return, that churn registers in retention dashboards or product analytics — not in fraud operations. No one connects the revenue leak to the detection decision that caused it, which means no one is accountable for fixing it.

Taken together, this is the cumulative tax the security infrastructure levies on the organization’s customers, largely without anyone’s knowledge and with no one formally responsible for stopping it.

The Organizational Accountability Gap

In most enterprises, fraud teams are measured on fraud loss: detected incidents, chargeback rates, and dollar amounts prevented. They are not measured on approval rates, conversion rates, or customer friction caused.

The organizations that experience the cost of false positives are not the organizations that control the detection signals. The team generating the friction is not the team being measured on it. This is not a competence problem. It is a structural one.

The result is that the false positive problem remains invisible at the leadership level until it’s large enough to show up in revenue figures. 

At which point the conversation can stray away from analytical. Fraud and growth teams fighting over approval rate thresholds is a symptom of this misalignment, not the cause. The cause is that no one formally owns the friction cost, and there is no organizational incentive to reduce it.

The Feedback Loop Problem

There is a longer-term consequence that technical executives will recognize, and most business-level analysis ignores: fraud models that run on imprecise signals don’t just produce false positives. They degrade over time.

When legitimate users are incorrectly blocked or escalated, they don’t always retry through the same channel. They call support. They use a different device. They abandon and don’t return. This means the model never receives a corrected signal on what a good outcome looked like for that session. The feedback loop that should improve detection accuracy over time is broken at the source.

Meanwhile, the fraud patterns the model was trained on evolve. When disrupted, attacker infrastructure doesn’t just disappear — it mutates, reappearing through new IP addresses, devices, and networks. The FBI’s takedown of the Volt Typhoon botnet illustrated this directly: the network rebuilt itself after the disruption rather than dissolving. 

Legitimate traffic patterns shift. Without infrastructure-level context to anchor the signal, the model becomes progressively less able to distinguish good traffic from bad — not because the attackers got smarter, but because the training signal was compromised from the beginning.

This is a well-understood failure mode in automated fraud detection — and it applies whether the decisioning layer is model-driven, rules-based, or a hybrid of both. Its absence from most business-level discussions of false positives is a gap that any technical executive will notice.

Reducing Friction Without Reducing Protection

The answer is not less automation. It is better if inputs are fed into that automation.

The distinction between surface IP address signals and infrastructure-level intelligence matters here. Basic signals, such as location, a proxy flag, and a generic risk score,  tell the detection layer what an IP address is. Infrastructure signals, like  IP stability, device density, behavioral persistence, proxy architecture type, and provider intent signals, tell it what the traffic represents.

That distinction produces different decisions. Infrastructure context enables confident approvals on ambiguous-but-legitimate traffic: the corporate VPN user, the privacy-conscious consumer, the remote worker on a shared gateway. It also enables targeted, proportionate scrutiny on activity that actually warrants it: the credential-stuffing ring cycling through residential proxies, the account takeover campaign hiding behind clean-looking consumer IPs, and the bot network rotating identities at scale.

Digital Element’s approach to this problem is built around a specific data set: IP Characteristics (IPC), which maps the infrastructure context around an IP address rather than treating the address itself as the signal. Instead of asking ‘where is this IP?’ it asks ‘what does this IP’s behavior tell us about the traffic behind it?’ That produces four measurable dimensions:

  • Activity (device density per IP)
  • Location (geolocation consistency)
  • Range (distance between observed locations over time)
  • Persistence (how long an IP remains tied to a location) 

Together, these dimensions can distinguish the Chicago corporate VPN from the residential proxy attack, even when both originate from the same metro area.

The business outcome of better inputs is not just fewer bad decisions. It has fewer manual reviews, lower analyst load, and a fraud layer that stops levying an invisible tax on the customers it is supposed to help protect. 

The Takeaway

The false positive problem is a data problem. As attacker infrastructure grows indistinguishable from legitimate consumer traffic, and automated systems scale decisions to machine speed, organizations running on basic IP signals are not just accepting higher false positive rates. They are systematically transferring revenue from their own customers to their competitors, one friction event at a time, at volumes that don’t appear in any incident report.

The path forward is not recalibrating the model. It is re-examining what the model is working with.

The most effective fraud defenses don’t just detect risk. They understand it.

Related reading from Digital Element:

Moving Beyond Postal Codes: A More Precise Approach to Location with NetAcuity

Posted on by Digital Element

Postal codes were never designed for modern targeting.

They’re broad, inconsistent, and often don’t reflect how people actually live. Originally built for mail delivery, not data-driven decisioning, postal codes can group together populations that behave very differently, creating gaps between location data and reality.

As use cases become more precise, that gap becomes harder to ignore.

The Need for More Precise Geographic Units

As use cases become more precise, the limitations of postal codes become more apparent.

They were never designed to support modern targeting, measurement, or analytics. Their size, inconsistency, and lack of standardization introduce gaps between location data and how people are actually distributed in the real world.

To close that gap, a more precise geographic foundation is required.

Many countries have developed standardized statistical units designed specifically to represent real-world populations and boundaries. These units offer smaller, more consistent geographic definitions that better reflect how people live.

Unlike postal codes, these units are purpose-built for analysis. They provide a clearer, more reliable way to understand location, making them better suited for modern targeting, measurement, and data-driven decisioning.

A Closer Look at Alternate Area Frameworks

While the concept is consistent globally, many regions have  developed their own framework for defining these smaller geographic units.

Australia: SA1 (Statistical Area Level 1)

Defined by the Australian Bureau of Statistics, SA1s are the smallest unit used for census data collection. They are designed to be relatively uniform in population, typically containing between 200 and 800 people, with an average of around 400.

This structure creates highly granular, evenly distributed areas that enable precise analysis while maintaining privacy.

In the image above, the red lines show the postcodes in Melbourne, Australia and the white lines are SA1s. 

France: IRIS (Ilots Regroupés pour l’Information Statistique)

Developed by Institut National de la Statistique et des Études Économiques (INSEE), IRIS zones represent coherent neighborhoods within cities and towns. 

Each IRIS typically contains between 1,800 and 5,000 residents and is structured to reflect meaningful demographic groupings. Compared to postal codes, IRIS provides a more consistent and standardized way to analyze population distribution at a local level.

Germany: PLZ8

PLZ8 extends traditional postal codes into a more granular, eight-digit format, breaking larger postcode areas into smaller, more precise segments.

This added level of detail allows for improved geographic resolution in a market where standard postal codes can vary significantly in size and density.

Why Granularity Matters

When location data is too broad or inconsistent, it introduces noise into everything built on top of it.

  • Targeting becomes less precise
  • Measurement becomes harder to trust
  • Insights become less actionable

More data doesn’t solve that problem.

Better inputs do.

Smaller, standardized geographic units provide a stronger foundation—aligning data more closely with how people are actually distributed and enabling more accurate downstream outcomes.

NetAcuity’s Alternate Area Database

That’s exactly why Digital Element developed the Alternate Area Database (AADB).

AADB maps IP data to more granular geographic units like SA1, IRIS, and PLZ8, enabling organizations to move beyond postal code-level approximation and toward true geographic precision.

By aligning IP addresses to these standardized areas, organizations can:

  • Improve targeting accuracy with more relevant geographic inputs
  • Strengthen measurement by reducing inconsistencies
  • Gain deeper insight into audience distribution
  • Maintain privacy alignment through aggregated, non-identifiable location data

From Approximation to Precision

As location-based strategies continue to evolve, the quality of foundational data becomes increasingly important.

Postal codes served their purpose, but they were never designed for the demands of today’s ecosystem.

By mapping IP data to smaller, standardized geographic units, it becomes possible to move beyond approximation and toward true precision.

Because better inputs lead to better outcomes.

For more detail on how Alternate Area Database works and where it applies, reach out to support@digitalenvoy.com

Your Campaign Is Running. Your Audience Already Moved.

Posted on by Digital Element

There’s a version of digital advertising where everything looks fine. The campaign launched on time. Impressions are serving. The dashboard shows delivery in the right regions. And yet, somewhere between the brief and the final report, performance quietly fell apart.

IP volatility is one of the most underreported causes of that gap, and it’s costing advertisers more than most realize.

The Problem With the Signal Everyone Relies On

The IP address has been the default location signal in digital advertising for decades. It’s what connects a household to a geography, anchors an audience segment, and ties an impression to a target market. The assumption baked into most campaign planning is that the IP address representing a given location today will still represent that location when the ad serves tomorrow, or next week, or at the end of a 30-day flight.

That assumption is wrong.

According to Digital Element’s IPC (IP Characteristics) database, over 40% of IP addresses are reallocated to new locations within a typical 30-day period. Network providers regularly reassign IP blocks to meet shifting infrastructure demands, and when that happens, the household your campaign was targeting is no longer where your data says it is.

The Problem Gets Worse the Longer You Run

IP volatility isn’t a static risk. It compounds over the life of a campaign.

At the household level, Digital Element’s data shows 24.75% volatility at two weeks. By four weeks, that figure climbs to 42.57%. By eight weeks, nearly 60% of household-level IP addresses have moved. What starts as a precision-targeted campaign gradually drifts into something far messier, and because the campaign continues to serve impressions and report delivery, the problem is rarely visible until it’s too late to fix.

The longer the campaign runs, the greater the gap between the audience you defined at the start and the one actually being reached.

What That Looks Like in Practice

Consider a local CTV campaign for a car dealership group, targeting audiences across four specific postcodes over 30 days with a frequency cap of three ads per day per IP. On paper, a clean, well-structured buy.

By the end of the campaign, Digital Element’s analysis found that of 2.65 million total impressions served, only 1.7 million (64%) were delivered within the intended target postcodes. The remaining 960,000 impressions, representing 36% of total spend, went out of market entirely. Spend that began the campaign flowing into the right geographies was, by week three, crossing over to audiences outside the target area entirely.

The campaign reported delivery. What it didn’t report was how much of that delivery was to the wrong people, in the wrong places.

The Real Cost Is Invisible

Wasted impressions are the obvious casualty. But the downstream effects go further. When IP addresses shift mid-campaign, measurement breaks down alongside targeting. Attribution data becomes unreliable because the location signal used to define the audience at the start is no longer the one present at the point of conversion. Budget clawbacks follow. Reporting becomes difficult to defend. And confidence in the channel, and the data underlying it, erodes.

This isn’t a problem specific to one campaign type, one market, or one buying platform. It’s structural. IP was designed for network routing, not audience stability. Expecting it to hold a geotargeted campaign together for 30, 60, or 90 days is asking it to do something it was never built for.

The Fix Isn’t More IP Data. It’s a Different Foundation.

Optimizing against an unstable signal only goes so far. The real solution is anchoring campaigns to a signal that doesn’t move.

LocID is a persistent, privacy-compliant geospatial identifier that represents a fixed physical location — a building, a household, a place in the real world — rather than the IP address currently associated with it. Because LocID is tied to place rather than network infrastructure, it remains stable even as IP addresses underneath it shift. Targeting is set at campaign launch. Measurement aligns to the same identifier throughout. The audience doesn’t drift because the reference point doesn’t move.

LocID integrates across the supply chain, compatible with DSPs, SSPs, and measurement platforms via OpenRTB, so it doesn’t require rebuilding existing workflows. It’s designed to complement existing ID graphs and ensure audience alignment holds at every stage of the campaign lifecycle, from segment creation through to post-campaign reporting.

Location Should Be a Strength, Not a Liability

Geotargeting is one of the most powerful tools in an advertiser’s toolkit. Local campaigns, regional strategies, household-level reach — these are high-value capabilities when the location signal underneath them is reliable.

Right now, for most advertisers, it isn’t.

The 40% reallocation rate isn’t an edge case or a technical footnote. It’s a structural problem with the signal the industry has treated as stable for years. Advertisers who recognize it and build their campaigns on a foundation that accounts for it will see the difference in targeting accuracy, measurement confidence, and ultimately, in results.

Digital Envoy Appoints Steve Broadhead as VP Sales International

Posted on by Digital Element

Digital Envoy, the global leader in IP intelligence and geolocation, is pleased to announce the appointment of Steve Broadhead as VP Sales International. As demand for deterministic IP intelligence accelerates across Europe and global markets, the appointment marks a deliberate investment in Digital Envoy’s international growth strategy.

Steve assumes the role from Charlie Johnson, who over the past decade has been instrumental in building Digital Envoy’s international business into the formidable operation it is today. Charlie moves on to a new chapter within the company as SVP of the LocID line of business, where she will lead one of Digital Envoy’s most strategically important growth areas.

Drawing on over 20 years of international commercial leadership in ad tech and media technology, Steve joins Digital Envoy to drive new business, expand strategic partnerships, and grow the company’s presence across Europe, EMEA, LATAM, and APAC. His experience leading sales organizations across EMEA — including senior roles at Video Intelligence, Unruly, and Nexxen — gives him a strong foundation to build on the global customer base Charlie has established.

Jerrod Stoller, CEO of Digital Envoy, commented:

“The international opportunity for IP intelligence has never been stronger — from programmatic advertising and content rights enforcement to fraud prevention and cybersecurity, global enterprises are increasingly relying on the kind of deterministic, privacy-forward data that Digital Envoy has delivered for over 25 years. Charlie has done an exceptional job building our international presence, and we’re excited to see her bring that same energy to LocID. Steve’s deep experience building commercial teams across EMEA and beyond makes him exactly the right person to take that foundation and accelerate our global growth even further. We’re thrilled to have him on board.”

On his appointment, Steve said:

“I’m genuinely thrilled to be joining Digital Envoy at such a pivotal moment. IP geolocation and intelligence sits at the heart of so many of today’s most critical business challenges — whether that’s delivering precision-targeted advertising, enforcing digital content rights, or protecting businesses from fraud, cybercrime and risk. The fact that Digital Envoy has been the trusted foundation for all of these use cases for over 25 years, across some of the world’s largest brands, platforms and security organisations, speaks for itself. The international opportunity is enormous and I can’t wait to get started.”

Steve is based in London and takes up the role with immediate effect.

Digital Advertising Taxes Are Expanding — Here’s Why Location Accuracy Now Matters More Than Ever

Posted on by Digital Element

For years, digital advertising has lived in a gray area of state tax policy. Ads are created in one place, bought in another, served everywhere — and taxed almost nowhere.

That’s changing.

Washington State recently expanded its retail sales tax to include many digital advertising services, joining a growing group of states reconsidering how digital ads fit into existing tax frameworks. While Washington’s approach differs from Maryland’s standalone digital advertising tax, the signal is clear: states are moving to tax digital advertising based on where it is delivered, not just where it’s sold.

As more states explore similar laws, advertisers, agencies, and ad platforms face a new challenge: accurately determining where ads are actually served — at scale.

The Emerging Patchwork of Digital Advertising Taxes

Washington isn’t alone. Legislators in states like New York, Massachusetts, Rhode Island, Connecticut, and Minnesota have introduced or debated proposals aimed at taxing digital advertising or related digital services.

While the details vary, these proposals share common traits:

  • Taxes triggered by where ads are delivered or consumed
  • Increased scrutiny on digital services historically treated as non-taxable
  • A reliance on location-based sourcing rules to determine tax liability

This shift creates a fundamental operational problem for digital advertising: How do you prove where an ad was actually served?

Why “Location” Is Now a Tax Problem, Not Just a Marketing One

Digital advertising has traditionally optimized for performance metrics — impressions, clicks, conversions. Tax authorities care about something different: Jurisdictional accuracy.

For tax purposes, states increasingly want to know:

  •  Which ads were delivered to users in their state  
  •  Whether ads crossed county, city, or local tax boundaries  
  •  How much taxable activity occurred inside vs. outside their jurisdiction

Without precise location intelligence, companies risk:

  •  Over-collecting tax, inflating customer costs  
  •  Under-collecting tax, creating audit exposure  
  •  Inconsistent reporting across finance, legal, and ad operations teams

This is where IP intelligence moves from “nice to have” to critical infrastructure.

Why ZIP+4–Level IP Intelligence Is Essential

Many tax rules — especially sales and use taxes — are applied at the local jurisdiction level, not just the state level. Broad geolocation (country or state only) isn’t enough.

To correctly calculate and allocate digital ad taxes, organizations need:

  •  Accurate user location at the time an ad is served  
  •  Consistent, auditable location data  
  •  Coverage that scales across billions of ad impressions

This is where ZIP+4 granularity becomes especially valuable. ZIP code alone can still mask important local tax differences, while ZIP+4–level precision can help organizations better align ad delivery with real-world jurisdictional boundaries.

IP intelligence provides the only practical way to do this without relying on personal data or cookies.

How NetAcuity Supports Tax Accuracy for Digital Advertising

NetAcuity’s IP intelligence enables advertisers, platforms, and service providers to confidently determine where digital ads are delivered — down to the ZIP+4 level.

With NetAcuity, organizations can:

  • Determine tax jurisdiction at ad-delivery time  
  • Map impressions to precise geographic locations without collecting personal identifiers
  • Support accurate tax calculation and allocation  
  • Attribute ad activity to the correct state, county, city, or local tax authority 
  • Reduce audit and compliance risk  
  • Use consistent, independently validated location data across finance, legal, and operations teams. 
  • Future-proof against expanding regulations  

As more states adopt digital advertising taxes, location accuracy becomes a reusable compliance asset — not a one-off fix.

Critically, NetAcuity enables this without relying on cookies, device IDs, or personal data, aligning with modern privacy and data-minimization requirements.

Preparing for What Comes Next

Whether or not your state has enacted a digital advertising tax yet, the direction of travel is unmistakable. Tax authorities are catching up to the digital economy — and location accuracy is the foundation of enforcement.

The question is no longer if digital ad taxation expands, but how prepared your systems are when it does.

  • Organizations that invest now in ZIP+4–level IP intelligence will be best positioned to:
  • Adapt quickly to new laws  
  • Avoid costly retroactive corrections  
  • Maintain trust with regulators and customers alike

Digital advertising may be borderless — but taxes are not.

Want to explore how NetAcuity supports jurisdiction-level accuracy for digital advertising and compliance use cases?  

Learn more about NetAcuity’s IP intelligence solutions.

FAQs

What are digital advertising taxes?

Digital advertising taxes are state or local taxes applied to revenue from digital ads, often based on where the ads are delivered or viewed, rather than where they are sold or where the advertiser is located.

Which states currently tax or are considering taxing digital advertising?

Maryland currently enforces a standalone digital advertising tax, while Washington State taxes certain digital advertising services under its retail sales tax. Other states — including New York, Massachusetts, Rhode Island, Connecticut, and Minnesota — have introduced or debated similar proposals.

Why does location matter for digital advertising tax compliance?

Location matters because many digital advertising taxes use location-based sourcing rules, meaning tax liability depends on where an ad is delivered to a user, not where the advertiser or platform is based.

How do states determine where a digital ad is delivered?

States typically rely on technical indicators such as IP address data to determine where a digital ad was served at the moment of delivery, allowing tax liability to be assigned to the correct jurisdiction.

Is state-level location accuracy enough for digital ad taxes?

No. Many tax rules apply at the county, city, or local level, meaning state-only location data can result in incorrect tax allocation and increased compliance risk.

Why is ZIP+4–level IP intelligence important for digital advertising taxes?

ZIP+4–level IP intelligence enables organizations to assign digital ad activity more precisely to local tax jurisdictions, supporting more accurate tax calculation, more consistent reporting, and stronger audit readiness.

How can companies determine where a digital ad was served?

Companies determine ad delivery location using IP intelligence, which identifies a user’s geographic location at the time an ad is served, without relying on cookies or personal data.

How does IP intelligence support digital advertising tax compliance?

IP intelligence helps companies map ad impressions to the correct jurisdiction, reduce under- or over-collection of tax, and maintain auditable, consistent location data across finance, legal, and advertising teams.

What risks do companies face if they lack accurate ad location data?

Without accurate ad location data, companies risk tax underpayment, audit exposure, retroactive assessments, and inconsistent regulatory reporting as digital advertising taxes expand.

Is digital advertising taxation expected to expand?

Yes. As states adapt tax laws to the digital economy, more jurisdictions are expected to tax digital advertising, making location accuracy a critical long-term compliance requirement.

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