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Author: Digital Element

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

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.

Why Legitimate ISP IP Addresses Are Mistaken for Proxies

Posted on by Digital Element

Streaming providers occasionally encounter situations where legitimate subscribers are blocked because their IP address appears to be operating as a VPN or proxy. In many cases, neither the subscriber nor the ISP has intentionally enabled any anonymization service, which can make these blocks difficult to explain and even harder to resolve.

What’s often misunderstood is why this happens. These classifications are not based on who owns the IP address or how the ISP’s network is designed. Instead, they are driven by observable traffic patterns. When an IP shows behavior that resembles how modern residential proxy networks operate, it may be flagged—even if the IP itself was issued by an ISP and is being used by a legitimate household.

As the internet has evolved, so has the way proxy networks are built. Today, many residential proxies do not rely on traditional data centers. Instead, they quietly operate inside real households using ISP-assigned IP addresses. This shift has made proxy detection more complex for streaming platforms and more confusing for everyone involved.

The Problem: Why Legitimate ISP IPs Get Blocked

A common entry point is the installation of free VPNs, browser extensions, or mobile apps that rely on peer-to-peer networking. These services often include permissions, disclosed only in the fine print, that allow a user’s internet connection to be reused as part of a broader proxy pool.

Once installed, the device becomes a relay node. Traffic from remote users flows through that household’s connection and exits the internet under the same public IP address assigned by the ISP. From the subscriber’s perspective, nothing appears unusual. From the ISP’s perspective, the network is functioning normally.

From the streaming provider’s perspective, however, the IP now carries mixed traffic patterns originating from multiple locations. This behavior closely resembles a residential proxy service, even though neither the subscriber nor the ISP has knowingly participated in one.

Why These Blocks Occur and Why ISPs Are Often Surprised

Streaming platforms make access decisions based on observed risk signals, not assumptions about intent. When an IP shows signs of relaying traffic, geographic instability, or anonymization behavior, it may be blocked to protect licensed content and reduce abuse.

In shared residential environments, such as apartment buildings or managed broadband networks, a single device acting as a relay can impact everyone behind the same public IP. This is why legitimate viewers may be blocked even when they are not using a VPN themselves.

Importantly, this behavior does not originate from ISP infrastructure. Shared IPs and NAT (Network Address Translation) are standard across residential networks and do not cause proxy classification on their own. The activity triggering these signals occurs inside the subscriber environment, outside the ISP’s visibility or control. This is why ISPs are often blindsided by these situations and why ownership of the IP alone does not explain the behavior being observed.

Adding Time-Based Context to Proxy Detection

One of the core challenges in proxy detection is understanding recency. Many systems rely on simple indicators that show whether an IP was seen acting like a proxy within a fixed window of time. These signals lack the context needed to determine whether the behavior is ongoing or historical.

Nodify addresses this by adding time-aware proxy intelligence. Each observed proxy event is recorded with precise timestamps and frequency data. This allows streaming providers to see when proxy behavior occurred, how often it was observed, and whether it is still active.

With this level of detail, platforms can distinguish between a short-lived relay event and sustained proxy activity. This helps reduce false positives while still enforcing content protection policies effectively.

Using Behavioral Signals to Reduce False Positives

While Nodify answers when proxy behavior occurred, IP Characteristics helps explain how an IP is being used over time.

IPC analyzes behavioral and environmental signals such as device counts, geographic stability, and connection volatility. These signals provide insight into whether an IP behaves like a normal residential connection or shows patterns consistent with proxy or automated traffic.

For streaming providers, this additional context helps validate enforcement decisions. IPC allows teams to understand whether unusual behavior reflects normal residential usage patterns or something more anomalous, enabling more proportional responses that protect content without unnecessarily disrupting legitimate viewers.

A More Accurate Way Forward for Streaming Platforms

An ISP-issued IP address no longer represents a single household’s activity in all cases. In today’s internet, many residential IPs are quietly reused as part of proxy networks without the knowledge of the subscriber or the ISP.

For streaming providers, recognizing this reality is key to reducing confusion and improving resolution times. Combining time-based proxy intelligence with behavioral context allows platforms to separate real risk from unintended side effects, protecting content while minimizing disruption for legitimate subscribers.

Frequently Asked Questions

Why would a legitimate ISP IP address be mistaken for a proxy?

Proxy detection systems evaluate observable traffic behavior, not IP ownership or intent. If an ISP-issued IP shows patterns such as traffic relaying, geographic instability, or mixed usage consistent with modern residential proxy networks, it may be classified as proxy-like—even when the subscriber and ISP have not knowingly enabled any anonymization service.

Are shared IPs or NAT the reason these blocks happen?

No. Shared IPs and Network Address Translation (NAT) are standard features of residential ISP networks and do not cause proxy classification on their own. Proxy-related flags are triggered by activity that occurs inside the subscriber environment, not by normal ISP infrastructure or IP sharing practices.

How can a residential IP start behaving like a proxy without the user realizing it?

Some free VPNs, browser extensions, or mobile apps use peer-to-peer networking models. When installed, these tools may allow a device to act as a relay for third-party traffic. From the user’s perspective, nothing appears unusual, but externally the IP may exhibit traffic patterns that resemble a residential proxy service.

How can streaming platforms reduce false positives without weakening enforcement?

By adding time-based proxy intelligence and behavioral context, platforms can distinguish between short-lived, historical proxy activity and sustained risk. This allows teams to make more proportional enforcement decisions—protecting licensed content while minimizing unnecessary disruption for legitimate subscribers.

CTV Advertising in 2026: Navigating an Era of Abundant Data and Trust Scarcity

Posted on by Digital Element

CTV viewership continues its upward trajectory, growing both in terms of number of viewers and time CTV viewership continues its upward trajectory, growing both in terms of number of viewers and time spent consuming content. 

According to the latest Comscore State of Streaming data, CTV usage continues its rapid ascent — by 2025, 96.4 million U.S. households were streaming content on connected TV devices, with total streaming time reaching 13.9 billion hours. This isn’t surprising given the number of options now available to consumers to watch their favorite shows at any time, day or night, that’s convenient to them, and on any device of their choosing.

Those options include ad-supported video on demand (AVOD), free video on demand (FVOD), free ad supported television (FAST) and virtual multichannel video programming distributor (vMVPD).

It’s a truism in advertising: where consumers go, brands must follow. But are advertisers reaping the full benefit of digital advertising when they buy CTV inventory? The data says no, largely due to a lack of standardized measurement and verification.

A report from Xenoss succinctly highlights the lack of common identifiers, myriad measurement methodologies, and complex device identification as key challenges. Its authors discuss the need for ad platforms to pull data from multiple sources to give a complete picture of ad performance, and the challenges of cross-media measurement and data fragmentation.

Meanwhile, DoubleVerify ​​reports that CTV advertisers confront multiple types of ad fraud, including bot traffic, fake apps and fake traffic, and urgently need sophisticated fraud detection mechanisms to combat ad fraud in the CTV environment.

This leads us to the most notable trend for 2026: CTV advertisers will demand assurance that their ads were delivered to real people, not bots, and were seen by the right consumer or household as promised.

Lack of Measurement Stymies CTV Advertising 

There’s no denying that the TV advertising landscape is in the midst of a radical transformation. Notably, projections indicate that spending for streaming ads will exceed those of traditional linear TV by 2025. Within the next three years it will account for 68% of total TV ad spend.

For its part, spending on linear TV will decline from $61.31 billion in 2023 to $56.83 billion in 2027, according to Insider Intelligence. In that same time period, CTV ad spend will grow from $25.09 billion to $40.9 billion.

So what are the implications of this shift in focus? To begin, as more ad dollars flow into CTV, the more advertisers will be confronted with the challenges of measurement and the scourge of ad fraud. Without trusted and standard tools, how will they feel confident that the impressions they pay for were actually seen by real users?

This contrasts with linear TV, which had a standard mechanism for buying and measuring TV for 50 years: Nielsen’s panels and the gross rating point (GRP).

Say what you will about the efficacy of linear TV, at least it was standardized, and enabled marketers to compare performance across all publishers.

Three Vectors for Linear TV

The linear TV framework offers three vectors for audience audience targeting:

  1. Content: Marketers select shows based on content — sports, soap opera, news, etc., — which serves as a proxy for the viewers’ demographics. This, of course, is based on assumptions, such as “kids watch cartoons” and “adults watch the news.”
  2. Time of day: Day parts help advertisers match products to consumers. For instance, breakfast cereal brands target consumers while they’re likely to be eating breakfast, and fast casual restaurants show their lunch specials at noon time.
  3. Geography: Where a consumer is located helps advertisers hone their targeting further. For instance, a tire company will show regular tires to markets in the south, and ads for snow tires for markets likely to experience winter weather.

Measurement was done through the GRP, which is a metric for gauging the effectiveness of linear television ads. GRP is calculated based on the percentage of the total potential TV viewership that the ad reaches. Specifically, one GRP represents the ad being seen by 1% of the entire potential TV audience.

Then vs. Now

While linear TV relied on content, time of day, and geography as proxies for audience targeting, modern CTV advertisers expect deterministic and real-time signals. They want to understand not just what content was viewed, but where the viewer was located, what device was used, and whether the impression was valid. This shift in expectations highlights the growing gap between legacy TV buying models and digitally driven CTV strategies.

Digital Expectations for a Critical Digital Channel

The linear model doesn’t translate well to CTV for multiple reasons. To begin, day parts are no longer relevant, as anyone can watch any content at any time. And while IP address signals have historically supported location targeting in digital advertising, they are often inconsistent, obfuscated, or missing in programmatic CTV bid streams — making location difficult to ascertain. This issue grows more severe as the surge in programmatic TV continues.

There is also the issue of the ad buyer’s expectations. CTV is a digital channel, and its inventory is traded by people who are steeped in the digital landscape. They are marketers who are accustomed to a plethora of attributes for targeting, measuring and optimizing campaigns. This absence of reliable geographic signals in the programmatic CTV ecosystem has created what many advertisers refer to as a “location gap,” where ads can be served without consistent insight into where or to whom they were actually delivered. Simply put, the location gap occurs when ads are delivered without consistent or dependable insight into where — or to whom — they were actually served.

While some platforms can derive household identity and enhanced geographic signals through authenticated logins and cross-device graphs, these capabilities are not universally available. Across the broader programmatic CTV ecosystem, such signals remain fragmented and inconsistent, allowing the location gap to persist.

As a result, advertisers and their agencies know there are hundreds of data variables they should be able to use for targeting, measurement, and optimization but often cannot, including:

  • IP location address, and all the insight that surrounds it, including locations down to the +4 ZIP code, home vs. business, mobile carrier, device type, among others.
  • Time spent watching TV, when did they stop watching TV. ACR is opt-in and powerful on smart TVs, but coverage is uneven across device types. Data availability varies by provider and location often isn’t consistently available in a usable form.
  • Precise audience segmentation based on demographics and psycho-demographics (e.g. users in these households who love fashion, or east coast moms who shop at Trader Joes). The city, neighborhood and block can contain a great many types of consumers, which is why digital advertisers place a premium on creating unique audience segments.
  • Tracking and attribution. Advertisers are keen to track the outcomes of their ad spend. Of the users who saw their ads, how many visited the website or retail outlet? How many conversions or sales did the ad spend generate?

The State of CTV Measurement

CTV measurement has been a topic of concern for advertisers since the channel’s rapid acceleration during the pandemic years. In response, CTV platforms, verification providers, and measurement companies have continued to introduce new approaches — though challenges around consistency, transparency, and standardization remain.

For digital advertisers, CTV measurement feels opaque because it lacks the transparency, consistency, and interoperability they expect from other digital channels. Performance data is fragmented across platforms, measurement methodologies vary widely, and buyers often have limited visibility into how audiences are defined or impressions are validated. This disconnect makes it difficult to compare CTV performance to channels like display, search, or social.

In some cases, the old way of measuring TV is being shoehorned into CTV. This is an important point, because buyers say they don’t trust such methodologies. That lack of trust, in turn, is putting a damper on ad spend, preventing it from reaching its full potential.

How do we build trust in CTV measurement? We need to resolve the complexities in the current CTV landscape, which include:

  • A lack of common identifiers. CTV measurement lacks common identifiers, making it difficult to track and measure ad performance across different platforms and devices.
  • Data fragmentation. Advertisers have little visibility into where their CTV ad runs and who they reach, due to highly fragmented data. This, in turn, makes it difficult to measure and track campaign KPIs.
  • Inconsistent measurement. Unlike the GRP, CTV is plagued with inconsistent measurement practices. What’s more, advertisers don’t have transparency into how audiences are measured and how outcomes are attributed to ad spend.
  • Opaque practices. Ad buyers often think they’re buying premium CTV inventory when, in fact, it can be less than premium or even fraudulent.

The bottom line is that CTV measurement today faces significant challenges for marketers who are accustomed to digital campaigns. At present, advertisers can’t accurately determine whether the intended audience for an ad is indeed the one who views it. Panels, although valuable, do not paint a complete picture, as precise geolocation is often lacking.

If all panels were to disclose the geographical reach of their data (which is currently not a widespread practice), ad buyers would have a standardized understanding of their viewership, even if different panel measurement companies provide varying insights.

Many ad buyers already rely on multiple measurement companies, and with access to location-based data, they can better comprehend the origins of their viewers and the demographic information provided by the panel company.

Until a standardized approach to television measurement comes to market, advertising will be stymied. That said, given the increasing importance of CTV in reaching and engaging consumers, 2026 will be a year when substantial innovation occurs.

State of Fraud in CTV

Ad fraud in CTV has been a significant concern for advertisers, as nefarious players deploy deceptive tactics, such as bots or fake CTV devices to simulate viewership in order to inflate video ad impressions. In some cases, these sophisticated schemes are the work of organized crime rings.

In 2024, bot fraud made up 65% of all fraud in CTV environments; a share significantly higher than in other digital channels.

The billions of dollars that flow into CTV advertising is irresistible to fraudsters, many of whom have significant technical skills and resources at their disposal to ply their craft. As more dollars move into the CTV space, the greater the opportunity for fraud.

Residential IP proxy networks are another issue of concern for streaming TV providers. Consumers, crime rings and VPNs seeking to circumvent digital rights management (DRM) restrictions are leveraging residential IP proxy networks to circumvent geo-restrictions, a topic we’ve covered in the past. Quality teams need greater visibility into residential IP proxy networks to safeguard premium CTV inventory and support accurate household targeting. These networks can obscure fraudulent activity, undermining advertiser trust and complicating efforts to validate real audiences.. When fraudulent traffic is masked behind residential IP proxy networks, it can contaminate premium CTV inventory, reduce advertiser confidence, and make it harder to distinguish legitimate household viewers from automated or coordinated fraud schemes.

This leads to a defining trend for CTV advertising in 2026.

Solving challenges related to location accuracy, fraud detection, and audience validation requires high-quality IP intelligence that can operate at scale across fragmented CTV environments.

What Comes Next for CTV Advertising

As CTV matures, the conversation is shifting from rapid growth to long-term sustainability. Advertisers are no longer evaluating the channel solely on reach or scale, but on whether it can deliver the same level of accountability they expect from other digital media investments.

The next phase of CTV advertising will be shaped by how effectively the ecosystem addresses structural blind spots — particularly around geographic validation, impression legitimacy, and cross-platform comparability. Solutions that bring greater clarity to these areas will help reduce friction between buyers and sellers and support more confident budget allocation.

Rather than relying on legacy TV constructs, the industry is moving toward data-driven approaches that better reflect how streaming content is consumed and monetized. As this shift continues, trusted data foundations and verification capabilities will play an increasingly important role in establishing consistency and rebuilding confidence across the CTV supply chain.

The Data Backbone Advertisers Can Trust

This evolution is already underway, and the progress made in 2026 will define how CTV is measured, valued, and trusted moving forward. Digital Element helps make that progress possible by delivering the data foundation the CTV ecosystem depends on.

Through consistent geographic validation, household and device context where available, and advanced detection of VPNs, proxies, and residential proxy traffic, our Digital Element products bring greater accuracy and accountability to programmatic CTV. Combined with supply-path transparency and alignment with verification standards, these capabilities enable advertisers to measure performance with confidence, validate delivery, and build trust at scale. 

Contact us to learn more about our range of Digital Element CTV advertising intelligence platforms. 

Now Available: IRIS – Unlocking Granular Geolocation Data in France with NetAcuity

Posted on by Digital Element

For decades, marketers have relied on postal codes as a core component of location-based targeting. While this level of data has delivered meaningful insights and campaign results, today’s competitive digital landscape demands even greater precision. That’s why Digital Element continues to innovate, expanding the boundaries of IP geolocation with the launch of IRIS  – France’s standardized geographic unit – now available through NetAcuity’s Alternate Area Database.

What Is IRIS?

In France, IRIS (Ilots Regroupés pour l’Information Statistique) refers to statistical blocks used by the French National Institute of Statistics and Economic Studies (INSEE) to collect and analyze data. Each IRIS zone contains approximately 2,000 residents and is designed to represent a coherent area from a demographic and urban planning perspective. While postal codes in France can be large and sometimes inconsistent, IRIS zones offer a standardized, fine-tuned way to understand population characteristics and trends at the neighborhood level.

Think of IRIS as France’s equivalent to Australia’s SA1 or Germany’s PLZ8 regions—purpose-built for statistical analysis, yet perfectly suited for marketers seeking smarter geolocation targeting.

More Granularity, Same Commitment to Privacy

By integrating IRIS into the Alternate Area Database, Digital Element enables businesses to map IP addresses to these smaller statistical units. This upgrade delivers sharper geographic resolution than traditional postal codes, unlocking new levels of campaign precision and audience understanding—without sacrificing user privacy.

IRIS areas are small enough to deliver actionable insight but aggregated enough to ensure individual data remains protected. This makes them ideal for privacy-compliant demographic profiling, content localization, and fraud prevention.

Why It Matters for Your Business

  • More relevant targeting: Deliver messages tailored to hyper-local communities
  • Smarter planning: Understand audience distribution and behavior across smaller, more meaningful areas
  • Greater campaign ROI: Boost conversion rates by aligning offers with the real-world context of your audience

Whether you’re managing digital advertising, localizing content, or conducting market analysis, IRIS gives your team a deeper, more accurate picture of the French market.

Now Available in NetAcuity

IRIS is now available through NetAcuity’s Alternate Area Database and can be licensed on its own or alongside any of Digital Element’s 18 other databases for maximum insight.

And this is just the beginning. Following the successful rollout of SA1 in Australia and now IRIS in France, Digital Element will soon be adding PLZ8 regions in Germany to the Alternate Area Database—further expanding global access to hyper-local IP intelligence.

For a deeper dive into the launch of IRIS and what it means for location-based data innovation in France, be sure to check out our official press release. It highlights how IRIS complements our Alternate Area Database strategy and what’s next as we continue expanding across global markets.

Ready to go deeper with your location targeting in France?
Learn more about the power of IRIS and how it can enhance your marketing, analytics, and security strategies by reaching out to support@digitalenvoy.com.

How to Unlock Granular Insights with NetAcuity’s Alternate Area Database

Posted on by Digital Element

Since commerce has gone online, marketers have relied on IP addresses for targeted marketing campaigns. In today’s competitive marketplace, the ability to target marketing efforts with precision is more critical than ever.

Since 1999, Digital Element has been helping marketers target their audiences precisely by postal code globally. This level of detail not only enhances the effectiveness of marketing campaigns but also drives higher conversion rates and better ROI.

While postal codes are granular, advertisers are always eager for even more precise data to gain deeper insights and improve performance of their targeted campaigns, and Digital Element has the answer.

A Deeper Understanding of Your Marketing Campaign

For census and planning purposes, many countries have created standardized geographic areas in addition to postal codes. These alternate areas help governments collect and analyze population data, but they also unlock valuable opportunities for marketers who need more precision than postal codes alone can offer.

In Australia, for example, these areas are known as Statistical Areas Level 1 (SA1), defined by the Australian Bureau of Statistics (ABS). SA1s are designed to be relatively small and consistent in population size—typically between 200 and 800 people, with an average of about 400.

SA1s represent the smallest unit in Australia’s statistical area hierarchy. Other countries use similar systems, such as PLZ8 regions in Germany and IRIS zones in France.

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

Traditional postal codes in Australia can be quite broad, often covering multiple towns or large urban populations. There are approximately 3,333 postal codes nationwide. By comparison, the number of SA1s expands to more than 61,800 distinct areas.

In metro Sydney alone, a single postal code can contain as many as 40 individual SA1s. Each SA1’s smaller footprint enables more accurate targeting and more meaningful insights—without narrowing data down to an individual level.

This balance is critical. SA1s are granular enough to support detailed analysis and localized engagement, yet large enough to preserve anonymity and protect privacy. By focusing on smaller, more defined geographic units, companies can deliver personalized content that resonates with the right audience, at the right time, in the right place.

Why This Level of Granularity Matters in a Cookieless World

As third-party cookies continue to disappear, marketers are relying more heavily on signals that do not depend on personal identifiers. IP-based geolocation has emerged as a durable alternative because it enables geographic relevance without tracking individual behavior.

By working at the level of postal codes, cities, or alternate areas like SA1s, brands can:

  • Localize messaging and offers without cookies
  • Maintain reach and scale across browsers and devices
  • Support privacy-compliant advertising strategies

This makes geographic intelligence especially valuable for campaigns where relevance is tied to where someone is, not who they are.

1. Get More Granularity without Sacrificing Privacy

Digital Element is taking IP-based location targeting to the next level with the introduction of its new Alternate Area Database.

This innovative feature maps IP addresses to individual SA1s in Australia, providing more precise geographic data by aggregating IP addresses from specific areas smaller than  traditional postal codes. By leveraging these detailed geographic boundaries, companies can enhance the granularity and accuracy of location-based data without compromising privacy.

The Alternate Area Database can be licensed as a standalone product or in conjunction with NetAcuity’s 18 other databases for more detailed insights.

2. Supporting More Accurate Targeting, Measurement, and Localization

With access to Alternate Area data, marketers can refine how they use location intelligence across multiple use cases.

For advertising and marketing, this means serving campaigns that are more closely aligned with local context, reducing wasted impressions and improving relevance at the neighborhood or community level.

For content and commerce experiences, alternate area data supports automatic localization. Product catalogs, pricing, language, subtitles, and regulatory messaging can adapt dynamically based on estimated regional location, without requiring logins or persistent tracking.

For analytics and measurement, greater geographic precision improves confidence. When campaigns are evaluated using consistent, deterministic location signals, marketers can better understand performance by region even as traditional cookie-based attribution becomes less reliable.

3. Designed to Complement Verification and Measurement Partners

NetAcuity’s Alternate Area Database is designed to work alongside existing verification, fraud prevention, and measurement solutions, not replace them.

By adding a stable, privacy-safe geographic signal, it helps preserve campaign scale and consistency as cookies fade, while enhancing the overall quality of location-based insights. This makes it a strong foundational layer within modern, privacy-first advertising stacks.

Looking Ahead: Expanding to France and Germany

The introduction of the Alternate Area Database in Australia is just the beginning. NetAcuity plans to extend this feature to other regions, including Germany and France. In Germany, the focus will be on PLZ 8 regions, and in France, the IRIS system will be utilized. These expansions will further enhance the granularity and accuracy of IP geolocation, providing valuable insights and targeting capabilities across Europe.

Revolutionizing IP Geolocation for Smarter Marketing

NetAcuity’s Alternate Area Database represents the next evolution of IP-based geolocation. By mapping IP addresses to smaller, well-defined geographic areas, it empowers businesses to make more informed decisions. Whether it’s for precise ad targeting, in-depth demographic analysis, or enhanced cybersecurity, the ability to map IP addresses to smaller, more accurate regions like SA1s unlocks new potential while maintaining alignment with Digital Element’s privacy-centric approach.

As this capability expands globally, it will continue to open new possibilities for brands that need precision, performance, and compliance in a cookieless digital environment.

Learn More About Our Portfolio of Products

For more info on our capabilities, check out the resource center or reach out to support@digitalenvoy.com.

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