Traditional identity graphs are failing, the foundations are cracked
Marketers have spent years trying to assemble a complete picture of people using what’s called an identity graph. The idea is to take different pieces of data, cookies, device IDs, IP addresses, phone numbers, and connect them to form a single profile. And for a while, that actually worked. Advertisers could personalize at scale and performance marketing got sharper.
But it’s not holding up anymore. Regulatory changes, platform restrictions, and shifting user behaviors are pulling those signals apart. As of early 2025, more than 85% of global web traffic is cookie-restricted. Apple’s App Tracking Transparency has pushed opt-in rates for mobile tracking down to 25%, and Android is moving in the same direction. VPN use is increasing. IP addresses now represent households. Phone numbers are recycled at scale, with a Princeton University study showing that 66% of them still link to past accounts, and 9% continue receiving sensitive information.
These aren’t minor issues. The failure isn’t at the edge, it’s at the foundation. Many of these identifiers were never meant to be permanent. They were built for convenience, not long-term accuracy or trust. This is why you’re seeing ad spend leak into poorly attributed conversions and mistargeted segments. The system assumes data points equal identity. That assumption no longer holds.
If your current strategy is still leaning on this old infrastructure, you’re operating on flawed ground. It’s not a question of optimization. It’s a need for replacement. The cost of doing nothing will grow exponentially as these data sources degrade, and the trust required to reach customers continues to rise.
Digital shadows are not real people, mistaken identities are driving waste
Many marketing teams are still confident in the data they’ve aggregated. There’s this belief that if your identity graph looks dense, filled with data, it must be accurate. That’s wrong. It’s not the presence of data points that matters. It’s the quality and truth behind them.
In reality, many graphs are filled with digital echoes. Email addresses that look valid but have no history. Phone numbers that no longer belong to real users. Device IDs that reflect shared usage or automated traffic. These are treated as customers. They’re getting offers and taking up marketing resources. And they’re being factored into performance models, which then become meaningless.
AtData worked with clients who thought their audiences were clean. After layering in email intelligence, 15% or more of those contacts were fake, invalid, or temporary. These weren’t fringe cases. These addresses were actively counted in reports, used in segmentation, and rewarded with discounts.
Why did these identities get through? Because identity graphs weren’t built to challenge their assumptions. They just connect what’s there. They don’t ask when a signal was created, whether it ever engaged, or if it matches fraud behavior.
Executives need to rethink the idea that more data equals more clarity. You don’t need noise, you need certainty. Without confirming whether someone is real, you’re not doing identity resolution. You’re just dressing up assumptions in data points.
Email is a living signal of identity
Email doesn’t shift like a cookie or disappear with a software update. It’s persistent. It stays with people across devices, platforms, and life changes. It connects their activity over time, logins, purchase confirmations, subscriptions, access to apps. If someone’s digital behavior has a backbone, this is it.
What matters most isn’t the address itself. It’s the behavior behind it. A primary inbox that’s been around for years tells you a lot, how often it’s used, whether it sees real engagement, how it handles transactions and notifications. You’re not just verifying that the email exists, you’re evaluating whether the person behind it is consistent and real.
This is the kind of signal you can rely on. It’s not easily spoofed. It’s not constantly changing. And it integrates into systems you’re already using, CRM platforms, eCommerce engines, fraud tools, and support desks. You don’t need to rip anything out. You just need to turn on the intelligence that’s already buried in the data you own.
Email also gives you a clear behavioral context. You can detect high-risk patterns before they leak into customer lists. You can flag dormant or suspicious inboxes. You can distinguish between real users and automated bots or short-term spam traps.
Backing this up, research out of MIT shows people revisit the same email threads over months or even years. That persistence is key. If you want stability in your identity architecture, it’s hard to find anything more durable or verifiable than email that consistently acts like a real person’s inbox.
Email-based identity drives measurable performance gains
When you anchor your identity strategy in active email behavior, your targeting improves immediately. You cut down fraud. You stop wasting time and budget on fake inboxes and invalid traffic. Most importantly, you reach people who intend to engage, and that drives outcomes.
The Direct Marketing Association reports that email, when done right, delivers an average return of $36 for every $1 spent. It’s the highest ROI across the digital marketing stack. That return doesn’t come from volume, it comes from precision, from knowing that your message is hitting inboxes that actually matter.
AtData’s work across multiple industries, retail, finance, lead generation, proves this further. Clients that validate and score their email identity data see a 10% to 25% drop in fraud exposure. They also see significantly better deliverability and bounce rates, and stronger re-engagement over time. These are measurable advantages that stack up quickly when scaled.
And since an email inbox is already consent-based and directly tied to personal behavior, it adapts well to privacy changes. It doesn’t vanish with platform policies. It holds firm even as other identifiers erode. From a strategy standpoint, this means you’re building on a signal that’s not just effective, but built to last.
If you’re serious about optimizing acquisition costs, protecting brand trust, and increasing engagement, you need to calibrate your identity layer around signals that actually hold up. Email, validated with intelligence and behavior, isn’t guesswork. It’s operational value.
Identity strategy must shift from linking data to verifying people
The future of identity in marketing isn’t about how many data sources you can connect. It’s about whether those connections point to a real human being. Most current systems still focus on stitching together touchpoints, cookies, device IDs, phone numbers, even if those signals are unstable, expired, or easily falsified. That model is outdated and increasingly ineffective.
If you don’t validate whether someone actually exists behind the data, you’re not confirming identity, you’re just reinforcing assumptions. And assumptions at this scale distort campaign performance, inflate engagement metrics, and expose your organization to fraud.
What’s required now is a shift in mindset. Ignore data quantity. Focus on data quality. Use signals that prove there’s a person with real activity and a consistent pattern over time. Ask harder questions: How old is this email address? Has it been used consistently? Does it exhibit normal behavior compared to active inboxes? Is there recent engagement?
Many systems don’t ask those questions. Most still operate in passive mode, connecting data instead of qualifying it. That failure leads to wasted spend, compliance risks, and customer experience breakdowns. This isn’t just a technology gap. It’s a strategy problem at the leadership level.
Executives need to demand accuracy before scale. The choice isn’t between fast and slow data enrichment, it’s between verified and unverifiable identity. In a privacy-driven digital environment, guessing at who your customer is will get increasingly expensive and risky.
If you’re going to spend time and budget on personalization, targeting, and attribution, start with the certainty that there’s a person on the other end. Strip out identifiers that can’t prove presence. Focus only on the signals that can. Clean data is no longer a nice-to-have. It’s the edge.
Main highlights
- Traditional identity signals are collapsing: Leaders should reduce reliance on outdated signals, like cookies, MAIDs, and recycled phone numbers, that have become unstable, restricted, or no longer reliable for long-term identity resolution.
- Assumed identity doesn’t equal verified identity: Executives need to push for deeper validation layers in identity graphs to avoid costly mistargeting, media waste, and performance distortion caused by treating temporary or fraudulent data as real.
- Email offers durable and verifiable identity: Prioritize email-based signals as a core identifier, when measured for activity and behavioral consistency, email provides long-term, cross-platform intelligence that significantly outlasts other IDs.
- Email-based targeting outperforms legacy methods: Investing in validated email identity drives measurable returns, including 10–25% reduction in fraud, improved engagement, and the highest ROI across digital channels at $36 per $1 spent.
- Verification must replace connection in identity strategy: Leaders should reframe their approach to identity by replacing volume-driven data matching with systems that confirm presence, engagement, and authenticity behind every user profile.
