Psst. 🕵️
Right now, there’s a murder mystery happening inside your Marketing budget.
Campaigns look like they’re working. Budget is disappearing. The dashboard shows conversions. AI tools are humming.
And yet…the actual results don’t add up.
You might blame the channel, or the algorithm, or the creative.
Marketers who stay stuck in this loop are playing Scooby-Doo (been there) – running around, pulling off masks, blaming whoever’s nearby.
Meanwhile, the 1s who discover the real culprit are thinking like Sherlock Holmes. They follow the signal. Which almost always leads back to the same place.
To quote Sherlock Holmes:
“Data, data, data!”
Quick (Email) Data Detour
When I say “email data,” what do you think of?
Open rates and subscriber lists? Inbox stuff? You wouldn’t be alone. But we’re not talking about inboxes today.
An email address =/= an inbox.
An email address is an IDENTIFIER.
It’s the closest thing we have to a universal, online tag that says “this is a real human being!”
Think of it like a behavioral fingerprint. 📧
Every time someone uses their email to log into an app, create an account, claim a discount, authenticate a transaction, sign up for loyalty programs, nab a waiting list spot, or subscribe to their favorite newsletter (hi)…they generate a signal.
Stack those signals over time, and you get a profile that says whether the person behind the email is a) real, b) active, and c) worth Marketing to.
Good news: you probably already have the data you need to answer these Qs.
Bad news: you might not know how much of it is still accurate…or whether it was even real to begin with.
Ready to crack the case?
Let the investigation begin.
5 Signal Crimes Killing Your Marketing
CRIME #1: Your AI Is Learning From Your Worst Customers
Everyone has an AI strategy right now.
Personalization engines. Predictive lead scoring. Lookalike audiences. Send time optimization. Churn prediction. The whole stack.
Here’s the catch: your AI learns from whatever you feed it. And right now, you’re probably feeding it a CRM full of bots, fake signups, abandoned emails, and ghost profiles mixed in with real customers.
The model can’t tell the difference. It treats every record equally, and finds patterns across all of them. Then, it makes confident recommendations based on those patterns.
That means, if 20% of your “customers” are synthetic identities – a problem only getting worse in ad tech – your AI just built a model that includes them as the target.
Now, it’s optimizing toward finding more people who look like bots.
You’ll never see this in the output. The model still produces recommendations, and they still look smart. They’re just silently wrong in a direction that’s costing you more money every month.
AI-generated identities are entering funnels at scale and getting harder to detect with basic validation. The only way to catch them is behavioral signals, tracked over time. Asking things like “does this email address have real human patterns behind it?” instead of just “does this email exist?”
🔎 Follow this lead: Before you evaluate any AI output, evaluate the input. Garbage in = garbage out. Ask your vendor how they handle identity quality in the training data. If they look confused by the question, you have your answer.
CRIME #2: Blaming Channels For a Data Problem
Picture the scene: you’ve fed a custom audience to a walled garden like Meta, or a retail media network like Amazon Ads or Walmart Connect – platforms where you send customer data and get very little transparency back out.
You spend real budget. You get back mediocre performance. So you blame the platform.
What probably happened instead: you fed it a list full of stale records, churned contacts, and fake accounts. The algorithm did exactly what it was designed to do and found more people who look like your audience. Your audience…just happened to be mostly noise.
Not that you’ll have many clues to go off: you won’t get a breakdown of what went wrong. The platform just hands back a disappointing-looking number (and a rep telling you to increase your budget.
The part that will make you tear your hair out: you could actually be getting quality conversions that are getting lost in the bad data. This actually often gets worse the bigger the company. Fortune 500 brands with massive tech stacks, but absolute garbage data inputs.
TL;DR: the channel isn’t broken. Your seed list is broken.
🔎 Follow this lead: Before you upload any audience anywhere, score it by engagement recency. Exclude anything without real behavioral activity in the last 90 days.
Your seed list will be smaller. Your lookalike will be dramatically better.
When performance improves you’ll know it was the data, not some mysterious algorithmic magic you can’t replicate.
CRIME #3: Your Suppression List Is Broken – In Both Directions
Your suppression list is supposed to protect you from mailing people you told your ESP never to mail: unsubscribes, hard bounces, or inactive contacts.
In practice, bad identity data breaks your suppression list in 2 directions. Both cost you.
1st, you’re suppressing people you should be mailing. Someone changed their email address. Their old 1 bounced and landed on your suppression list. Their new 1 came in through a different form and is sitting in your active list. You have no way of knowing they’re the same person. Now, you’re either mailing them twice or suppressing a real customer and are wondering why your retention looks off.
2nd, you’re mailing people you should be suppressing. The address passed your validation check and looks fine on paper. But in real life, the person abandoned that inbox 8 months ago. Now you’re burning send volume, tanking your engagement rate, and hurting your sender reputation on contacts that are functionally dead.
People change emails all the time. When they do, their behavioral patterns drop off. You need a system that catches that shift BEFORE you make decisions based on stale information.
🔎 Follow this lead: Audit your suppression list quarterly. You’ll find real customers stuck in suppression from an old bounce. You’ll find dead weight in your active list that should be retired. Or skip the manual work – a vendor like AtData does this continuously – searching for human behavior patterns and flagging shifts in engagement behavior before they turn into deliverability damage. That’s the difference between just email validation, and using email to track better signal architecture.
CRIME #4: Death by a Thousand Bounces
FACT: Email list decay runs at about 20 to 25% per year. People graduate, change jobs, or ditch inboxes that are too full of spam.
That doesn’t mean you’ve lost them as a customer, but it DOES mean you are mailing into the void.
If you keep mailing the dead addresses, your bounce rate climbs. Your spam complaint rate goes up. ISPs start flagging your sender reputation.
Once that happens, you’re losing delivery on the good addresses, not just the dead end inboxes.
A 1-time list scrub isn’t the answer – it only tells you what was true the day you ran it. 2 months later, your list has decayed again and you’re back to the same problem.
What works is continuous identity validation. Instead of asking whether or not an email still exists, it says whether the person still uses it. Those are 2 completely different questions – and only one of them matters.
Example: AtData answers the 2nd question, because they process behavioral signals at scale across 150 billion monthly events. A valid email nobody’s touched in 14 months gets treated completely differently than a valid email someone opened yesterday.
🔎 Follow this lead: Stop treating list hygiene as a 1-time event, and pick a cadence. If you aren’t running identity resolution automatically, run your most important segments through engagement scoring: mail your high activity contacts first, watch your deliverability metrics, and work down the list from there. Intentional re-engagement messaging, instead of blasting everyone at once.
CRIME #5: Fraud Is In Your Funnel And It Looks Like Growth
This is the trickiest 1, because it doesn’t look like fraud from the inside. It looks like a really great week.
New accounts up. Sign-ups trending the right direction. Conversions looking healthy. Everything’s coming up green. Except…a huge chunk of it isn’t real.
Story time.
A major fast food chain – you’ve definitely eaten there – ran a new account promo offering free food for new sign ups. Accounts started rolling in. Numbers looked fantastic. Obviously.
Turns out, people were spinning up 1000s of disposable email addresses, harvesting coupons in bulk, and reselling the codes on a massive forum you also definitely know.
Once AtData plugged into this company’s data, they caught millions in fraud that email validation was never going to catch – because the email accounts were technically valid.
AtData caught them because they’re running behavioral checks instead of standard email validations. They looked for the patterns of real humans – who engage at human speeds, on email addresses with history behind them, where they interact consistently over time.
Fraudulent identities spike, cluster, and show up in batches. They also have no behavioral history because they were created 11 minutes ago, LOL.
If those fraudulent emails make it into your CRM, your coupon abuse problem turns into a targeting problem turns into a whole-strategy problem.
🔎 Follow this lead: When a campaign goes live, watch what KIND of accounts come in, instead of how many Real signups bring addresses with behavioral history behind them. AtData does this by slotting right into in your tech stack, right at the account creation layer, where it catches exactly that distinction in real time, flagging the velocity spikes and zero-history addresses BEFORE they steal promos or get into your system and start teaching your models the wrong things.
Ready to solve the case?
Case, Closed
Ladies & Gentlemen of the Marketing Jury…these 5 crimes have the same perpetrator. [Gasp!]
1 perpetrator, 1 root cause that compounds through your entire operation:
Your data is a snapshot, and the real world isn’t.
The downstream damage of static data is what traps you in a loop that starts you back at Crime #1:
Fake accounts enter your CRM and get tagged as customers.
You upload tainted audiences to Meta or Amazon or your own AI.
They influence your segments. They train your models.
Your AI optimizes toward more of the same.
Fraud enters your funnel at scale, because the old hygiene rules don’t catch them anymore.
And the whole time everything still looks kind of fine on the dashboard.
AtData was built to interrupt this crime wave. Their activity network processes 150 billion behavioral signals a month – logins, transactions, authentications, app activity – all anchored to email addresses. Continuously tracking real behavior, in real time – not in snapshots. They were building this network before “identity resolution” was even a thing.
Also, AtData isn’t a platform or a product you demo.
They don’t replace anything in your stack – they slot in via API wherever you need them. Form fills. Account creation. Ad audience uploads. CRM cleanup.
They’re like the secret sauce behind the scenes. It’s 1 of the reasons Experian acquired them. (You don’t get bought by 1 of the biggest data companies on the planet because your syntax checker is slightly better than the competition, LOL.)
My point is, the clues were in your data the whole time.
Now it’s time to follow them.
👉 See how AtData works for Marketers.
