Stop fake signup waves
Stop disposable-email farms draining promo budgets at scale
Promo Abuse Prevention
Prevent promo abuse from draining marketing budgets
Keep first-order discounts, promo codes, and referral credits on real customers, and acquisition spend on real growth
How ShieldLabs helps
Block repeat promo redemptions
Spot the same person across emails, IPs, and devices claiming the same code twice
Protect acquisition spend
Keep first-order discounts, referral credits, and welcome offers driving real growth
Keep real shoppers friction-free
Legitimate buyers complete checkout without extra verification or holds
How ShieldLabs prevents promo abuse
ShieldLabs recognizes the real visitor behind every anonymous session, so your team can stop fraudulent promo redemptions in real time
Accurate Identification
Identify returning visitors and users across sessions, cleared cookies, incognito mode, and rotated IP
Risk Signals
Detection of VPN, proxy, Tor, anti-detect browser, IP reputation and other risk signals on every visit, with 99.9% risk signal detection accuracy
Risk Score
A ready-to-use score reflecting the risk of each visit, with the weight of every signal behind it
High-Risk Events
High-Risk Events detected out of the box: multi-accounting, account sharing, impossible travel and account takeover, each with Medium or High confidence
Real-Time Analytics
See how much of your traffic is masked, with an overall quality score and a breakdown of the sources sending your visits
Start preventing promo abuse in 5 minutes
Easily integrate into any signup, checkout, or promo redemption flow
- 1
Create your account
Get 5,000 free identifications
- 2
Add the snippet
It identifies every visitor and returns their risk signals and a risk score
- 3
Check your traffic quality
See how much of your traffic is masked, with an overall traffic score
- 4
Use the API and Webhooks
Get the risk score, risk signals and High-Risk Events in your backend to stop fraud and abuse
api.shieldlabs.ai/v1/visits/latest
{
"request_id": "0c284695-cf0b-4755-8beb-0a2e9536595e",
"visitor_id": "aa8c616a-8a25-4a5e-bee2-a9a08e5128a4",
"device_id": "6a45967d-1371-9652-ba99-b01ea3992208",
"user_hid": "u_9f2a41c7",
"public_ip": { "ip": "62.197.149.124", "country": "United States" },
"local_ip": { "ip": "45.83.91.7", "country": "United States" },
"connection_type": "vpn",
"os": "Windows",
"browser": "Chrome",
"device_type": "desktop",
"risk_score": 85,
"signals": [
{ "name": "antidetect_browser", "weight": 60 },
{ "name": "vpn", "weight": 15 },
{ "name": "timezone_mismatch", "weight": 10 }
]
}Start preventing promo abuse today
Free 5,000 one-time identifications, with transparent pricing that scales with your needs
Frequently asked questions
Promo abuse is when one person claims the same promotional offer repeatedly under multiple identities to extract more value than the offer intends. ShieldLabs links those identities back to one persistent visitor and user across sessions, regardless of email, IP, or cleared cookies, so repeat claims are detected as multi-accounting.
Promo code abuse usually breaches a merchant's terms of service rather than criminal law, so enforcement is a policy decision the merchant makes. Criminal prosecution is rare unless stolen identities or stolen payment methods are involved. What merchants can do: void orders, reverse discounts, close accounts, and refund manipulated transactions under their own terms.
Coupon fraud usually means counterfeit physical coupons created or sold for resale, while promo abuse means one shopper creating multiple accounts to claim digital offers meant for new customers. ShieldLabs links each new account to one persistent visitor, so promo code reuse, signup farming, and discount stacking across fake identities are detected as multi-accounting.
Discount abuse is the broad term for any exploitation of merchant discounts by one person operating across multiple identities. It covers first-order code reuse, coupon stacking, voucher fraud, and self-referrals through fake accounts. Merchants sometimes call this discount fraud or voucher abuse, but the mechanism is the same. ShieldLabs links those identities back to one persistent visitor and user, regardless of email, IP, or cleared cookies.
Coupon abuse is when one person redeems a coupon more times than the offer allows, usually by creating multiple accounts. ShieldLabs links each redemption back to one persistent visitor and user, so a coupon reused across many identities is detected as multi-accounting, regardless of email, IP, or cleared cookies.
Promo abuse overlaps with several adjacent abuse types that share the same multi-accounting tactic.
- First-order fraud: an account opened purely to claim the first-order discount, then abandoned.
- Refund abuse and policy abuse: where the same multi-accounting feeds both stacked discounts and abused return windows.
- Incentive fraud: any signup incentive farmed across multiple identities.
ShieldLabs detects promo abuse at signup by combining several signals into one decision, upstream of the discount being applied.
- Device identification across sessions, cleared cookies, and incognito.
- IP reputation and anonymizer signals: VPN, proxy, Tor, residential ISP versus egress IP mismatch, and anti-detect browser detection.
- Multi-accounting detected out of the box as a High-Risk Event.
ShieldLabs runs all of this from a single JS snippet, returning persistent visitor and user identification with a 0–100 Risk Score on every visit.
ShieldLabs gives you enterprise-level functionality without enterprise pricing: the full signal set, self-serve from $99/month, where enterprise fraud platforms gate the same capability behind five-figure annual contracts and sales-led onboarding. ShieldLabs is ready for promo abuse prevention in 5 minutes from a single JavaScript snippet, no sales call required.
ShieldLabs stops promo abuse by flagging risky redemptions and helping block fraudulent and abusive traffic. It returns a Risk Score, a full Details breakdown and High-Risk Events, and you choose the action for each case.
- Suspicious score (30-59): require email or phone verification before applying the discount.
- Dangerous score (60-100): hold the order for manual review.
- Trusted score (0-29): let the legitimate shopper through with no extra friction.