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DTC e-commerce fraud protection
Keep Bot Purchases Out of Your Prospecting Budget
DTC growth runs on automated bidding that buys more of whatever converts. A bot checkout reads as a winning customer, so Advantage+ and Performance Max widen the audience that produced it. ClickLens scores every paid session and grades each checkout on its provenance before a fake order steers your prospecting budget.
Where the money goes
Poisoned prospecting
Broad prospecting on Advantage+ and Performance Max optimises straight to purchases. A bot checkout reads as a new customer, so the algorithm widens the audience that produced it and spends more chasing the same source.
Card testing
Card-testing scripts run small orders to validate stolen card numbers. Each one fires the purchase pixel, draws a chargeback days later, and pushes your fraud rate up while the order count still looks healthy.
ROAS you cannot trust
Return on ad spend is the number every DTC budget decision hangs on. Fake orders inflate it, so a campaign that reads as profitable is quietly buying clicks that never had a buyer behind them.
CAC that flatters the report
Customer-acquisition cost reads off the conversion count. Count fake checkouts as customers and CAC looks lower than it is, so you scale spend on a number that will not survive contact with real revenue.
How ClickLens fits DTC e-commerce
One tag across the whole paid mix
Install the ClickLens tag once and every paid session is scored across automation, behaviour, fingerprint, network and context — Google, Meta, Microsoft and TikTok alike, not one platform at a time.
Checkout grading on provenance
Each checkout is graded on how it was completed and whether the journey holds together, measured as timing and structure, never card or customer details. A card-testing script that fills the form in a fraction of a second never reads as a buyer.
Exclusion sync to Google
Detected bot IPs and placements sync to Google Ads so repeat offenders stop being served on the next sync. Microsoft and Meta placement sync is rolling out; TikTok is report-only.
Verdicts before they reach reporting
Pass, downweight or retract resolves on every order before it trains the bidder. On Google a retraction is written back keyed by gclid, under a measured rollout against a 10% holdout.
What you get
- Real-time session scoring across Google, Meta, Microsoft and TikTok
- Provenance grading on every checkout, with no card or customer data read
- Card-testing and bot-order detection before the chargeback lands
- Pass / downweight / retract verdicts, written back by gclid on Google
- IP and placement exclusion sync to Google Ads (Microsoft and Meta rolling out)
- A wasted-spend figure by campaign and placement
- Free plan with 1,000 sessions a month, no credit card
See how conversion grading works in conversion protection, or stop click fraud on Google Ads.
Common questions
How does a bot checkout hurt my prospecting?
Advantage+ and Performance Max optimise toward whatever converts. A fake order reads as a new customer, so the algorithm widens the audience that produced it and spends more on similar traffic. Grading the checkout before it trains the bidder breaks that loop.
Does ClickLens read my customers' card or contact details?
No. The checkout grader uses the timing and structure of the submit, never the field values, so it works on any checkout — Shopify, headless or custom — without touching PII.
Can ClickLens stop card testing?
It detects the scripted, high-speed order pattern card testing uses and grades those checkouts as fraudulent before they train your bidding. The chargeback still lands with your processor, but the conversion stops steering your budget.
Other use cases
See how much of your spend reaches a real person
Install one tag and ClickLens scores every session and grades every conversion in the open. Free plan with 1,000 sessions a month, no credit card.
Sources
- Imperva (Thales), “2025 Imperva Bad Bot Report” , 2025. Accessed 26 June 2026. imperva.com
- Association of National Advertisers (via WFA), “ANA’s 2024 Programmatic Benchmark Study” , January 2025. Accessed 26 June 2026. wfanet.org