Advanced click fraud rule builder for power users

There's a specific type of advertiser — typically in-house paid media leads at mid-market companies, or senior PPC specialists at agencies — who has outgrown the default blocking rules in every click fraud tool they've tried. They know what they want to block: specific device fingerprint combinations, clicks from certain ISP types during specific hours, repeat visitors with anomalous session lengths. But as one user put it plainly: 'the software could benefit from more advanced settings and options for power users who want more control over how the tool works and what it blocks.'

This gap is almost entirely incentive-driven. Click fraud tools market themselves to a broad audience that wants fire-and-forget automation. Building a complex rule engine takes significant engineering effort, and the users who need it — maybe 15% of the customer base — are not louder or more valuable to the vendor than the 85% who want defaults. So the power user gets a tool that's simultaneously confusing to navigate (the UI complaints are consistent across products) and under-powered for what they actually need. They're stuck with blunt instruments dressed in complicated interfaces.

The real cost is miscategorization: when you can't tune your rules precisely, you either over-block (real users get caught in fraud filters, conversion rates drop mysteriously) or under-block (fraudulent clicks keep burning budget). Both outcomes are expensive and hard to diagnose. The power user knows this is happening but has no mechanism to test hypotheses — no sandbox to simulate what a rule change would have caught historically, no way to see the counterfactual.

This is a business because sophisticated advertisers run continuous optimization cycles — they're not setting rules once and walking away. Every major campaign change, every new traffic source, every seasonal audience shift potentially requires rule recalibration. The need doesn't diminish as the user gets better at their job; it intensifies, because they start seeing patterns the defaults will never catch.

What to build

Build a rule-authoring interface for click fraud filtering that lets users define multi-condition blocking logic using a visual condition builder, backtest any rule against 90 days of historical click log data to see what it would have blocked, and deploy exclusion lists to Google Ads and Meta Ads automatically when a rule fires.

Where to start

Start with advertisers in verticals that are disproportionately targeted by click fraud — legal services, financial products, insurance — where a single fraudulent click can cost $50–$300 and the ROI of precise blocking is obvious enough that a power user will pay a premium on day one without needing to see a case study.

The hard part

Backtesting requires storing large volumes of raw click event data per customer account, which makes infrastructure costs significant before you have pricing leverage — you'll need to solve data retention limits and storage costs early or the unit economics break before you reach scale.

How it makes money

Usage-based pricing anchored to ad spend under management — a percentage of monthly managed spend (e.g. 0.3–0.5%) with a monthly minimum, so revenue scales naturally as customers grow their campaigns.