Catch Click Fraud in Google Ads With Session Recordings

Catch Click Fraud in 2026 in Google Ads With Session Recordings

By Michael Green | January 18, 2026

Catch Click Fraud in 2026 In Google Ads With Session Recordings

Session recordings showing repeat click patterns in Google Ads that can signal click fraud

Many advertisers look for click fraud in the wrong place. They stare at CTR, CPC, and average session duration and expect fraud to look loud and obvious. In reality, many attacks are designed to look normal. They blend into your account, avoid extreme spikes, and leave you with the same outcome every week: spend rises, conversions stay flat, and lead quality gets worse.

Most accounts do not fail because they cannot spot a problem. They fail because response time is slow. Click fraud is built for that delay. It repeats day after day until someone reacts, and by then the account has already paid for a large volume of waste. That is why the real fix is a 2 part system: fast detection of repeat patterns, and fast blocking of the source automatically.

Google uses systems to detect and credit invalid traffic. It helps, but it does not solve everything for your business goals. A click can be considered valid by the platform and still be worthless for you, because it never had real intent. Google explains their approach here: invalid traffic.

The most reliable way to confirm suspicious activity is not another dashboard metric. It is behavior after the click. That is why session recordings matter. They let you see whether a real person explored your offer, compared options, and moved toward an intent step, or whether the visit followed a repeat script that never goes anywhere.

Article navigation

Why click fraud often looks normal

Many people imagine click fraud as a huge spike from 1 location or 1 device. That still happens sometimes, but it is not the most common pattern in modern attacks. Attackers learned how advertisers react and how platforms detect extremes. So they spread activity across IPs, rotate locations, vary user agents, and avoid obvious repetition in account level reports.

The goal is simple: drain budget without producing a clean signature that triggers a fast block. That is why you can see stable CTR and CPC while business outcomes get worse. The platform measures interactions. You measure intent and outcomes.

Click fraud also hides because the damage is indirect. It does not only waste money on fraudulent clicks. It poisons optimization. When low intent clicks dominate a campaign, bidding systems learn from the wrong signals. Your best segments lose budget, CPA rises, and lead quality falls.

What repeat patterns really mean

Most advertisers think repeat clicks means the same person clicked twice. Sometimes that is true, but it is not the main problem. The real danger is repeat behavior across many visits that appear to be different users in Google Ads. Different IPs, different locations, different devices, but similar timing, similar paths, and similar exits.

Repeat behavior can come from bots that mimic human actions, click farms following a task flow, competitor activity designed to drain spend, or low quality placements that generate accidental clicks. The label matters less than the pattern because your response depends on what the sessions show.

For a practical overview of using recordings for traffic quality checks, use this internal guide: session recordings.

How to review recordings fast

Most teams fail with recordings because they watch them across mixed sources. 1 from Search, 1 from Performance Max, 1 from Display, then a different country. That produces noise, and noise creates wrong conclusions.

Start with 1 clean segment. Choose a single traffic bucket and keep it strict for the first pass. Examples: Search non brand only, Performance Max only, or Display only. Then review a small sample. In most accounts, 20 to 30 sessions is enough to spot repetition. You are not trying to compute a precise percentage. You are trying to see whether behavior repeats.

Keep your notes simple. Track only what drives decisions: time to first meaningful action, scroll pattern, click targets, and exit point. Do not label a problem based on 1 session. A single weird visit is normal. A repeated pattern inside 1 segment is the signal.

What click fraud looks like in recordings

Click fraud in recordings is rarely a single smoking gun. It is overlap. When multiple signals show up together and repeat in the same segment, probability climbs fast.

The clearest pattern is repeated timing with repeated navigation. A typical beat looks like this: land, micro scroll, click a non critical area, pause, exit. Then the same beat appears again and again. Normal users do not behave like that at scale.

Another common pattern is engagement that looks present but has no intent. The session lasts 60 to 120 seconds, which looks normal in reporting, but there is no natural reading rhythm, no comparison behavior, and no movement toward pricing, contact, booking, or checkout. The visit exists to look real, not to buy.

You will also see fake exploration. The user clicks, but the click targets are low value: repeated menu taps that do not lead to a decision page, random header areas, or repeated clicks on elements that do not change anything. This creates the illusion of engagement but never approaches an intent step.

Forms add another layer. Fraud and lead spam attempts often produce fast, unnatural form behavior. You may see rapid focus changes, paste events, and repeated starts that never complete. Be careful because password managers can look similar in 1 session. The difference is repetition and context. If the same form behavior repeats across many sessions from the same segment, and those sessions show no real exploration, treat it as a stronger signal.

Look for clustering. Fraud often arrives as clusters that share multiple traits at once: similar session length, similar scroll rhythm, similar click targets, similar exit page, and similar device environment signals. 1 trait alone is weak evidence. Several traits repeated together are strong evidence.

Fraud vs bad traffic

If you mislabel bad traffic as fraud, you may block real demand. If you mislabel fraud as bad traffic, you keep paying for waste. Use recordings to separate them.

Bad traffic is usually messy. Sessions vary. Paths differ. Some users scroll a lot, others bounce, some look for details, some get lost. The consistent theme is low intent, but behavior does not repeat with tight similarity. Bad traffic often comes from broad targeting, weak keyword intent, or weak match between ad promise and landing page.

Click fraud is usually repeatable. Even when IPs rotate, behavior keeps a pattern. You will see a similar story: similar pacing, similar shallow actions, similar exits. It is not 1 weird user. It is many sessions that look like they came from the same playbook.

Use a comparison set to stay honest. Watch 10 sessions from users who converted or at least reached a serious intent step. That gives you a baseline for how real prospects behave in your funnel.

Build an evidence pack

Click fraud decisions should not depend on memory. They should depend on a small, repeatable evidence pack. This is how you move from I think something is wrong to I can justify action.

For each suspicious cluster, capture the same five items: campaign or source, landing page, time window, the repeat behavioral signature, and the impact on outcomes. Impact can be simple: rising spend with flat conversions in that segment.

Your goal is not to collect hundreds of clips. Your goal is to collect enough consistent examples to support controlled action. If you can show 8 to 12 sessions from the same segment that repeat the same behavioral story, you have enough to move.

What to do after you confirm fraud

Once you confirm repeat fraud shaped behavior, the next step is controlled isolation. Reduce exposure where the pattern lives while you protect the parts of the account that drive real outcomes.

Start by locating the fraud segment. Most accounts can isolate it by campaign type, a specific campaign, a set of search themes, a geo pocket, a device class, or a placement source. If you cannot isolate it, you cannot fix it cleanly and you risk blocking real demand.

If the pattern lives in Search, treat it as an intent and targeting issue first, then apply exclusions. Tighten match types where needed. Add negatives based on what sessions show users expected. Separate brand and non brand so the highest intent traffic does not get polluted by the lowest intent traffic.

If the pattern lives in Performance Max, stop feeding the system low quality engagement signals. Split asset groups by offer or intent theme. Tighten creative so the click matches the landing page promise. Reduce broad reach behavior where you see repeat shallow sessions.

If the pattern lives in Display or YouTube, focus on inventory quality. Narrow sources that generate repeat low intent behavior and redirect budget to traffic that shows real progression.

Then apply exclusions where evidence is concentrated. IP based action can help when you have repeat offenders. Use it as a tool, not as the only defense, because rotation is common. If you want a step by step internal guide, use: Google Ads IP exclusions.

Also check for false positives. If many sessions fail at the same form field, fix the form. If many sessions exit fast because the page loads slowly, fix speed. Fraud exists, but so does friction. Both waste budget, and both are fixable.

Solution: automated blocking with Clixtell

Once you know what click fraud looks like, the real challenge is speed. Fraud does not wait for your next weekly review. It repeats every day. If response time is slow, you keep paying the same source of waste again and again.

That is why Clixtell is the practical solution. It is built for fast detection and fast blocking. Clixtell helps you identify repeat patterns quickly and stop the source automatically, so you reduce wasted spend, keep cleaner data for optimization, and protect your best campaigns without constant manual firefighting.

FAQ

Why does click fraud look normal in Google Ads reports?

Many attacks avoid extreme spikes and spread activity across rotating IPs and devices. Reports can look stable while post click behavior shows repeat low intent patterns.

How many recordings do I need to confirm click fraud?

In most accounts, 20 to 30 recordings from 1 clean segment is enough to see whether behavior repeats. Look for clusters, not 1 off sessions.

What is the clearest recording signal of click fraud?

Repeated path with repeated timing across many sessions inside the same segment, especially when sessions never move toward pricing, contact, booking, or checkout.

What should I do first before adding IP exclusions?

First, confirm the repeat pattern with session recordings and isolate where it lives (campaign, geo, device, or placements). Then use Clixtell to automatically detect and block the source fast, and apply IP exclusions only where evidence is concentrated, since rotation is common.

How does Clixtell confirm click fraud besides session recordings?

Clixtell confirms click fraud using a Security Profile that combines detection signals and rule based enforcement. It analyzes repeat click patterns, IP and ASN risk, VPN and proxy indicators, device and browser fingerprints, geo anomalies, click frequency spikes, and post click behavior signals. Then it applies Security Rules to automatically block or exclude suspicious sources, reduce repeat waste fast, and keep campaign data clean for optimization.