Bot clicks on ads: Detect and prevent invalid traffic to protect ad spend

Bot clicks on ads are a growing threat to advertising budgets. Nearly half of all web traffic is automated, and a large portion of it is malicious, draining ad spend, distorting performance metrics, and leading to costly optimization errors.

In 2023 alone, ad fraud consumed $84 billion, a figure projected to rise without action. This guide shows how to detect bot clicks on ads using analytics filters, outlines the signals to watch for, and explains how ClickPatrol complements these filters by blocking invalid traffic in real time, preventing wasted ad spend before it occurs.

Why analytics filters matter for detecting bot clicks on ads

The signals you require to distinguish between human and bot traffic are gathered by analytics platforms (Google Analytics, GA4, server logs, and others): IP addresses, user agents, session duration, page depth, bounce rates, geolocation, and device information.

Effectively designed filters enable you to identify patterns of suspect early, eliminate noise in reporting, and input clean data into your bidding and attribution models.

Note: filters protect you by identifying and blocking suspicious traffic in analytics and reporting, but do not necessarily prevent bots from clicking your live ads.

This is why a combination of analytics filters and a proactive protection system that blocks invalid traffic at the ad-entry point, such as ClickPatrol, provides the best defence.

Top analytics signals that indicate bot clicks on ads

Watch out for these key analytics signals that indicate bot clicks on ads:

Unusual click-to-conversion ratios

Sudden spikes in clicks with no corresponding conversions are a classic sign of invalid clicks, especially when other metrics such as time on site and pages per session are also very low.

Extremely brief session length and single-page sessions

Bots commonly produce sessions under 5 seconds and instant bounces. If a campaign shows a high percentage of micro-sessions, flag it.

Suspicious user-agent strings and IP ranges of known bots

Wholly vacant, wholly generic, or utterly strange user-agent strings are red flags, plus traffic from data center IPs.

Geographical oddities

Heavy clicks from countries or regions outside of your targeting settings, or from unlikely locations for your business, typically indicate bots or click farms.

Impossible device/browser combinations

Combinations such as an unknown mobile user-agent, coupled with a desktop screen size or the exact resolution, raise flags for automation.

Repetitive timing and cadence patterns

The bots usually click either in nearly regular intervals or in enormous bursts-e.g., hundreds of clicks within seconds.

Referral traffic inflated from low-quality

Suspicious domains are low-quality or irrelevant referrers that deliver high click volume but zero engagement.

Practical analytics filters you can implement to detect bot clicks on ads

Here are some filter tips that you can create using GA4 and other analytical platforms. These are useful and are usually employed by analysts to identify patterns of bot clicks.

By known bot user agents and suspicious strings:

Keep a set of strings to identify suspicious user agents (headless, curl, python, scrapy, etc.), and filter out sessions where the user agent matches any of these strings (uhlassistant).

Exclude data center and cloud provider IP networks:

Block or segment traffic from hosting companies and cloud data centres (AWS, Azure, Google Cloud) if these IP addresses are not present in your audience. Many botnets and click farms involve IPs from the above.

Create a low-engagement segment for review:

Track sessions with a duration of less than 5 seconds, 1 page per session, and no conversions. Use this segment for campaigns to estimate the number of bot clicks.

Flag click bursts with time filters:

You can set an alert or filter for campaigns that show an unusual clicks-per-minute value. For instance, if the normal value is 10 clicks per hour and 2,000 clicks are registered in an hour, this needs further investigation.

Geo and Language MisMatch Filters:

Flag sessions where the language and geography do not match our targeting criteria, or where clicks originate from regions our company never markets.

Device fingerprint anomaly detection:

Record combinations of browser, OS, screen resolution, and timezone. Thousands of sessions with the same fingerprint imply automation.

Append UTM validation checks:

Additional parameters required within UTM parameters or token validation for sensitive campaigns; eliminate clicks that are lacking parameters or have invalid parameters.

How to stop bot clicks on ads

If bot clicks are found, take the following immediate actions:

• Pause the impacted campaign or ad group to prevent further spending.
• Record sample session IDs, IPs, timestamps, and user agents for documentation.
• Provide proof to the advertising platform (Google Ads, Microsoft Advertising, etc.) and request credits if applicable.
• Blacklist the identified IP addresses in your advertising account and/or server firewall.
• Submit suspicious indicators to a mitigation platform (e.g., ClickPatrol) to stop ongoing traffic and prevent future invalid clicks.

Limitations of analytics-only detection and why prevention matters

They include:

• Analytics filters are post-retrospective, showing patterns only after clicks have occurred.
• They are helpful in cleaning up reports and obtaining refunds, but do not stop real-time budget loss.
• Modern botnets and human-run click farms are fast and sophisticated, making prevention at the ad-entry point more effective.
• ClickPatrol blocks invalid traffic in real-time, preventing bot clicks from accumulating charges or corrupting analytics.
  Combining analytics filtering with source-level blocking is the most effective strategy.

How ClickPatrol complements analytics filters

ClickPatrol monitors incoming ad traffic in real time, blocking invalid clicks and filtering them by behavioural and technical criteria before they are counted as billable conversions or distort campaign data.

As analytics filters reveal suspicious patterns, ClickPatrol can leverage those cues to strengthen protection and automatically enforce blocking policies, minimizing manual cleanup and saving ad budget.

Quick checklist: filters to implement this week

Add the following necessary filters to heighten ad campaign security:

• Add user-agent exclusion list.
• Create a “low-engagement” segment for all campaigns.
• Set up alerts for sudden spikes.
• Block known data centre IPs in the account and server firewall.
• Validate UTMs on conversion-critical ads.
• Set GeoTargetingStrictness and review exceptions.

How much difference does detection make?

According to industry research, the scale of the problem is considerable and increasing; Juniper Research estimated that the amount of ad money wasted on fraud in 2023 alone is up to $84 billion, and this figure is expected to keep growing in the upcoming years, which is why the urgency of its active detection and prevention is significant.

According to Statista, nearly half of web traffic is bot-generated, which further supports the need for analytics teams to consider a minimum level of automated traffic when analysing campaign data.

Protecting your ad budget: Defending against bot clicks and invalid traffic

Protecting your ad campaigns from bot clicks on ads requires both detection and prevention. Analytics filters help identify suspicious patterns and clean reporting data, but real-time blocking at the ad-entry point is essential to prevent immediate budget drain.

ClickPatrol automatically monitors incoming traffic, blocks invalid clicks, and enforces rules based on user behavior, IPs, and device patterns.

By combining analytics filters with real-time protection, advertisers can preserve ad spend, maintain accurate metrics, and ensure that campaigns target genuine prospects, turning bot-click threats into manageable risks.

Abisola

Meet Abisola! As the content manager at ClickPatrol, she’s the go-to expert on all things fake traffic. From bot clicks to ad fraud, Abisola knows how to spot, stop, and educate others about the sneaky tactics that inflate numbers but don’t bring real results.

View all posts by Abisola (Abisola)

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