Detect invalid clicks: Long-term strategies to stop fraud and protect ad spend

The detection of invalid clicks remains the biggest problem for marketers in online advertising presently.

Bots and dishonest individuals have been contributing greatly to invalid clicks, resulting in wasted ad spend.

According to Statista, approximately 50% of all internet traffic originates from bots. Most bots are not designed to assist but to cause harm.

Research indicates that up to 11% of internet ad impressions globally are invalid. In 2023, fraud from bots, invalid clicks, and click farms accounted for 22% of all digital ad spend, totaling $84 billion.

As ad fraud evolves, strategies to detect invalid clicks must be implemented. This article explores various sustainable detection techniques and how ClickPatrol secures campaigns by detecting and filtering out invalid traffic at the source.

Detect invalid clicks: Understanding the nature of invalid clicks and fraudulent traffic

We need to know what we mean by ‘invalid clicks’ first before we delve into strategies and who creates them.

What can be considered an invalid click?

An invalid click is a click interaction on a web-based ad that is not authentic.

These are not clicks by a real human or user with a real interest in your ad, product, or service.

These are divided into two general categories:

• Automated bots: software designed to look or act humanly, clicking ads, and imitating interaction.
• Fraud involving human assistance, such as click farms that use low-wage workers to generate large volumes of fake activity.

Invalid click activity wastes ad spend, invalidates performance metrics such as CTR (click-through rate) and CPA (cost per acquisition), and, in some cases, can cause reputational damage as fraudsters redirect analytics or misuse attribution.

How big is the problem?

• Based on analysis across various platforms, 14% of all PPC clicks are estimated to be fake or invalid, many of them the result of bots or organized fraud.
• The overall average invalid traffic rate of all advertising channels in digital campaigns is approximately 11%, and mobile formats and display ads are even more vulnerable.
• It is estimated that bots account for up to 37% of all online traffic, and bad bots are used to perpetrate click fraud, an attack designed to empty advertisers’ coffers and corrupt analytics.
• According to industry estimates, invalid traffic and click fraud cost marketers and businesses hundreds of billions of dollars globally, and overall ad fraud losses are projected to approach $172 billion by 2028.

The figures convey only one thing: invalid clicks are not isolated cases but systemic vulnerabilities that require long-term, scalable detection and mitigation plans.

Invalid clicks: Why traditional methods fall short

Publishers, ad networks, and basic analytics tools have long helped advertisers flag unusual activity, such as high click volume from a single IP, a suspicious geographic spike, or an inordinately low dwell time.

While these controls catch some fraud, they’re no longer sufficient in themselves. Fraud networks have grown more sophisticated, using tactics such as:

• IP rotation and residential proxies that bypass simple IP-blocking filters.
• Cloaking bot traffic that blends real human-like behavior patterns into bots.
• Geographic spoofing, which conceals the origin of fraudulent clicks.
• Click farms are coordinating across thousands of devices and networks.

In such an environment, long-term protection cannot be about mere reactive measures and basic filtering; it has to be about systems that understand real-time activity, behavioral signals, and pattern anomalies, not just surface metrics.

6 Proven long-term strategies to detect invalid clicks

Building a lasting defense against invalid clicks requires the following:

Long-term strategy 1: adopt real-time analytics for detection

Any successful attempt to identify invalid clicks relies on real-time analysis. By the time fraud is detected hours or days later, it has already cost the budget and corrupted performance data.

Real-time detection enables advertisers to detect threats as they occur and take action before they cause harm.

One of the major components of real-time detection is analyzing behavioral patterns. Rather than just counting clicks or IP addresses, sophisticated systems analyze post-click user behavior.

This involves the click cadence (the speed at which clicks occur), depth of engagement (whether users scroll, interact, or bounce quickly), and conversion behaviour (the frequency of clicks that result in meaningful action).

Human behavior can be imitated by bots, but the patterns often show small irregularities that machine learning models can quickly detect.

ClickPatrol uses machine learning alongside rule-based engines to track these behavioral signals in real time.

This allows the platform to identify invalid clicks in real time and prevent fraudulent traffic before it is billed, providing a much more proactive solution than fixed filters that rely on historical blocking.

Long-term strategy 2: use machine learning to adapt and evolve

Fraud methods are constantly changing, and fraud detectors must keep up. Machine learning models are trained to adapt to evolving data trends, recognize normal campaign behavior, and detect small anomalies that indicate invalid clicks, even when bots mimic real users too closely.

Over time, such models enhance precision, reducing false positives and revealing advanced invalid traffic that traditional rule sets often overlook.

The machine learning engines behind ClickPatrol analyze traffic patterns across numerous campaigns and enable dynamic threshold setting, anomaly scoring, and lifelong learning.

Long-term strategy 3: prevent fraud at the source, not post-billing

The most effective way to handle invalid clicks is to prevent them before they affect reporting or billing.

Trying to detect fraud after clicks are reflected in billing reports is too late to lose budget.

This can be stopped by source-level protection that inspects traffic in real time, before counting clicks.

ClickPatrol focuses on analyzing incoming click signals, scoring them using advanced scoring models based on session context, and blacklisting recognized bot signatures and malicious network actions.

Blocking fraud or spam requests at the source prevents any chargeable events and ensures that both spend and data are correct.

Long-term strategy 4: multi-channel security surveillance

Invalid clicks do not work on one platform. Bot networks usually target multiple channels, such as search, display, mobile, and social.

Isolating each platform creates blind spots. Cross-channel monitoring supports coherent analytics, uniform detection policies, and centralized notifications that expose coordinated threats.

ClickPatrol holistically calculates traffic across platforms, enabling advertisers to detect patterns that single-channel monitoring may miss.

Long-term strategy 5: ensure continued auditing and changeable rule sets

Patterns of invalid clicks evolve, and continuous audits are necessary. Periodic traffic reviews help detect new anomalies, and rule updates ensure detection stays aligned with campaign objectives and emerging fraud trends.

Comparing with larger traffic patterns will help ensure the detection’s accuracy and relevance.

Long-term plan 6: train teams and in-house analytics

A successful detection is not technical alone but organizational. Teams are expected to know the difference between normal and abnormal traffic behavior, the developmental stages of threats, and what to do when fraud is detected.

By combining internal analytics with fraud detection processes, a feedback loop is established in which insights reinforce prevention measures, bidding decisions, and the overall campaign performance.

How ClickPatrol detects and blocks invalid clicks at the source

ClickPatrol is for advertisers who are fed up with fighting invalid clicks. Instead, ClickPatrol lets you:

• Detects and blocks invalid traffic at source to ensure fake engagement activity is never charged against your spend.
• Helps analyze behavior and patterns of interaction to detect advanced botnet traffic that uses adaptive machine learning to evolve over time
• It offers cross-platform views for unified campaign protection.

This preventive approach to fighting fraud is not just proactive; it also prevents it from influencing performance, analytics, or revenue.

Building a resilient, long-term defense against invalid clicks

False clicks remain among the most tenacious and expensive threats to online advertising, and as bots and organized fraud rings become increasingly sophisticated, fundamental methods such as IP blocking or post hoc analysis are no longer sufficient.

An active, long-term strategy is needed to achieve sustainable protection through a combination of real-time behavioral detection, adaptive machine learning, source-level blocking, cross-channel monitoring, continuous auditing, and strong alignment with internal analytics.

By prioritizing the detection and prevention of invalid clicks before they impact spend and data, advertisers preserve campaign performance, retain valid insights, and safeguard long-term ROI.

ClickPatrol helps this approach by identifying and preventing invalid traffic at the source, providing advertisers with the certainty and accuracy needed to operate safely within an ever more complex ad ecosystem.

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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