A strategic framework to reduce non-human ad clicks long-term

Non-human ad clicks are increasingly driving online advertising interactions, as bots and automated traffic replace real human users across many campaigns.

These clicks negatively affect advertising performance data, leading to wastage of marketing budgets. Juniper Research projects that global losses from ad fraud could climb toward $170 billion by 2028.

This article outlines a four-pillar framework covering source-level detection, continuous monitoring, platform collaboration, and operational discipline.

What are non‑human ad clicks?

Non-human ad clicks are clicks on digital advertisements created by automated systems and not initiated by real people.

These can result from fraud rings, botnets, click farms, or poorly configured crawlers. The objective of non-human activities can be to artificially inflate traffic metrics, drain a competitor’s advertising budgets with click spam, or to collect an advertiser’s spend without providing any value.

Unlike accidental invalid clicks, such as those caused by bots indexing content or benign crawlers, fraudulent automated clicks are intentionally strategic and provide no business value.

Why non-human ad clicks matter for advertisers

Non-human ad clicks directly affect all the stakeholders in the online ad ecosystem, including:

Budget leakage for advertisers

Advertisers are charged for each ad click, impression, or conversion, depending on the ad model.

If non-human ad clicks are involved, the advertiser will end up paying for non-converting ad impressions with zero chance of conversion.

Distorted analytics

Non-human ad clicks distort the analytics for the ad campaigns. Marketers may receive false information, leading to incorrect decisions.

Damage to the publisher’s reputation

Publishers who are unknowingly carrying non-human ad clicks may lose the premium ad revenue they once enjoyed.

Erosion of ecosystem trust

The more non-human ad clicks dominate the headlines, the more distrust there is in the ecosystem.

According to reports, ad fraud is one of the biggest concerns for digital marketers’ budgets, with some choosing to exit the ecosystem or move to a safer space.

The long‑term challenge of non‑human ad clicks

The fight against bot traffic is not new; however, to reduce this issue in the long run, there is a need for structural and strategic thinking. The major challenges in this regard are as follows:

Constantly evolving bot technology

Fraudsters also invest in technology that mimics real users’ behavior, for example, by varying click times, visiting pages as real users do, and maintaining sessions to avoid detection.

Fragmented detection across platforms

The definitions of invalid traffic vary across ad platforms, ad networks, and publishers. Without uniformity, there will be gaps in detecting bot traffic.

Resource barriers

Small advertisers and publishers face resource constraints that prevent them from implementing sophisticated detection technologies.

Reactive approaches

Current solutions to the problem of bot traffic are mostly reactive, such as using post-click analytics to identify bot traffic after the campaign has run.

To reduce non-human ad clicks in the long run, a more proactive approach to bot traffic is needed.

How to reduce non-human ad clicks long-term

This framework has four pillars:

• Detection and filtering at the source
• Continuous monitoring and adaptive modeling
• Policy and platform collaboration
• Education and operational discipline

Below is a detailed discussion of each.

Pillar 1: Detection and filtering at the source

The best way to reduce non-human clicks on ads is to identify and block them at the source. The conventional approach is to use post-click logs and anomaly detection, i.e., after the clicks have occurred.

However, source-level detection identifies and prevents non-human clicks at the source, thus avoiding:

• Fraudulent click attribution
• Ad wastage
• Distorted advertising campaign metrics

This is exactly what ClickPatrol does: real-time identification and prevention of invalid traffic before it is reported or billed to advertisers.

Key techniques for detecting non-human ad clicks at the source

Behavioral profiling of click sources

• Monitor for patterns in time and mark sources with bot-like behavior (e.g., excessive volume, implausible timing).
• Employ heuristics such as quick clicks, consistent intervals, or impossible geography-to-ISPs.

Bot signature and fingerprint databases

• Maintain frequent updates for known bot and crawler signatures.
• Share intelligence across campaigns and clients to speed up detection.

Device and browser validation

• Detect irregularities in user agent strings, screen sizes, or rendering patterns not typical of real-world devices.
• Employ browser integrity checks and responses for questionable traffic.

CAPTCHA as a gatekeeper (Strategically)

• Implement light CAPTCHA or behavior challenges only for sources that match a pattern of questionable behavior.
• Do not compromise the human user experience.

Implementation considerations

• Real-time blocking: Block invalid click sources before they reach analytics or billing systems.
• Filtering rules: Keep dynamic, customizable filters that adapt to campaign behavior.
• Automation: Manually configuring rules is not feasible at scale; use automated systems to dynamically adjust filtering based on data.

Pillar 2: Continuous monitoring and adaptive modeling

Stopping fraud once is not sufficient. The methods that are used to commit fraud will continue to evolve.

Thus, to be successful over time, this must be continuously re-monitored and re-evaluated.

Real-time analytics dashboards

Create an analytics dashboard to track:

• Click anomalies vs. previous trends,
• Unusual spikes in click activity,
• Geographic or device groups that are acting strangely.

This must be available to marketing or analytics teams to view in near-real time.

Machine learning & predictive modeling

Using machine learning techniques, models can be created to distinguish legitimate from fraudulent click activity by analyzing millions of user interactions and identifying patterns.

• Session length,
• Action sequences,
• Click timing anomalies.

These models must be built using known-good vs. known-bad/invalid sources. Models must be retrained as more data becomes available.

Adaptive thresholds

Static thresholds, such as “block traffic if > X clicks per minute,” are not sufficient and will quickly become ineffective.

Adaptive thresholds adjust to seasonality and campaign effects, reducing false positives while keeping detection sharp.

Feedback loops

Feedback loops must be implemented to allow:

• Data from post-block traffic to be used to improve models,
• Known fraud sources are to be logged to improve models.
• Human analysis of suspicious traffic to be incorporated into machine-level decisions. This is a necessary component for creating a system that learns over time.

Pillar 3: Policy and platform collaboration

No advertiser can operate in isolation; reducing non‑human ad clicks long‑term requires collaboration with platforms and adherence to industry policies.

Align with platform enforcement

Work with the ad platforms (Google Ads, Meta, DSPs) to:

• Ensure that you recognize invalid traffic as defined by industry standards.
• Share blocked sources with the platform, where possible.
• Ensure you utilize the platform’s toolset for invalid traffic exclusions.

Platform‑specific tools like Google’s invalid traffic reporting are helpful but not sufficient alone, as they often surface data after the fact.

Combine platform reporting with your own detection systems for the best results.

Establish clear acceptable traffic policies

Publishers and advertisers should reach a consensus on what constitutes acceptable traffic. This should include:

• What bot filtering is acceptable for search engine indexing?
• What sources should be disallowed?
• What are the consequences for non-compliance?

Industry intelligence sharing

Fraudsters target multiple advertisers simultaneously. Joining a threat intelligence network or sharing aggregated bot patterns with peers is a great way to improve the overall ecosystem. Examples of where this matters:

• Known proxy pools used for click spam,
• Bot traffic clusters coming out of certain ASN ranges,
• Coordinated invalid traffic patterns targeting specific verticals.

Pillar 4: Education and operational discipline

Organizations are better able to mitigate non-human ad clicks if their internal teams understand the problem and can respond consistently.

Train teams on fraud awareness

• Marketers: Teach marketers how to spot suspicious patterns in performance data.
• Analysts: Teach analysts how to differentiate between bot traffic and actual trends.
• Developers: Teach developers how to integrate fraud detection tools into ad systems.

Standard Operating Procedures (SOPs)

Create SOPs to cover:

• Pre-campaign risk assessments.
• Review cycles to continuously evaluate traffic.
• Actions to take in case of suspected fraud.

Budget guardrails

Create budget guardrails to:

• Set daily budgets with automatic throttling if anomalies are suspected.
• Set thresholds to pause campaigns with too many invalid clicks.
• Set review cycles to evaluate budgets periodically, including suspected fraud metrics in KPIs.

Key metrics for tracking and reducing non-human ad clicks

To monitor the reduction of non-human ad clicks over the long term, the following are the key performance indicators to consider:

Invalid Click Rate (ICR)

The percentage of the total number of clicks that is considered non-human. A decreasing trend in the invalid click rate over time is a good sign of the system’s effectiveness.

Click-Through Rate (CTR) accuracy

A good campaign should have a constant click-through rate. Any unusual spikes in CTR may indicate non-human clicks.

Cost per Conversion stability

Non-human clicks may increase the cost per conversion.

Engagement metrics after the click

Non-human traffic may not engage in the following ways:

• Session duration
• Multiple page views
• Logical conversion paths

Revenue and ROI growth

A reduction in non-human clicks should also lead to the following:

• Improved ROI on the campaign
• Better allocation of the budget
• Better quality of leads and conversions

Case in point: Applying the framework

ClickPatrol client Conservio, a travel company, experienced unusually high ad spend with little return on its paid search and display ad spend.

Before the client utilized the proactive solution, the company experienced inflated click numbers, increasing acquisition costs, and a general lack of correlation between the data and actual behavior of their customers, all of which are telltale signs of non-human ad click activity draining the client’s budget.

After the client utilized the proactive solution from ClickPatrol, the non-human click activity was detected and prevented from affecting the client’s data and metrics.

The client’s non-human click activity decreased by 14% within a short period, and the client saved nearly $2,000 in ad spend.

This example shows the effectiveness of a proactive approach compared to a reactive one for protecting non-human click activity and ad spend.

Reducing non-human ad clicks for long-term advertising success

Non-human ad clicks are a persistent, evolving threat to the performance of digital ads, which negatively impact spend, data, and trust within the digital ad ecosystem.

As non-human clicks become even smarter, a strategic approach is required for long-term protection, including source-level identification, continuous monitoring, collaboration, and operational discipline.

This reduces invalid traffic, ultimately providing greater control over ad campaigns. ClickPatrol offers a solution to this problem: real-time identification and prevention of non-human ad clicks, which can occur even before they affect spend and data.

If you are ready to protect your ad spend, start protecting your traffic with ClickPatrol today!

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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