By Clixtell Content Team | January 22, 2026

Estimated reading time: 11 minutes

Google Ads Invalid Clicks Refund Evidence Pack for 2026

If you have ever tried to request a Google Ads invalid clicks refund, you already know the hard part is not finding the option. The hard part is proving the problem in a way that a reviewer can verify quickly.

This article is not a repeat of the general refunds guide. It is focused on 1 thing: how to prepare a simple, readable evidence pack that makes your case clear, especially when the issue looks like click fraud or other invalid traffic.

The goal is not to overwhelm support with data. The goal is to show a pattern, show impact, and make it easy to confirm.

What reviewers need to see

Support teams are not trying to read your mind. They need something they can check. That usually means:

1) A clear time window
2) A clear scope (which campaigns, which locations, which networks)
3) A pattern that repeats, not a single strange click
4) Evidence that the visits did not behave like normal users

Many requests fail because they are written like a complaint instead of a case file. A reviewer cannot validate “these clicks feel fake.” They can validate “these clusters happened in a short window, in the same segments, and each session ended in a few seconds with no interaction.”

Think of it like this. You are not trying to prove that 1 click was invalid. You are trying to prove that a group of clicks shares the same suspicious behavior and does not match typical user activity.

What an evidence pack is

An evidence pack is a small set of files and notes that support can review fast. It should fit on 1 page for the summary, plus attachments that show the proof.

A good evidence pack includes:

• A 1 page summary in plain language
• A table of clusters (time window, campaign, segment, count, cost)
• Supporting exports (Google Ads data, website behavior, and any session proof)

A weak evidence pack includes:

• Screenshots without dates
• Hundreds of rows with no explanation
• A long email with no structure
• Claims without behavioral proof

Keep it simple. Reviewers are human. If the pack is clean, the review is easier.

What to collect from Google Ads

Start with Google Ads data because it anchors the request. Your goal is to show where and when the suspicious activity happened.

You do not need every report. You need the minimum reports that explain the pattern.

Collect these items:

1) Campaign and ad group performance for the incident window
2) Segment by hour (or smaller window if you have it)
3) Geographic segment that highlights anomalies
4) Device segment if the issue is skewed to 1 device type
5) Search terms or placements if a specific source is involved

What you want from these exports is not volume. It is contrast. You are trying to show that something changed.

Examples of contrast that reads well:

• “From 10:00 to 13:00, clicks were 4x higher than the same hours the day before.”
• “The spike was limited to 1 campaign and 1 location.”
• “The spike created cost with near 0 engagement and no qualified conversions.”

Use plain numbers. Use clear times. Keep your time zone consistent in the pack.

For refunds basics and where to see credits, refer to the existing guide instead of repeating it here: complete guide to Google Ads refunds.

What to collect from your website

Google Ads data shows the what. Website behavior shows the why this looks invalid.

Your website proof should answer 2 questions:

• Did the visits behave like real people?
• Is the behavior consistent across many clicks?

Useful website signals include:

• Very short session duration in clusters
• 1 page sessions at scale with no interaction
• 0 scroll or near 0 engagement events
• Repeated landings on the same page with identical behavior
• Abnormal bounce rate changes isolated to the incident window

Avoid turning this into an analytics tutorial. You can use any analytics system, including GA4, server logs, or a tag based system. What matters is that you can show a consistent pattern.

A simple way to present website behavior:

• “In the 2 hour window, 312 sessions arrived from paid traffic. 280 ended in under 3 seconds and had no scroll or interaction events.”
• “Normal paid sessions that day averaged 55 seconds with at least 1 interaction event.”

If you can, include 1 screenshot or export that shows the change by hour. 1 clear image often explains more than a paragraph.

Third party signals and session proof

This is where many cases become easier to review.

If you use a 3rd party traffic quality tool such as Clixtell, include only the parts that help validate patterns. Do not attach everything.

Examples of useful artifacts:

• Session recordings that show no interaction, fast exits, or repeated behavior
• Repeated click behavior across short time windows
• Indicators like VPN or proxy usage when it is consistent across a cluster
• Repeated provider or network patterns that match the same suspicious group

Keep the tone neutral. Do not claim “this is definitely fraud.” Use language like “these sessions show repeated behavior inconsistent with typical users.”

If you want a short internal reference for session level proof: session recordings for click fraud evidence.

If you want a short internal reference to avoid mixing up fraud and low intent traffic: click fraud vs bad traffic.

How to match clicks to evidence

Correlation is where most evidence packs break. The data exists, but it is not tied together.

The safest approach is to match by:

• Time window (same day, same hour range)
• Campaign and landing page (if known)
• Location segment (at least country and region)
• Device category (if the anomaly is device specific)

You rarely need to match each click to each session 1 by 1. That is slow and fragile. Instead, build clusters.

A cluster is a group of clicks that share:

• A short time window
• The same campaign or ad group
• The same or similar location signal
• The same behavior after the click

In your pack, create a small table like this:

Cluster A: 10:05 to 10:40, Campaign X, Location Y, 68 clicks, $124 cost, 62 sessions under 3 seconds, 0 interactions
Cluster B: 11:10 to 11:55, Campaign X, Location Y, 74 clicks, $139 cost, 70 sessions under 3 seconds, 0 interactions

This is easier to review than a large export. It also makes your request sound measured and fair.

If your case is based on location mismatch, keep it simple. Do not argue about which geolocation method is perfect. Instead, show that the visits behave like automation or repeated low intent behavior and that the pattern is concentrated.

Patterns that are easy to verify

The following patterns tend to be easier for support teams to verify, because they are visible as repeated signals.

1) Short time bursts
A sharp spike over 20 to 90 minutes, limited to a few campaigns.

2) No meaningful post click activity
Large groups of sessions with no scroll, no page depth, no events.

3) Repeat behavior on 1 page
Many paid sessions landing on 1 page and exiting in the same way.

4) Segment isolation
The anomaly is concentrated in 1 location, 1 device type, or 1 campaign.

5) Cost without normal intent signals
A jump in spend with a drop in qualified actions, especially if you can show how “qualified” is defined in your business.

6) Repeated visit structure
Similar session length, similar navigation path, similar lack of interaction across a cluster.

If you only have 1 of these patterns, your pack can still work. If you have 3, it is usually much easier.

How to package the evidence

Packaging is not about design. It is about clarity.

Use a simple folder structure:

01-summary.pdf
02-google-ads-export.csv
03-website-behavior-export.csv
04-session-proof.zip (only if needed)

A short request template for manual review

Subject:
Request for manual review of suspected invalid clicks (Date range, Account ID)

Hello Google Ads Support,

I am requesting a manual review for suspected invalid clicks in this account: [Account ID].

Incident window (time zone: [TZ]): [Start date and time] to [End date and time].
Campaigns impacted: [Campaign names].

Summary of patterns observed:
• Cluster A: [time window], [campaign], [click count], estimated cost [amount], repeated sessions with no interaction.
• Cluster B: [time window], [campaign], [click count], estimated cost [amount], repeated sessions with no interaction.

Attached evidence pack includes:
1) 1 page summary with cluster table
2) Google Ads export for the incident window
3) Website behavior export showing repeated short sessions and no interactions
4) Optional session proof samples for representative sessions

Please confirm receipt and advise if any additional details are required for the review.

Thank you,
[Name]

What to do after a denial

Sometimes, after contacting Google Ads Support, you may be told that invalid clicks are already filtered and credited automatically under “Invalid clicks.” The challenge is that these automatic credits can cover only a portion of the suspicious activity you are seeing in your own data. If your internal evidence suggests a larger impact than the credited amount, treat the credit as a baseline, not a full resolution. Document the gap with a clear evidence pack, focus on the strongest clusters, and submit a narrow time window request for manual review.

Prevention that stays conservative

Examples of conservative prevention steps:

• Monitor spikes by hour and investigate early
• Use exclusions carefully when a pattern is consistent
• Prefer blocking repeat sources or ranges over blocking single 1 off events
• Review search terms and placements that correlate to poor behavior

FAQ

What evidence increases the chance of an invalid clicks refund?

Evidence that shows repeated patterns in a short time window is usually easier to review. A 1 page cluster summary, a Google Ads export for the incident window, and clear website behavior proof often works better than large screenshot collections.

Why do invalid clicks refund requests get denied?

Common reasons include a time window that is too broad, missing post click behavior proof, and requests that do not show a repeatable pattern. Narrowing the scope and adding a cluster table can help.

How should I package evidence for a manual review?

Use a short summary that points to specific clusters, plus minimal supporting exports. Keep attachments small and label them clearly so a reviewer can find the key proof quickly.

Related reading on refunds basics: complete guide to Google Ads refunds.