Click Traffic

The per-click inspector — every ad click with status, IP, location, VPN flag, device, browser, and session signals.

The Click Traffic page is the per-click inspector. Every click on your ads shows up as a row, with the signals ClickFortify used to score it. This is where you investigate flagged clicks, look for patterns, and decide whether a Bad classification was right.

Open the page

In the sidebar, click Click Traffic.

Summary cards

Four cards across the top show the click data for the selected date range. Each shows a percentage change vs. the previous period.

CardMeaning
Total ClicksAll clicks tracked
Normal ClicksClean traffic
Suspicious ClicksFlagged but not blocked
Bad ClicksConfirmed fraud or invalid

Filtering

Two filters above the table:

  • Traffic Type — Ad Clicks, Organic / Direct Traffic, or All Traffic.
  • Status — All, Normal, Bad, Ignored, or Suspicious.

The date range picker in the top-right scopes the whole page.

The table

One row per click. Click any row to expand the full per-click signal detail.

ColumnWhat it shows
TimeRelative ("5 minutes ago") + absolute timestamp
StatusNormal (green), Suspicious (amber), or Bad (red)
CampaignThe Google Ads campaign that delivered the click
Ad Click IDThe Google gclid
Converted✓ or ✗ — whether this click led to a tracked conversion
IP AddressThe source IP with usage classification (Business / Residential / etc.)
LocationCountry and city with flag icon
VPNRed indicator if a VPN or proxy was detected
CrawlerBot detection result — "N/A" if not a known bot, otherwise the bot name
Time SpentHow long the visitor stayed on your site
MachineDevice type — Desktop / Mobile / Tablet
Browser / OSBrowser name + version, operating system
UTM Source / MediumUTM parameters captured from the click

Pagination: 10 rows per page; the footer shows the current range.

A Click Traffic table row expanded to show the full signal detail panel for that click

How to investigate

The most useful workflow:

Filter Status = Bad for the past 7 days.
Look for clusters: same IP repeating, same VPN, same device, very low Time Spent (under 2 seconds).
Expand a row to see the full signal detail — what specifically flagged the click.
If a pattern matches your idea of fraud, add the source to the Blacklist.
Filter Status = Suspicious and repeat — these are the borderline cases. Confidently fraudulent ones move to the Blacklist; clear false positives mean your sensitivity is too high.

Reading individual rows

When you expand a row, look for the combination of signals — not any single one:

  • Short Time Spent + VPN + repeat IP — strong fraud signal.
  • Long Time Spent + converted — almost certainly real.
  • Bad status + converted — investigate. Either it's a false positive (lower sensitivity) or the conversion itself is fake (see Conversions for anomalous-timing flags).
  • No gclid — the click bypassed your tracking template. Check the tracking template configuration.

Suspicious clicks aren't blocked yet — they're monitored. If you see patterns in Suspicious that look like clear fraud, tighten protection sensitivity by one level and watch for a week.

Even Bad clicks aren't blocked retroactively when you're on Warn mode — Warn only adds the bad source to exclusion lists for future clicks. The current bad click in the table still got charged. If you want real-time blocking on detection, switch to Strict mode in Protection settings.

Best practices

  • Check daily. New suspicious activity often shows up overnight.
  • Compare campaigns. Some campaigns attract more fraud than others — Top Threat Sources on the Main Dashboard is the fastest way to spot which.
  • Verify converting clicks. Suspicious clicks that converted are worth a second look — either a false positive worth whitelisting, or a fake conversion worth investigating.
  • Use filters aggressively. Wide views give noise; narrow views give signal.

What happens next

  • Conversions — see which clicks led to outcomes
  • Reports — export the data for analysis or a refund claim
  • Blacklist — manually block patterns you find here

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