Phishing Simulation Metrics Beyond Click Rate

Click rate is the metric everybody quotes, and it is the metric most likely to mislead you.

If you run phishing simulations long enough, you start seeing it. A great campaign with a low click rate that still produces zero reports. A bad campaign with a spike in clicks that turns out to be security scanners detonating links, auto-previews fetching URLs, or a mobile client helpfully rendering a page in the background. A quarter of steady improvement that disappears the moment you change the lure style. A team that learns how to pass the test, not how to stop the attack.

Brand protection and security teams don’t have time for vanity metrics. When your brand is being impersonated, attackers aren’t grading you on clicks. They are measuring whether they can move someone from a believable message to a credential capture, a helpdesk reset, a wire, a gift card, or a customer trust incident.

So if clicks are still your headline number, here is the hard truth. Click rate is a noisy proxy for behavior and is easy to game, misread, or inflate through automation. It can be useful as a supporting signal, but it isn’t a readiness score on its own.

This article lays out a measurement framework for phishing simulation metrics that better map to risk reduction for security and brand protection teams. It covers how to reduce false positives, separate machine traffic from human behavior, and normalize results with difficulty scoring (including the NIST Phish Scale) so trends remain meaningful as lures become more realistic.

Summary

Click rate is easy to distort. Link scanners, safe-link rewrites, preview fetching, and training to the test can swing clicks without changing real-world risk. A stronger set of phishing simulation metrics centers on behaviors that reduce damage: correct report rate, time-to-report, repeat susceptibility (weighted by severity), and performance in high-risk cohorts. Pair those with false-positive controls and difficulty scoring, so you can compare campaigns over time without rewarding easy lures or punishing realistic ones.

Why Click Rate Misleads

Click rate lies because a click is no longer a single human decision. It is an event your tooling observes through layers of email security, browser protections, mobile clients, link wrappers, and automated scanners. That stack generates traffic that can appear to mimic user behavior even when no one did anything. Even when the click is real, it may not indicate meaningful susceptibility. Many employees interact to inspect. Hovering alone typically shouldn't register as a click, but previews, link rewrites, auto-loading, and security tooling can still create click-like events that appear human in reporting. If you treat every click as a failure, you will overstate risk, misrank teams, and optimize your program toward cosmetic improvements instead of safer behavior.

Here are the usual culprits.

Scanner and Automation Activity

Many organizations use link-scanning and detonation technologies that automatically fetch URLs. Some do it at delivery, others at click, others at both. Some email clients also prefetch content. If your simulation records a click when an automated system requests the link, your reported click rate can inflate even if no user ever touches it.

Preview Behavior and Accidental Interaction

Mobile clients can register unintentional interactions. A fat-finger tap while scrolling. A preview pane loading a URL. A safety banner that rewrites links. A safe link wrapper that changes how your tracking works. If you don’t normalize these behaviors, you end up comparing different client behaviors (mobile vs desktop, different mail apps, different security stacks) as if they were the same human decision.

Habituation and Test Literacy

Teams learn the patterns of your tests. The same templates. The same internal sender style. The same cadence. People stop reading email, and instead look for the tells that indicate “this is probably a simulation.” That drives click rate down while real-world resilience stays flat.

Gotcha Culture Breaks the Signal

If employees feel punished, shamed, or tricked for sport, you will reduce reporting. You will also teach people to hide mistakes. That is the opposite of what you need in a real incident. A program that optimizes for embarrassment will optimize against early detection.

What Should You Measure Instead of Clicks?

You should measure what you want to happen in a real phishing event. That means shifting from “did someone interact with the lure” to “did the organization detect it early, route it correctly, and reduce the chance of repeat compromise.” Strong phishing simulation metrics reward actions that help defenders and protect the business. Reporting is the most obvious, but it isn’t enough on its own. You also need speed, consistency, and severity-aware measurement. Otherwise, you end up celebrating lower clicks while credential entry, helpdesk bypasses, or workflow violations quietly stay the same.

A strong metric set usually includes:

Report rate
Time-to-report
Repeat susceptibility and trendline by person and cohort
High-risk cohort performance
Escalation quality and workflow adherence
Real incident outcomes tied to reporting and response

Severity-Weighted Critical-Action Rate

Track actions by severity, not as a single fail. Clicking isn’t the same as entering credentials, and credential entry isn’t the same as sharing an OTP or bypassing an identity check. A severity-weighted metric lets you show real progress even when you intentionally run more realistic lures that might increase low-severity interactions.

What Is a Good Report Rate, and How Should You Interpret It?

A good report rate climbs steadily as you increase realism. That’s the key. If report rate only looks good when lures are obvious, you haven’t built durable detection behavior. Report rate should also be interpreted alongside noise. A program that drives tons of reports but overwhelms triage isn’t succeeding. It is shifting the burden. The best report-rate trends are paired with stable or improving report quality. More correct reports, fewer low-signal “everything is phishing” submissions, and faster routing into the right queue so response teams can act.

To make report rate meaningful:

Define what counts as a report. Reported via email client button. Forwarded to an abuse inbox. Ticketed through the right channel. Whatever your process is, lock the definition and document it.
Separate “correct reports” from “noise reports.” If you only track raw report volume, you can create a spam cannon that burns out your SOC.
Track report rate by channel. Email, SMS, chat apps, voice workflows. Modern social engineering does not stop at the inbox.

How Fast Should Employees Report a Suspected Phish?

Time-to-report matters because speed turns a suspicious email into an incident we can contain early. Most organizations don’t fail because nobody reports anything. They fail because reporting is slow, inconsistent, or routed into a dead-end mailbox. A strong time-to-report metric captures both human recognition and workflow design. If you make reporting frictionless and you reinforce that reporting is valued, time-to-report usually drops fast, especially among people who see high volumes of external messages. That is a meaningful win because early reports give defenders a head start on blocking sender infrastructure, searching mailboxes, and warning targeted teams.

Time-to-report captures:

Whether employees recognize the cues early
Whether the reporting path is frictionless
Whether people trust the process enough to use it
Whether your internal coordination works under mild pressure

Practical ways to track it:

Median time-to-report, not just average. A few very slow reports can skew averages.
Percent reported within thresholds. For example, within 5, 15, or 60 minutes.
Time-to-report by location and device type. Mobile friction is real.

If your report flow requires three clicks, a login, and a form, your metric is grading your UX, not your humans.

How Do You Measure Repeat Susceptibility without Creating a Shame List?

Repeat susceptibility is the most honest metric you can track, and it is also the easiest to misuse. The goal is to identify where targeted coaching, guardrails, or workflow changes reduce risk.

Use it like this:

Track repeat interaction rate over rolling windows—for example, two or more risky interactions in 90 days.
Separate risky interaction types. Clicking a link is different from entering credentials. Entering credentials is different from initiating money movement.
Look for role patterns. Certain jobs get hammered with specific lures. Finance and payroll. HR. IT helpdesk. Sales. Exec admins.

If the same cohort repeats, treat it as a design problem, not a character flaw. Are they overloaded? Are they trained on outdated examples? Are attackers targeting their workflows more intensely?

For brand protection teams, repeat susceptibility also serves as a bridge between internal readiness and external threat reality. When you detect an impersonation campaign targeting customers, you can model similar lures internally and see whether the same weaknesses exist.

Which Cohorts Are High-Risk?

High-risk cohorts aren’t just privileged users. They are the people whose everyday workflows intersect with money movement, identity verification, and exceptions. That includes finance, payroll, and AP. It includes IT helpdesk staff who can reset MFA or approve access changes. It includes customer support teams who can override safeguards or validate identity under pressure. It includes executive assistants who act as trusted proxies. These groups are targeted differently, so they shouldn’t be judged by the same generic baseline as the rest of the organization. If you don’t break out their performance, your overall averages will hide the outcomes you actually need to improve.

Typical high-risk cohorts include:

Finance, payroll, AP, treasury
IT helpdesk, identity admins, endpoint admins
Executives and exec assistants
Customer support agents handling refunds, account access, or verification
Anyone approving vendors, invoices, or wire changes
Anyone with privileged access or broad data access

Your program should report results for these groups separately, even if you also maintain an overall dashboard. If your overall click rate drops while your helpdesk cohort stays flat, your real risk is that you won't improve.

What Metrics Matter Most for High-Risk Cohorts?

The strongest metrics for high-risk cohorts are correct report rate, time-to-report, and critical-action rates, such as credential submission, MFA/OTP sharing, and workflow violations (for example, approving a vendor change without out-of-band verification).

Clicks are weak here because the business impact usually happens after the click. The goal is to measure whether the cohort detects fast, escalates correctly, and avoids the irreversible actions that attackers need.

How Do You Tie Simulations to Real Incident Outcomes?

If simulations never align with real outcomes, the program becomes a quarterly ritual rather than a risk-reduction engine. The point is to shorten detection time, improve escalation accuracy, and reduce successful compromise in the scenarios you see in the wild. You can tie simulations to outcomes without perfect attribution. Track whether reports create actionable triage events. Track whether the security team responds faster when reports are high-quality. Track whether specific workflow failures decline over time. For brand protection teams, the connection can be even tighter. Simulation themes can mirror real impersonation tactics your organization is seeing externally, so you’re measuring readiness against current threats rather than generic templates.

Start with two questions.

Did simulated reporting produce faster defensive action?
Did that translate to fewer real incidents, lower loss, or reduced dwell time?

You can build a lightweight outcome model without creating a giant analytics project.

Provide a Metrics Framework That Maps to Outcomes

Behavior Metrics

Report rate (correct, channel-specific)
Time-to-report (median and threshold-based)
Escalation quality (did it go to the right place, with the right context)
Repeat susceptibility (by action severity)

Control Metrics

Did email security controls flag the lure?
Did identity controls stop credential use?
Did browser protections block the destination?
Did endpoint controls stop payload execution, if relevant?

Outcome Metrics

Reduction in successful real-world phishing incidents tied to similar lures
Faster containment times for real incidents
Reduced fraud loss or fewer account takeovers
Reduced customer impact from brand impersonation flows when you also run external monitoring.

If you’re already monitoring external phishing infrastructure, you can use live campaign patterns to inform simulations, then see whether internal metrics align with those themes. That creates a tight loop between threat reality and human readiness.

How to Reduce False Positives in Simulation Metrics

False positives are where many simulation programs quietly lose credibility. If people are blamed for clicks that were actually scanners, or if a campaign fails because the tracking is distorted by link rewriting, the metrics stop being trusted. Trust matters because you need employees to report honestly, and you need leadership to fund the program based on signal, not noise. Reducing false positives is both a technical and a program design problem. You need telemetry that can distinguish machine behavior from human behavior, and you need definitions that don’t shift from campaign to campaign. When you get this right, your trendlines become defensible. That makes the rest of the framework worth implementing.

Control Scanner and Prefetch Noise

Use unique per-user tokens, but also detect and label known scanner user agents and IP ranges.
Separate “link requested” from “link interacted.” Treat a real click as an event that includes a human-like browser signature plus a follow-on interaction, not just a GET request.
Consider credential entry as the stronger event for certain simulations. If your aim is to test susceptibility, a form submission is harder for scanners to fake than a link fetch.

Normalize by Device and Client Type

If half your org is on mobile and half on desktop, your click and report patterns will differ. Your metrics should reflect that reality instead of averaging it away.

Fix the Reporting UX

If your employees cannot report in under 10 seconds, your time-to-report is measuring friction.

Put the report mechanism where people already work.
Make the confirmation clear so people trust it worked.
Provide fast feedback loops so reporting feels useful, not performative.

How to Score Difficulty across Campaigns

You score difficulty, so your results aren’t just a reflection of how tricky your latest template was. Difficulty scoring lets you compare performance across quarters, teams, and lure types with less self-deception.

This is where frameworks like the NIST Phish Scale help. It rates human phishing detection difficulty by scoring observable cues and premise alignment (how well the lure matches the recipient’s context), then mapping that to a difficulty rating. In plain terms, it helps you label whether a simulation was easy or hard based on properties that actually influence human judgment.

What Does Difficulty Scoring Fix?

It reduces the temptation to celebrate lower click rates driven by easier lures.
It helps you explain why a worse month represents progress if the simulations got harder.
It supports trend analysis that stays meaningful when your scenarios evolve.

How to Use NIST Phish Scale in Practice

You don’t need to turn this into a dissertation.

Score each simulation template before launch.
Keep the scoring consistent—same rubric, same training for whoever scores it.
Report results as “performance by difficulty band.” Easy, moderate, hard. Or by numerical score ranges if you want more precision.
Track improvement within each band. That is your real trendline.

If you want the program to feel current, difficulty scoring also encourages you to keep up with attacker realism. When you run more realistic lures, your raw click rate might rise. Your difficulty-normalized performance can still improve, and that is the story your leadership actually needs to hear.

What a Better Metrics Dashboard Includes

A better metrics dashboard shows detection and response behaviors first, then susceptibility, then click data as supporting context.

Here is a practical structure that works.

Detection and Reporting Panel

Correct report rate
Time-to-report (median and percent within thresholds)
Report channel breakdown
False report rate, with trendline

Susceptibility and Risk Panel

Repeat susceptibility rate (rolling window)
Severity-weighted action rate (click vs credential entry vs OTP vs workflow violation)
High-risk cohort performance, isolated from the overall average

Normalization Panel

Difficulty score distribution by campaign
Performance by difficulty band
Device and region splits

Operational Outcomes Panel

Simulation-driven tickets created and resolved
Time from report to triage
Time from triage to containment action
Correlation with real incident trends, when available

If your current reporting stack cannot produce this, that is usually not a people problem. It is a telemetry and workflow problem.

How Do You Prevent Metrics from Becoming a Gotcha Program?

You prevent gotcha culture by aligning metrics with learning and response, not punishment. That means no public leaderboards, no shaming, and no incentives that encourage hiding mistakes.

Practical guardrails:

Report at the team or cohort level for broad audiences. Keep individual-level data limited to coaching workflows.
Celebrate reporting and fast escalation more than perfect avoidance.
Use simulations to test processes, too. Does the helpdesk follow identity verification steps? Does finance verify out of band? Does security triage correctly?

Security awareness only works when people trust the system they are part of.

Key Takeaways

Click rate is noisy. Scanner traffic, client behavior, and test literacy can distort it.
Report rate and time-to-report are stronger indicators of real-world readiness.
Repeat susceptibility and high-risk cohort metrics reveal where risk actually lives.
Reduce false positives by separating machine activity from human activity and addressing reporting friction.
Use difficulty scoring, including the NIST Phish Scale concept, to normalize results and track true progress over time.

Replace Vanity Metrics with Readiness Metrics

If clicks still dominate your phishing simulation program, you are probably leaving risk insights on the table. Shift measurement toward reporting, speed, severity-weighted actions, and high-risk cohort performance. Normalize by difficulty, then use the results to fix workflows and controls, not just training content.

If you want to pressure-test realistic social engineering flows and measure outcomes that map to risk reduction, Doppel Simulation is built for that kind of threat-informed loop.