Why are email click rates inflated and how to solve the issue?
Published 20 May 2025
Updated 7 Oct 2026
12 min read
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Updated on 7 Oct 2026: We updated our guidance on bot click filtering, website attribution, and safe unsubscribe handling.
Email click rates are inflated when automated systems request links before or after the human recipient reads the email. These requests are often called bot clicks or non-human interactions (NHI). The usual cause is security scanning by mailbox providers, corporate gateways, link protection systems, and preview services. Investigate any sudden jump in click rate by mailbox provider and recipient domain before treating it as improved engagement. Corporate, government, and education inboxes also warrant attention because their security gateways scan links.
The fix is not one setting. Separate human clicks from non-human interactions, stop raw clicks from driving automations, report affected mailbox providers separately, and keep authentication and reputation signals clean. DMARC, SPF, DKIM, and DNS settings do not stop link scanning. They establish an authentication baseline so a click-reporting problem is not confused with a separate sender configuration problem.
- Security scanners and mailbox protection systems fetch links to assess risk.
- Group click events by mailbox provider, timestamp, URL count, and bot flag for a fast first test.
- Report filtered unique clicks instead of raw total clicks, and protect automations from bot-triggered events.
- Maintain clean authentication, stable sending patterns, consistent reporting baselines, and separate bot diagnostics over time.
Why click rates inflate
A tracked click in an email service provider (ESP) starts with a request to a tracking URL. Reports can exclude requests classified as automated. It does not always mean a person made a choice. Security systems request links to inspect redirects, check landing pages, classify URLs, detonate suspicious content in a sandbox, or update a risk score. Scanning can happen during mail flow, before delivery, or when a recipient clicks. The tracking system can record an automated request as a click. Removing invalid addresses reduces bounces but does not stop scanning of mail sent to valid recipients.
This is why the issue often appears as a sudden spike. One mailbox provider changes link scanning behavior, one security layer becomes more aggressive, or one sender gets additional inspection for a period of time. The sender sees a click rate that looks excellent, but the downstream behavior does not match it. Site sessions, purchases, replies, and form fills stay flat.
Treat raw clicks as noisy
Raw click rate is a useful directional metric when the data is stable. During a bot-click spike, it is contaminated. Do not use it for content decisions, segment scoring, win-back eligibility, or campaign winner selection until the click stream has been filtered.
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Provider or domain spike | One mailbox provider or corporate domain drives most of the lift | Report the affected group separately |
Instant clicks | Events appear before or seconds after recorded delivery | Combine timing with other signals |
All-link activity | One recipient requests many URLs | Combine link breadth with timing and bot flags |
High clicks | Sessions and conversions remain flat | Use conversion-backed reporting |
Action-link requests | A seed inbox triggers an action without consent | Protect the endpoint |
Common click inflation patterns and first actions.
Inbox Collective's bot click analysis documents automated activity in newsletter reports, including corporate, government, and education inboxes. A recorded request establishes that a URL was fetched, not that a person chose to visit it.
Choose the right click metric
Before comparing campaigns, confirm what the report calls a click. Total clicks count recorded link requests after any configured filters, so repeated requests by an undetected scanner can inflate them sharply. Unique clicks usually count each recipient once, which limits repeat inflation but still counts a recipient whose only activity came from a scanner. Email click-through rate (CTR) commonly means unique clickers divided by delivered messages, multiplied by 100. Some reports use sent messages instead, so check the denominator and bot-filter setting before comparing results.
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Total clicks | Tracked link requests after any configured filters | Diagnostics and link-level activity |
Unique click rate (CTR) | (Unique recipients with a qualifying click / delivered messages) x 100 | Campaign comparison after bot filtering |
Click-to-open rate (CTOR) | (Unique clickers / unique openers) x 100 | Secondary content analysis only |
Conversion-backed click rate (custom metric) | (Unique filtered clickers with a verified downstream action / delivered messages) x 100 | Check whether click engagement leads to verified outcomes |
Use the same filtered definition across campaigns.
CTOR has two noisy inputs
Click-to-open rate (CTOR) can be distorted by automated clicks in the numerator and privacy-driven or prefetched opens in the denominator. Use filtered unique click rate against delivered email as the main campaign trend, then validate important decisions with website or conversion data.
How to identify likely bot clicks
Start by looking for concentration. If a campaign suddenly moves from a 2% click rate to a 35% click rate, and the jump sits mostly inside one mailbox provider or corporate domain, investigate that group before comparing the campaign with prior sends. If many links in the email are requested within a narrow window, that is stronger evidence.
The best data fields are event timestamp, recipient domain, mailbox provider, URL, user agent, IP, bot classification, delivery timestamp, and later web session behavior. Some ESPs expose only part of this. If IP and user agent are hidden, use the fields you have: provider concentration, event timing, number of distinct links, and whether the clicks create normal web sessions. No single signal proves bot activity. Recipient domains and MX hosts do not reveal every security gateway, and shared IP addresses or ordinary browser user agents also occur in human traffic. Avoid excluding a whole domain or IP based on one clue.
Bot click detection workflow for grouping email clicks and filtering automated link scans.
- Clicks before or within seconds of recorded delivery, especially across several URLs, point to scanning.
- One mailbox family driving most of the lift narrows the investigation but does not identify the scanner.
- A human rarely clicks every tracked link, social icon, footer link, and unsubscribe path.
- Clicks without matching site sessions or conversions need investigation, not automatic bot classification.
Example event filter logicSQL
-- Precompute unique_links_clicked_in_30s for each event: -- distinct URLs for the same recipient and campaign within -- 15 seconds before and 15 seconds after that event. SELECT event_id, recipient_domain, url, clicked_at, CASE WHEN bot_click IS TRUE THEN 'bot' WHEN ABS(EXTRACT(EPOCH FROM (clicked_at - delivered_at))) <= 30 AND unique_links_clicked_in_30s >= 2 THEN 'probable_bot' WHEN unique_links_clicked_in_30s >= 5 THEN 'probable_bot' ELSE 'unclassified' END AS click_class FROM email_click_events WHERE campaign_id = 'campaign_123';
This PostgreSQL example assumes a precomputed count of distinct links for the same recipient and campaign within a 30-second window centered on each event. It includes scans just before recorded delivery and leaves unmatched events unclassified. The thresholds are illustrative, not validated cutoffs. A fast click alone can still be human, so combine timing with link breadth, bot flags, and downstream behavior. Audit the rules against known test clicks before using them in automation.
There is no universal safe percentage of automated clicks. Compare each provider and campaign with its own baseline, and investigate whenever suspected scanning changes a campaign winner, engagement segment, or attribution result.
How to solve the reporting issue
The cleanest fix is to create a reporting layer that separates raw clicks, filtered unique clicks, and conversion-backed clicks. Raw clicks still have diagnostic value. Filtered unique clicks are better for campaign comparison. Compare verified conversions alongside filtered clicks for revenue and audience decisions.
Raw click reporting
- Dashboard defaults vary; check whether bot filtering is enabled.
- Security scans and link previews inflate the number.
- A single scan can move contacts into the wrong segment.
- Raw clicks can select a campaign winner based on scanner behavior.
Filtered click reporting
- Bot flags, timing, link breadth, and provider behavior reduce false lift.
- Engagement groups use corroborated activity.
- Lead and intent models avoid scan-only activity.
- Filtering rules need audits when mailbox behavior changes.
If the ESP has a bot-click flag, use it in segments and reports. A common rule is to count click events only where the bot flag is false. False means the platform did not classify that event as a bot, rather than independently proving human intent; missing flags need review. If the flag is per event, a scanner event and a later human event can coexist for the same person. Keep the later qualifying event and ignore the scanner event.
Check the filter separately in dashboards, exports, attribution settings, segments, and automation triggers. A reporting toggle does not guarantee that every surface or trigger excludes bots. Confirm which historical events are reprocessed, record when filtering changed, and compare campaigns using the same settings. A lower rate after filtering is enabled does not by itself show declining engagement. Keep filtering settings stable during an A/B test.
If the ESP does not expose the right fields, build a simple export process. Pull campaign click events, group by provider and domain, then apply timing and URL-count rules. Compare filtered click events with campaign-tagged website sessions and confirmed conversions; a missing session alone does not prove scanning.
A dedicated HTTPS click domain helps, but it is not a cure
Dedicated click tracking with HTTPS makes the tracking domain consistent with the brand and easier to separate in reporting. It does not stop Microsoft or corporate gateways from checking links. Treat it as a domain hygiene and attribution control, not as bot-click prevention.
- Keep raw clicks, filtered unique clicks, and conversion-backed clicks in separate columns.
- Exclude bot-flagged events from engaged, VIP, win-back, and suppression logic.
- Report the affected mailbox providers and recipient domains separately during abnormal spikes.
- Do not let one raw click trigger high-impact flows without another signal.
- Compare filtered click trends with sessions, revenue, replies, and form submits.
Send a test message to Suped's email tester to inspect authentication and message configuration. Separately, use seed accounts at the affected providers to compare tracked requests with deliberate human clicks.
Email tester
Send a real email to this address. Suped shows a results button when the test is ready.
?/43tests passed
Protect action links as well as reports. An ordinary GET request should not change preferences, approve a proof, notify sales, or perform another sensitive action because a scanner can fetch it. Require a confirming POST, with authentication where appropriate. For RFC 8058 one-click unsubscribe, keep the HTTPS List-Unsubscribe URL and List-Unsubscribe-Post header covered by a valid DKIM signature. Process its consent-driven POST without requiring a login or confirmation page, and do not apply engagement filters to a valid unsubscribe request.
Validate clicks with UTM-tagged website activity
Add UTM parameters to destination URLs so website activity can be compared with the same email campaign. Use consistent utm_source, utm_medium=email, and utm_campaign values, plus utm_content to distinguish individual links. Confirm that redirects preserve the tags, and keep email addresses or other personal data out of them.
Example campaign parameters for an email linktext
utm_source=newsletter&utm_medium=email&utm_campaign=october_campaign&utm_content=primary_cta
UTM tags identify a campaign, not whether a visitor is human. Basic scanners often do not load analytics JavaScript, but browser-based scanners can execute it and create page views. Consent settings, blockers, or a failed page load can also prevent a real visit from appearing in analytics.
Compare filtered unique clicks with tagged sessions and confirmed purchases or validated form submissions using a consistent attribution window. A bot click followed by an unrelated purchase must not claim credit just because it was the last recorded click. Filter automated touchpoints before assigning campaign revenue, and expect click counts and session counts to differ.
Keep authentication separate from click diagnosis
Email authentication does not tell a mailbox provider, "do not scan my links." Scanning still happens on authenticated mail. SPF and DKIM authenticate sending identifiers. DMARC passes when at least one passes with a domain that matches the visible From domain under DMARC identifier alignment rules. Authentication results do not prove whether a click came from a human. Broken authentication, unfamiliar sending sources, strange redirect domains, and blocklist (blacklist) problems should be investigated as separate deliverability issues.
Check the authentication baseline alongside the reporting investigation: confirm DMARC alignment, validate SPF and DKIM, inventory sending sources, and check for a blocklist (blacklist) listing. A domain health checker provides a first check, then DMARC monitoring helps reconcile observed sending sources with your authorized inventory.
Issues page showing top issues, verified sources, unverified sources, and authentication pass rates
Suped is our DMARC and email authentication platform. Use Suped's aggregate reports to review authentication results and reconcile sending sources with your authorized inventory, and use blocklist monitoring to investigate separate blacklist problems. Keep click classification in the ESP and web analytics, while using Suped to check the domain authentication baseline.
Separate the two investigations
Use click-event data to classify human and automated activity. Use DMARC reports, SPF, DKIM, DNS, source inventory, and blocklist (blacklist) checks to diagnose authentication and reputation. A clean result in one area does not settle the other.
First steps after a click rate spike
When click rates inflate overnight, do not rewrite the content strategy. Freeze decisions that rely on click rate, find the affected mailbox group, and build a clean metric before making marketing changes. The goal is to stop bad data from spreading into segments, tests, automations, and revenue attribution.
- Do not choose winners, suppress contacts, or score leads from raw click spikes.
- Compare mailbox providers and recipient domains, including corporate, government, and education domains.
- If the ESP exposes a bot flag, exclude true values from engagement segments.
- Classify fast, many-link activity as probable non-human activity only when more than one signal supports it.
- Verify authentication, DNS, sending sources, and blocklist (blacklist) status separately.
For a deeper operational playbook, the related guide on bot click filtering covers newsletter reporting, and the guide on artificial opens explains why open rate and click rate often break together.
Do not hide diagnostic links in production
Invisible or honeypot links can identify scanners in controlled tests, but they create deliverability and trust risks when used carelessly. Keep them for diagnostics, document the rule, and never treat the result as the only proof of bot activity.
