Email Marketing Statistics at a Glance
The six figures below are drawn from primary platform reports. They are starting points, not universal averages — each comes from a specific platform's user base with its own industry mix, sender types, and measurement approach.
These platforms have different user bases, industry mixes, and measurement methodologies. You cannot average Mailchimp and MailerLite figures to produce a "global average." Treat each benchmark as describing its source platform's population only.
How We Verified These Statistics
Every statistic on this page went through the same process: we identified an original source (a primary platform report, published industry study, or documented survey), recorded the data year separately from the publication year, noted the study population and sample size, and flagged any important measurement limitations. Statistics that could not be traced to an original source were excluded.
- Exact figure and unit — no rounded estimates presented as precise measurements
- Original source name and URL — not a secondary roundup article
- Data year versus publication year — separated, not merged into one date
- Population and sample scope — whose campaigns, which industries, which geography
- Metric definition — what goes in the numerator and denominator
- Key caveat — what limits how broadly the figure applies
Email Open Rate Statistics
The tracked open rate is the most widely cited email metric and the most distorted by privacy technology changes. Before comparing figures across sources or time periods, it helps to understand what the metric actually counts.
Unique Tracked Opens = distinct recipient addresses that triggered a pixel load
Delivered Emails = sent emails minus hard bounces
The denominator definition matters. Some platforms use emails sent, others use emails delivered (excluding bounces). Two reports with the same raw open count can produce different open-rate percentages if they use different denominators. Always check the source's methodology notes.
Average Open Rates by Provider
Mailchimp's published email benchmark table reports a 35.63% average open rate across its benchmarked user base. This figure represents the mean open rate calculated from campaigns sent by Mailchimp users across all reported industry categories. It is a platform-specific benchmark, not a census of all email senders globally.
MailerLite's 2025 benchmark study reports an average open rate of 40.22%. This study analyzed more than 3.6 million campaigns from approximately 181,000 approved MailerLite accounts observed between December 2024 and November 2025. The study uses a median-based calculation approach for some metrics, so figures may not be directly comparable to Mailchimp's mean-based benchmarks.
Why Open Rates Can Be Misleading
Tracked opens are not the same as confirmed human reading
Apple Mail Privacy Protection (MPP), introduced with iOS 15, allows Apple devices to pre-load remote email content — including tracking pixels — before a recipient consciously opens a message. When this happens, the sender's platform records an "open" even though no human actually viewed the email. The effect varies by how much of a sender's list uses Apple Mail clients. Senders with audiences that heavily use Apple Mail are most affected. Open rate trends before and after MPP adoption are especially risky to compare. Click rates and downstream conversions are more reliable signals of actual engagement.
Email Open Rates by Industry
The table below uses Mailchimp's published industry breakdown. Because the figures come from a single source, the industry-to-industry comparisons are internally consistent — though they still describe Mailchimp's user population, not the entire email industry. Figures should not be mixed with industry rates from different providers.
| Industry | Open Rate | Click Rate | Unsubscribe Rate | Data Source |
|---|---|---|---|---|
| All Industries (avg.) | 35.63% | 2.62% | 0.26% | Mailchimp |
| Government | 33.71% | 3.87% | 0.13% | Mailchimp |
| Education | 34.76% | 2.59% | 0.19% | Mailchimp |
| Non-profit | 37.57% | 2.79% | 0.20% | Mailchimp |
| Health & Fitness | 36.14% | 2.90% | 0.40% | Mailchimp |
| Legal | 35.28% | 2.68% | 0.23% | Mailchimp |
| E-commerce | 29.81% | 1.74% | 0.27% | Mailchimp |
| Retail | 34.93% | 2.24% | 0.28% | Mailchimp |
| Technology | 33.72% | 2.11% | 0.28% | Mailchimp |
| Media & Publishing | 36.83% | 3.41% | 0.12% | Mailchimp |
| Marketing & Advertising | 33.14% | 2.25% | 0.33% | Mailchimp |
| Real Estate | 35.54% | 2.06% | 0.27% | Mailchimp |
| Travel & Transportation | 35.10% | 2.39% | 0.22% | Mailchimp |
| Finance | 35.60% | 2.76% | 0.20% | Mailchimp |
| Restaurant & Food | 39.72% | 2.17% | 0.30% | Mailchimp |
Open Rate by Industry — Visual Reference
Source: Mailchimp Email Marketing Benchmarks. Chart shows select industries for illustration. All figures are Mailchimp platform benchmarks.
Email Click-Through Rate Statistics
Click-through rate measures the share of delivered emails that resulted in at least one click. Unlike open rate, click tracking does not depend on pixel loading — it records an actual server request when a recipient follows a link. This makes CTR a more robust engagement signal, though some automated security scanners do click links in emails as part of filtering, which can inflate raw click counts.
Unique Clicking Recipients = distinct addresses that clicked at least one link
Delivered Emails = sent minus hard bounces
Mailchimp's benchmark table reports a 2.62% average click rate across all benchmarked industries. E-commerce senders on the same platform show a lower 1.74%, illustrating how much industry composition can shift the overall figure. This is a recipient-based click rate using delivered emails as the denominator.
Omnisend's 2026-published benchmark report, which analyzes email activity observed during 2025, reports a 2.4% click rate for e-commerce campaign emails. The report covers more than 20 billion campaign emails across over 27,000 brands. Because Omnisend serves e-commerce senders specifically, its benchmarks should not be generalized to all email-marketing sectors.
Click-to-Open Rate (CTOR) Statistics
CTOR narrows the question: of the people whose open was recorded, what share went on to click? This makes it a measure of content relevance conditional on the email being "opened" — but since open tracking can be distorted, CTOR inherits that same problem.
Unique Clicking Recipients = distinct addresses that clicked at least one link
Unique Tracked Openers = distinct addresses that triggered a pixel load
Omnisend's 2026 report shows a 9.6% CTOR for e-commerce campaign emails observed during 2025. This means that for roughly every 100 tracked opens, about 9 to 10 resulted in a click. Because CTOR divides by recorded opens rather than delivered emails, inflated open counts from privacy pre-loading reduce the CTOR figure — making CTOR appear lower when MPP is prevalent. The direction of the bias matters when comparing pre- and post-MPP CTOR values.
CTR vs CTOR vs Open Rate — Comparison
These three metrics answer different questions and have different sensitivities to measurement problems. None is universally "better" — the right metric depends on what you want to know.
| Metric | Numerator | Denominator | Main question | Main limitation |
|---|---|---|---|---|
| Open Rate | Unique tracked opens | Delivered emails | What share of delivered emails registered an open? | Privacy pre-loading inflates counts |
| CTR | Unique clicking recipients | Delivered emails | What share of delivered emails got a click? | Security scanners may inflate click counts |
| CTOR | Unique clicking recipients | Unique tracked openers | Of recorded openers, what share clicked? | Depends on open tracking accuracy |
| Conversion Rate | Defined conversions | Delivered or sent emails | What share completed the target action? | Attribution window and model vary by sender |
Email Marketing ROI Statistics
ROI figures are the most widely cited and most frequently misunderstood email marketing statistics. The difference between attributed revenue, gross return, net return, and incremental return is large enough to change a business decision — so understanding exactly what a figure measures is not optional.
The $36-Per-$1 Claim: What It Actually Measures
Litmus has published a widely cited benchmark of approximately $36 in revenue for every $1 spent on email marketing. This figure represents attributed revenue reported by surveyed marketers and should be understood as a historical industry reference, not a guaranteed return. It does not mean every $1 generates $36 in incremental profit — it describes a revenue-to-cost ratio based on attribution, not a controlled experiment measuring causal impact.
Most marketers report returns between $10 and $50 per $1 spent
Litmus survey data suggests a distribution of responses across ROI bands rather than a single figure for all email senders. Approximately 35% of respondents reported returns of $10–$36 per $1 spent, around 30% reported $36–$50, and roughly 5% reported over $50. These are respondent bands, not measured accounting results. Individual performance depends on industry, list quality, product margins, sending costs, and attribution model. Reverify current Litmus research for precise band percentages before publication.
Three ROI Concepts You Must Separate
Revenue-to-Cost Ratio (what most "ROI" quotes describe)
Attributed email revenue ÷ email marketing cost. A $36-per-$1 ratio means the attribution model credited $36 to email for every dollar spent. This does not account for product costs, what revenue would have occurred anyway, or attribution model accuracy.
Simple Net ROI
(Net return − cost) ÷ cost × 100. Net return needs a clear definition — gross attributed revenue, gross profit, or net profit after all relevant costs. The denominator should include all email marketing costs, including platform fees, creative, and staff time.
Incremental ROI (the most meaningful but hardest to measure)
Revenue or profit caused by email above what would have happened without it. This requires a holdout group or another credible causal measurement method. Most published email ROI figures do not attempt to measure incrementality.
A $36 revenue attribution multiple does not mean 3,600% incremental profit. Attributed revenue includes the cost of goods sold, would-have-happened-anyway purchases, and other costs not subtracted in the attribution model. The actual incremental economic return of email marketing is unknowable without appropriate experimental design.
Email List Size Statistics
GetResponse's benchmark report provides one of the more detailed breakdowns of email performance by subscriber list size. The figures below come from GetResponse's 2024 publication covering email activity observed in 2023. They are GetResponse platform benchmarks for its own users, not universal industry norms, and they describe associations between list size and engagement rather than causes.
GetResponse's 2024 list-size benchmark report uses 2023 observation data. Do not describe these as 2026 measurements. Search for more recent GetResponse or comparable primary research to verify whether the patterns have shifted.
| Subscriber List Size | Avg. Open Rate | Avg. CTR | Avg. CTOR | Source / Data Year |
|---|---|---|---|---|
| 250 – 499 | 39.82% | 4.73% | 11.03% | GetResponse / 2023 data |
| 500 – 999 | 36.41% | 3.98% | 10.12% | GetResponse / 2023 data |
| 1,000 – 2,499 | 33.58% | 3.32% | 9.24% | GetResponse / 2023 data |
| 2,500 – 4,999 | 31.67% | 2.94% | 8.47% | GetResponse / 2023 data |
| 5,000 – 9,999 | 29.73% | 2.61% | 7.89% | GetResponse / 2023 data |
| 10,000 – 24,999 | 27.89% | 2.28% | 7.23% | GetResponse / 2023 data |
| 25,000 – 49,999 | 25.44% | 1.93% | 6.58% | GetResponse / 2023 data |
The pattern above — smaller lists showing higher open and click rates — reflects observed associations within GetResponse's dataset. It does not prove that list size causes engagement changes. Possible explanations include list age, audience composition, acquisition methods, sending frequency, and the types of businesses that happen to have large versus small lists. Having 250 subscribers does not cause high open rates.
Within GetResponse's 2024 benchmark data (2023 observations), the difference in average open rates between the smallest reported list-size cohort (250–499 subscribers, 39.82%) and the 25,000–49,999 cohort (25.44%) is 14.38 percentage points. This is the observed association within this platform's user population during this period. It should not be interpreted as a guarantee that growing your list will lower your open rate by exactly this amount.
Important: Total Contacts vs Active Subscribers
Platform benchmark tables describe subscriber counts as stored by the platform. These counts are not identical to actively engaged recipients, and a distinction matters when interpreting any list-size benchmark. A reported list of 50,000 contacts may contain a large share of inactive addresses that never receive or open campaigns, and those inactive contacts drag measured engagement rates downward without reflecting the quality of the active portion of the list.
Email Deliverability Statistics
Deliverability statistics describe whether emails reach recipients' inboxes, not just whether they were "delivered" in the technical sense. An email can be accepted by a receiving mail server (recorded as delivered by the ESP) and still end up in a spam folder — so platform delivery rates and inbox placement rates measure different things.
Bounce Rate Statistics
Mailchimp's benchmark table reports an average hard bounce rate of approximately 0.40% across its benchmarked industries. A hard bounce indicates a permanent delivery failure, typically because the email address does not exist, the domain does not accept email, or the recipient's server permanently rejected the message. Hard bounce rates above 2% are generally considered problematic by most ESPs, as they signal list quality issues that can damage sender reputation.
Mailchimp reports a 0.26% average unsubscribe rate across its benchmarked users. Unsubscribe rate is calculated as the number of recipients who clicked the unsubscribe link divided by the number of delivered emails. A rate below 0.5% is generally considered healthy on most platforms, though thresholds vary. Unsubscribe rates tend to be higher when sending to purchased lists, when email frequency is high relative to subscriber expectations, or when content relevance is low.
Email Conversion and Revenue Statistics
Conversion statistics in email marketing are the most context-dependent of all benchmarks because "conversion" can mean a purchase, a registration, a download, a booking, or another action. Comparing a lead-generation conversion rate to an e-commerce placed-order rate as though they were equivalent figures produces meaningless comparisons.
Omnisend's 2026 benchmark report shows a 0.08% placed-order rate for e-commerce campaign emails — meaning roughly 8 orders per 10,000 delivered campaign emails. While this sounds low, revenue per recipient (RPR) puts this in context: a single order at $80 average order value from 10,000 recipients at $0.01 per email send costs $100 total, generating $640 in attributed revenue. The per-recipient RPR figure is often more informative than the conversion rate alone for revenue planning.
Automated Emails vs Regular Campaigns
Automated emails — triggered by subscriber behavior such as signing up, making a purchase, or abandoning a cart — consistently show higher engagement metrics than manually scheduled broadcast campaigns. This gap exists because automated messages reach specific people at relevant moments rather than sending the same content to an entire list.
Omnisend's 2026 benchmark report shows automated email flows achieving a 41.1% open rate compared to 23.9% for standard campaign emails, based on 2025 e-commerce data. This roughly 17 percentage point difference reflects different audiences and sending contexts — automated flows reach people who triggered a relevant event, while campaigns reach the full list. The gap should not be interpreted as automation causing higher engagement in isolation from these contextual differences.
| Email Type | Open Rate | Click Rate | CTOR | Source / Data Year |
|---|---|---|---|---|
| Automated flows | 41.1% | 5.3% | 12.9% | Omnisend / 2025 data |
| Campaign (broadcast) | 23.9% | 2.4% | 9.6% | Omnisend / 2025 data |
| Welcome emails (automated) | ~57–63%* | ~14–18%* | — | Various / ranges reported |
*Welcome email figures vary substantially by source. Verify with original primary research before citing. Ranges reflect variation across published benchmarks, not a single measurement. Omnisend figures from 2026 report (2025 data).
How Email Marketing Metrics Are Calculated
Misreading an email statistic often comes down to one thing: not knowing what went in the denominator. The same raw click count produces different CTR figures depending on whether the platform divides by sent emails, delivered emails, or unique recipients. Below is a reference for the standard definitions as used by most major ESPs, along with common variations.
Why Different Benchmark Studies Report Different Numbers
Several structural differences explain why Mailchimp, MailerLite, Omnisend, and Klaviyo can all publish "average open rate" figures that differ significantly from each other without any of them being wrong. Each reflects its own platform's population.
| Factor | Effect on benchmarks |
|---|---|
| Customer industry mix | An ESP used primarily by e-commerce brands will report different averages than one used primarily by nonprofits |
| Account size distribution | A platform with many large enterprise senders will look different from one dominated by small businesses |
| Mean vs median | A pooled mean rate across all sends differs from a median campaign rate; both are described as "average" in reports |
| Denominator definition | Sent vs delivered vs unique recipients as the denominator changes every percentage figure |
| Bot filtering | Some platforms filter known security scanner clicks before reporting; others do not |
| Privacy measurement changes | MPP adoption shifts open rates differently across platforms based on their users' email client distribution |
Frequently Asked Questions
Related Statistics Resources
Email marketing benchmarks sit within the broader landscape of digital marketing measurement. The resources below from Statistics Fundamentals cover the statistical methods that underpin how these metrics are calculated, compared, and interpreted.
| Resource | What it covers | Relevance to email statistics |
|---|---|---|
| Marketing Statistics | Measurement fundamentals across digital marketing channels | Context for how email benchmarks sit within broader marketing data |
| Digital Marketing Statistics | Cross-channel digital performance data | Comparing email ROI to other channels (paid, social, SEO) |
| Content Marketing Statistics | Content performance benchmarks | Email content quality links to click and conversion rates |
| Statistics and Probability | Core probability and statistical concepts | Understanding statistical significance in A/B email tests |
| How Statistics Powers A/B Testing | A/B test design, sample size, and interpretation | Running statistically sound subject-line and content tests |
| Descriptive Statistics | Mean, median, percentiles, distributions | Understanding what "average" open rate benchmarks actually measure |
| Correlation vs Causation | Why associations don't prove causes | Why list size association with open rates isn't a causal relationship |
| A/B Test Calculator | Interactive statistical significance calculator | Testing whether your open/click rate differences are statistically significant |
| ← Marketing Statistics (Category) | All marketing statistics resources | Parent category for this page |
Sources and Editorial Methodology
This page draws on the following primary sources. Each source is listed with the report, the observation period where known, and the publication date. We access and verify sources directly rather than relying on secondary round-up articles.
- Mailchimp. Email Marketing Benchmarks. Mailchimp Resources. Available at mailchimp.com/resources/email-marketing-benchmarks/. Verified October 2026.
- MailerLite. Compare Your Email Performance Metrics: Industry Benchmarks. MailerLite Blog, 2025. Observation period: December 2024 – November 2025. Sample: 3.6+ million campaigns, 181,000+ accounts.
- Omnisend. Email Marketing Benchmarks 2026. Omnisend Blog, 2026. Data observed: 2025. Sample: 20+ billion campaign emails, 27,000+ e-commerce brands. Available at omnisend.com/blog/email-marketing-benchmarks/
- GetResponse. Email Marketing Benchmarks 2024. GetResponse Resources, 2024. Data observed: 2023. Includes list-size breakdowns. Available at getresponse.com/resources/reports/email-marketing-benchmarks
- Litmus. State of Email 2025 / State of Email 2026. Litmus Research. ROI benchmarks based on surveyed marketer data. Available at litmus.com/landing-page/state-of-email-2026
- Klaviyo. Email Benchmarks. Available at klaviyo.com/products/email-marketing/benchmarks. Platform benchmarks for e-commerce flow and campaign comparisons.
- Apple Inc. Use Mail Privacy Protection on iPhone. Apple Support. Available at support.apple.com. Reference for MPP mechanism and measurement implications.
Last research review: October 2026 | Latest observation year in this page: 2025 (Omnisend/MailerLite data) | Next scheduled review: When major ESPs publish 2026 full-year benchmark reports. Statistics that could have changed since the last review are noted at the source level. This page combines findings from multiple reports; differences between benchmarks reflect differences in source populations, not errors in any individual report.
Key Takeaways
- Average open rates across major ESPs range from roughly 29% to 42% depending on platform, industry, and observation year — there is no single global average
- Click rates typically fall between 1.7% and 5.3%, with automated triggered flows outperforming broadcast campaigns by a substantial margin within the same platform
- The $36-per-$1 ROI benchmark measures attributed revenue reported in surveys, not incremental profit — real economic returns vary widely and require proper measurement to determine
- Open rates are not reliable as a sole engagement signal since Apple Mail Privacy Protection pre-loads tracking pixels; clicks and conversions provide cleaner signals
- Smaller email lists tend to show higher engagement rates within the same platform dataset, but this reflects associated factors like list age and acquisition method rather than list size as a cause
- A benchmark is only meaningful when you know which platform, which observation period, which industries, and which denominator produced it