2026 benchmark database
Email benchmarks by industry (2026 database)
Need a planning baseline right now? Cross-provider reports in 2025 and 2026 cluster around a median open rate near 43%, a click rate near 2%, and a CTOR near 6.8%, based on large published datasets from MailerLite, Mailchimp, and GetResponse. This hub gives industry-level cuts so targets are closer to real list behavior.
The downside is simple: benchmark datasets are never a perfect map of your audience mix, so you should treat them as a starting point, not an automatic goal.
choose an industry and inspect the full metric profile
Pick an industry to load its complete benchmark view, including open-rate percentiles and all companion metrics used for weekly and quarterly planning.
Current selection:B2B SaaS
Industry sample in this database: 1,280,000,000 sends and campaigns.
| Metric | p25 | Median | p75 |
|---|---|---|---|
| Open rate | 33.20% | 39.80% | 46.70% |
| Click rate | N/A | 2.57% | N/A |
| CTOR | N/A | 6.46% | N/A |
| Bounce rate | N/A | 0.77% | N/A |
| Unsubscribe rate | N/A | 0.15% | N/A |
| Spam complaint rate | N/A | 0.02% | N/A |
| Conversion rate | N/A | 1.28% | N/A |
| Revenue per recipient | N/A | N/A | N/A |
Only open rate has percentile cuts in the source reports. Other rows show single-point medians where public sources publish one value.
full industry comparison table
This HTML table keeps every metric in one place so it is easy to crawl, export, and audit. Values are percentages unless noted.
| Industry | Open p25 | Open median | Open p75 | Click rate | CTOR | Bounce | Unsub | Spam | Conversion | Revenue / recipient | Sample size |
|---|---|---|---|---|---|---|---|---|---|---|---|
| B2B SaaS | 33.20% | 39.80% | 46.70% | 2.57% | 6.46% | 0.77% | 0.15% | 0.02% | 1.28% | N/A | 1,280,000,000 |
| Ecommerce fashion | 31.40% | 37.60% | 44.50% | 1.98% | 5.27% | 0.68% | 0.23% | 0.03% | 0.41% | $0.24 | 3,620,000,000 |
| Ecommerce beauty | 33.10% | 39.40% | 46.40% | 2.21% | 5.61% | 0.63% | 0.21% | 0.02% | 0.48% | $0.29 | 2,980,000,000 |
| Ecommerce home | 29.50% | 35.90% | 42.60% | 1.76% | 4.90% | 0.81% | 0.22% | 0.03% | 0.33% | $0.18 | 2,710,000,000 |
| Fintech | 28.80% | 34.70% | 41.50% | 2.19% | 6.31% | 0.94% | 0.17% | 0.03% | 1.08% | N/A | 840,000,000 |
| Edtech | 34.90% | 41.30% | 48.80% | 2.86% | 6.92% | 0.74% | 0.16% | 0.02% | 1.63% | N/A | 790,000,000 |
| Healthcare | 38.10% | 44.50% | 51.70% | 2.43% | 5.46% | 0.88% | 0.13% | 0.01% | 1.22% | N/A | 910,000,000 |
| Real estate | 30.10% | 36.50% | 43.80% | 1.42% | 3.89% | 1.15% | 0.19% | 0.03% | 0.93% | N/A | 620,000,000 |
| Media and publishing | 35.90% | 42.60% | 49.90% | 2.97% | 6.97% | 0.64% | 0.19% | 0.02% | 0.84% | N/A | 1,460,000,000 |
| Non-profit | 37.20% | 43.90% | 50.90% | 3.19% | 7.27% | 0.71% | 0.16% | 0.01% | 1.05% | N/A | 980,000,000 |
| Recruiting | 30.80% | 37.20% | 44.60% | 2.01% | 5.40% | 1.08% | 0.16% | 0.02% | 1.42% | N/A | 540,000,000 |
| Hospitality and travel | 27.70% | 33.80% | 40.70% | 1.83% | 5.41% | 0.92% | 0.24% | 0.03% | 0.72% | $0.12 | 730,000,000 |
| Food and beverage | 33.20% | 39.60% | 46.70% | 2.24% | 5.66% | 0.82% | 0.21% | 0.02% | 0.67% | $0.16 | 1,240,000,000 |
| Fitness and wellness | 38.40% | 45.10% | 52.40% | 2.62% | 5.81% | 0.69% | 0.18% | 0.02% | 1.14% | $0.14 | 860,000,000 |
| Professional services | 30.90% | 37.00% | 44.10% | 2.08% | 5.62% | 0.93% | 0.15% | 0.02% | 1.31% | N/A | 700,000,000 |
methodology and source list
This page merges benchmark inputs from 15 industries and 20.26 billion sends and campaigns in the current database. The source mix combines platform reports from Klaviyo (2026), MailerLite (2025), ActiveCampaign (2025), Omnisend (2025), Campaign Monitor (2025), HubSpot (2025), GetResponse (2024), and Mailchimp (2025).
The hero values are answer-first references for quick planning: open around 43%, click around 2%, and CTOR around 6.8%. Those values reflect central tendencies in large cross-industry reports, especially MailerLite and Mailchimp. The median across industries in this specific database is lower at 39.4% open, 2.21% click, and 5.62% CTOR because the table includes categories with lower intent windows, such as hospitality and real estate. That gap is expected and useful because it keeps targets close to the segment you actually send to.
We keep open-rate percentiles as published and avoid inventing percentile bands for other metrics. When a source provides only a single click or conversion value, this page stores that number as the median column and marks p25 and p75 as unavailable. It keeps the structure honest. A fabricated percentile table would look neat but it would hide where the evidence is thin. This choice makes the table less uniform, yet it is safer for planning and forecasting.
Industry relevance affects weighting in the source aggregation. Ecommerce categories get stronger influence from Klaviyo and Omnisend datasets because those reports include dense retail volume and revenue metrics. B2B SaaS and professional services pull more from HubSpot, Campaign Monitor, and Mailchimp benchmarks where business send behavior is better represented. The same logic applies to healthcare, education, and nonprofit rows, where list intent and send cadence differ from promotional retail programs.
There is a clear downside to every benchmark dataset: provider samples are not random and list quality varies by acquisition channel. A list built from high-intent product signups can beat the published p75 while a list built from broad giveaways can stay below p25 with similar creative quality. Segmentation depth also changes headline metrics. A team that sends tightly segmented campaigns will usually report higher opens and clicks than a team sending one broad newsletter to everyone on the list.
Use this hub as a benchmark baseline, then run controlled checks in your own account. Keep one control segment stable for at least four sends, then compare challengers against that control before changing goals. That process is slower than copying a headline number, but it prevents false wins and helps you avoid forecast errors when budget planning depends on realistic conversion and revenue assumptions.
source list used in this database
- Klaviyo email benchmarks by industry (2026), sample 183,000. View source
- ActiveCampaign AI industry benchmarker (2025), sample 8,340,000. View source
- ActiveCampaign email marketing benchmarks (2025), sample 3,300,000. View source
- Campaign Monitor email insights and reporting guide (2025). View source
- Campaign Monitor email metrics playbook (2025). View source
- HubSpot email marketing benchmarks by industry (2025). View source
- Mailchimp email marketing benchmarks (2025). View source
- MailerLite email marketing benchmarks (2025), sample 3,600,000. View source
- Omnisend ecommerce marketing report (2025), sample 24,000,000,000. View source
- GetResponse email marketing benchmarks (2024), sample 4,400,000,000. View source
- Klaviyo benchmark report (2024), sample 325,000,000,000. View source
how to read benchmarks honestly
Benchmarks help when you use them as context, not as a fixed grade. Start with industry medians, then move one level down into your own campaign types. A welcome flow and a monthly newsletter should not share one target. Product intent, time pressure, and list recency are different, so metric baselines should be different too. If you compare unmatched campaigns, you will judge execution with the wrong yardstick and likely make the wrong change.
List quality is the biggest hidden variable. Public benchmarks can include very healthy permission-based lists and weaker lists in the same industry. That spread affects every metric. A team with tighter acquisition control may beat p75 with average design, while a team with mixed list sources may struggle even with strong copy. This is why complaint rate and unsubscribe rate matter as much as open rate. If those quality signals rise, the headline open number is less meaningful.
Segmentation depth also skews benchmark interpretation. Campaign Monitor and HubSpot both stress metric context by audience and objective, because broad sends flatten performance and hide strong pockets of demand. You can see this directly in the reporting guides from Campaign Monitor and HubSpot. Treat any single benchmark number as a range anchor, then break targets by segment and campaign objective.
Time window choice can also change whether your benchmark read is fair. A short period with one seasonal promotion can push click rate far above normal, especially in ecommerce reports such as Omnisend's 2025 study. Compare against rolling 90-day medians when you can, and keep a separate weekly control so one campaign spike does not reset your baseline.
One practical rule works for most teams: if you beat your own trailing median for clicks and conversion while complaint rate stays stable, you are improving even if you are below top quartile headlines from public studies. The downside of chasing public p75 blindly is that it can push teams toward weaker list practices or short-term offer tactics. Sustainable gains come from steady segment cleanup, clearer message intent, and realistic benchmark targets.
frequently asked questions
What is a good email open rate in 2026?
Across major benchmark reports, around 43% is a practical cross-industry reference point. In this database, the median by industry is lower because business-heavy and travel-heavy categories pull the center down. Compare against your own industry first, then compare against your closest segment inside that industry.
Should I optimize for open rate or click rate?
Use opens to confirm inbox visibility, then move decisions to clicks, conversion rate, and revenue per recipient. Open rate by itself can look strong while buyer intent stays weak.
How often should I refresh benchmark targets?
Quarterly updates are usually enough for planning. Monthly checks are useful for high-volume programs or teams changing offer mix and audience source. If your list source changed, refresh targets early.
Can I compare B2B SaaS performance with ecommerce medians?
You can for rough orientation, but it can mislead strategy work. Purchase cycles and list behavior are different. Keep the main comparison inside your own industry and funnel stage.
What mistake hurts benchmark analysis most?
Copying top-quartile targets from unmatched lists. A better approach is to beat your own rolling median with controlled tests and stable segment definitions.
related guides and tools
Use benchmarks with timing and copy guidance so planning targets are matched to execution choices.