Email marketing benchmarks for SaaS by email type and industry

SaaS email benchmarks put click rate at 1.15% to 1.88% for software senders, below the all-industry figures of 2.09% and 2.62%. Open rates run from 20.68% to 39.31%, depending on whether machine opens from Apple Mail Privacy Protection are counted. Unsubscribes sit near 0.22%. Set targets on clicks, name the benchmark behind every target, and judge onboarding emails on activation rather than opens.

What is a good email open rate and click rate for SaaS

Which number you hold yourself to depends on whose data you use, because the two benchmarks below measure different things.

	Mailchimp, all users	MailerLite, all industries
Open rate	35.63%	43.46%
Click rate	2.62%	2.09%
Click-to-open rate (CTOR)	Not published	6.81%
Unsubscribe rate	0.22%	0.22%
Measure	Average	Median
Sample	Campaigns sent to at least 1,000 subscribers	3.6 million campaigns from 181,000 approved accounts
Period	Data last updated in December 2023	December 2024 to November 2025

Why the two sets do not swap

Mailchimp reports averages. MailerLite shows the median value for all benchmarks. A median is the middle campaign when all campaigns are lined up. An average adds every result and divides, so a few unusually strong sends pull it up.

A worked example. Take five campaigns with click rates of 1%, 1.5%, 2%, 2.5% and 8%. The median is 2%. The average is 3%. Same campaigns, a full point apart. Compare your own median against MailerLite's figure and your own average against Mailchimp's, never the reverse.

The definitions also differ by denominator. MailerLite computes click-through rate as total recipients who clicked / total emails delivered, while the CTOR calculates the same but considers only the recipients who opened the email. Check which one your email tool shows before you compare.

Treat open rate with caution

Both publishers flag Apple's Mail Privacy Protection. Mailchimp notes that the accuracy of email open rates may be impacted by it. MailerLite calls click rate the most accurate indicator of genuine campaign engagement. We would set targets on clicks first.

For software companies specifically, MailerLite's software and web apps segment had an open rate of 39.31% and a click rate of 1.15%, both below its all-industry medians.

This week: pull your last 20 campaigns, calculate the median click rate on delivered emails, and log it as your baseline before you run a growth experiment on subject lines or calls to action.

Email benchmarks by industry, with sample and definition

Industry rows only help when you compare like with like. The three benchmark sets below count differently, so read the method column before you read the number.

Mailchimp's average click rate ranged from 1.74% in Ecommerce to 3.27% in Non-Profits across these four industries.

Mailchimp average click rate by industryPer cent

1. Non-Profits3.27%
2. Education + Training3.02%
3. Business + Finance2.78%
4. Ecommerce1.74%

Mailchimp average click rate by industry

Item	Value, %
Non-Profits	3.27%
Education + Training	3.02%
Business + Finance	2.78%
Ecommerce	1.74%

Source	Industry row	Open rate	Click rate	Unsubscribe rate	How it is calculated
Mailchimp	Business + Finance	31.35%	2.78%	0.15%	Average (see above)
Mailchimp	Education + Training	35.64%	3.02%	0.18%	Average (see above)
Mailchimp	Non-Profits	40.04%	3.27%	0.18%	Average (see above)
Mailchimp	Ecommerce	29.81%	1.74%	0.19%	Average (see above)
MailerLite	Business and Finance	43.34%	2.37%	Not given by row	Median (see above)
Brevo	Information, Technology & Software	20.68% (30.98% with MPP)	1.88%	0.44%	Unweighted average, outliers removed
Brevo	Professional Services & Consulting	22.74% (34.37% with MPP)	2.20%	0.39%	Unweighted average, outliers removed
Brevo	Banking & Financial Services	21.30% (32.38% with MPP)	1.87%	0.34%	Unweighted average, outliers removed

Industry choice moves click rate a lot. In MailerLite's data, click-through rate ran from as low as 0.83% in politics to 4.90% in legal. Picking the wrong row can put your target off by a factor of several.

How Brevo built its numbers

Brevo's figures come from aggregated, anonymized messaging data from over 175,000 active Brevo customers across 2025. It calculated unweighted averages and eliminated extreme outliers, so a few very large senders do not pull the figure.

Brevo also reports open rate twice. The standard open rate excludes Apple Mail Privacy Protection (MPP) bot-triggered opens; the MPP-inclusive rate includes them. That split explains most of the gap between its open rates and the other two sets, in our reading. If your tool counts machine opens, compare your dashboard open rate with the MPP-inclusive column.

Which row to use for business-to-business software

We think the Brevo "Information, Technology & Software" row is the closest match for a business-to-business software company: it is recent, sector-specific and separates machine opens. Use Mailchimp's Business + Finance row as a second reference if your buyers sit in finance teams.

Then tie clicks to what happens next. A click that never reaches signup is a cost, so track it through to activation.

How automated emails compare with campaigns

The gap between automated flows and one-off campaigns is wide in the published data, and most of that data comes from ecommerce senders. Klaviyo's 2026 benchmarks draw on over 183,000 Klaviyo customers, all online stores. In that set, flows deliver over 3× higher click rates (5.58% vs. 1.69%) than campaigns. Campaigns drive the majority of send volume (94.7%), yet flows generate nearly 41% of total email revenue from just 5.3% of sends.

Omnisend's data points the same way. It analyzed more than 20 billion campaign emails and 470 million automated email sends across 27,000+ brands.

Metric	Flows / automations	Campaigns	Source
Click rate	5.58%	1.69%	Klaviyo
Top 10% click rate	10.48%	3.38%	Klaviyo
Share of sends	5.3%	94.7%	Klaviyo
Revenue per email, 2025	$3.41	$0.155	Omnisend
Conversion rate, 2025	1.49%	0.08%	Omnisend

Klaviyo's top 10% achieve a click rate of 3.38% for campaigns and 10.48% for flows.

What transfers to a software business

The likely reason, in our reading, is timing. Omnisend lists the moments automations reach people: after they join a list, browse a product, abandon a cart, place an order, or wait for an item to return. That mechanism transfers. A SaaS product has its own trigger moments: signup, a stalled setup step, a teammate invite left unaccepted, a trial nearing its end.

The dollar figures do not transfer. Revenue per email assumes a purchase inside the email session. In software, the conversion an onboarding flow drives is usually activation, and payment comes days or weeks later. We think the click rate gap is the more portable number, and even that should be read as a direction rather than a target, because ecommerce flows are short and product-led.

One more point carries over in our reading. Klaviyo reports nearly 48% of flow-driven email revenue comes from new buyers, compared to just 16% from campaigns. For a SaaS team, the equivalent is the onboarding flow doing most of its work on people who have not yet reached time to value.

What to do this week

1. Tag every flow email by trigger.
2. For each trigger, track the share of recipients who complete the next product step within seven days.
3. Compare that rate with users who did not receive the email, using a holdout group. Our guide on running a growth experiment covers how to size it.
4. Check the result against activation rate benchmarks, not against ecommerce revenue per email.

What to expect from welcome and onboarding emails

Klaviyo's flow benchmarks work as a lookup table, not a single average. The instruction is direct: "find your annual revenue range, then choose the flow type and AOV range that match your business." AOV stands for average order value.

The metric is narrow on purpose. Revenue per recipient is the revenue attributed to flow emails divided by the number of delivered emails. Each row then gives two numbers, and Klaviyo frames them as comparison values at the lower and upper ends of the range, not a guaranteed result.

In our reading, the useful part is the method, not the dollar figures. A range with a floor and a ceiling tells you where you sit. A single average only tells you whether you are above it.

Applying the method to a software onboarding sequence

Software sells no order at signup, so revenue per recipient does not fit. We swap in activation per recipient: the number of new accounts that reach your activation event within a fixed window, divided by the number of accounts delivered the first welcome email.

1. Pick one activation event and one window, for example a first project created within seven days.
2. Group signups into weekly cohorts, and keep each cohort's count of delivered first emails.
3. Compute activation per recipient for each cohort.
4. Sort the results and read off the 25th and 75th percentiles.
5. Judge each new version of the sequence against that band, not against last week alone.

Segment the way Klaviyo does. It splits by revenue band and AOV, so you can split by plan or acquisition channel, which keeps a self-serve trial from being judged against a sales-assisted account. For outside reference points on activation itself, see what is a good activation rate. To build the emails, start from the onboarding email sequence template.

Why open rates have been unreliable since Apple Mail Privacy Protection

Open tracking has always worked the same way: a tiny invisible image loads, and the provider logs an open. Apple's privacy feature broke the link between that image loading and a person reading the email.

What Apple's feature does

According to Apple, when an email arrives in the Mail app, Protect Mail Activity downloads remote content in the background by default, regardless of whether you engage with the email. The tracking pixel is remote content, so it loads too. Customer.io notes the feature was introduced in iOS 15 and that, as a result, opens are "pre-fetched at the delivery time on most devices." Since September 2021, open rates for Apple Mail users have been heavily inflated, Customer.io writes.

Clicks are affected as well, though less. Security systems and spam filters often follow links to check them, which registers as clicks with no human involved, per Customer.io. Its rule of thumb: any click within seconds of delivery is machine engagement.

How providers now report it

Some platforms separate machine activity from human activity. Customer.io added a machine open rate and click tracking feature that shows pre-fetched opens and bot clicks. Brevo, as covered earlier, publishes two open figures. Its overall averages show how wide the gap runs:

Metric	Average	Top 10%
Open rate	20.73%	44.02%
Open rate (incl. Apple MPP)	33.87%	59.89%

Source: Brevo.

Worked example. The gap between the two averages is 33.87 minus 20.73, or 13.14 percentage points. Divide that by 33.87 and roughly 39% of the MPP-inclusive opens in this table are machine-triggered. If your dashboard shows a single open figure and you compare it with a benchmark that excludes Apple Mail Privacy Protection (MPP), you can look about 13 points better than you are.

What to do this week

1. Check which open figure your provider reports. Find out whether it includes MPP opens before you compare against any published benchmark.
2. Turn on machine open and bot click filtering if your platform offers it.
3. Segment your list by email client, as Customer.io suggests, to see how much of your audience reads on Apple Mail.
4. Use opens as a relative signal only: subject line A/B tests within one send, and sudden drops. Images often fail to load in spam folders, so a sharp dip in inflated opens can flag an inbox placement problem, per Customer.io.
5. Discount clicks that land seconds after delivery before you judge a send, and tie reporting to conversions and retention instead.

Which metrics to use instead of opens

With opens inflated, the numbers worth reporting are the ones a recipient has to act on. Below are five metrics, each with a formula. Use delivered emails as the denominator unless the formula says otherwise.

Metric	Formula	Report it for (our reading)
Click-through rate (CTR)	Unique clicks ÷ delivered × 100	Newsletters, product updates
Click-to-open rate (CTOR)	Unique clicks ÷ unique opens × 100	Comparing two versions of one email
Conversion rate	Recipients who complete the goal ÷ delivered × 100	Onboarding, trial and upgrade emails
Bounce rate	Bounced ÷ sent × 100	Every send, as a list health check
Unsubscribe rate	Unsubscribes ÷ delivered × 100	Every send, as a fatigue check

Salesforce gives the CTR formula as CTR = (Number of Clicks / Number of Delivered Emails) * 100. It places a "good" CTR in the range of 2% to 5%. Promotional emails run lower, ranging from 1% to 3%. Transactional emails often surpass 5%.

MailerLite's median click rate across 3.6 million campaigns in 2025 was 2.09%. For software and web apps, its click rate is 1.15%. Read CTR alongside link count. MailerLite notes that if you don’t include many links in your emails, your click rate will be low.

CTOR still uses opens as its denominator, so treat it as a relative measure. MailerLite's median CTOR across all campaigns in 2025 was 6.81%, and it warns that your actual click-to-open rate will also be higher because inflated opens shrink the ratio.

Unsubscribe reference points

MailerLite's unsubscribe rate in 2025 was 0.22%, up from 0.08% in 2024. It attributes the rise to Gmail changes that make it easier to unsubscribe without opening. If your rate climbed in 2025, compare it against that shift before you blame your content.

Worked example: one SaaS newsletter

You send to 10,200 addresses. 200 bounce, so 10,000 are delivered. Your dashboard shows 4,000 opens, 115 unique clicks, 12 trial starts and 22 unsubscribes.

• Bounce rate: 200 ÷ 10,200 = 1.96%
• CTR: 115 ÷ 10,000 = 1.15%
• CTOR: 115 ÷ 4,000 = 2.88%
• Conversion: 12 ÷ 10,000 = 0.12%
• Unsubscribe rate: 22 ÷ 10,000 = 0.22%

In our reading, this send is on par for software on clicks and on par for unsubscribes. The number to work on is conversion.

For onboarding emails, define conversion as the in-product event you count as activation, not the click. A click shows interest. The activation event shows the email did its job. Report it by signup cohort so a single large week does not skew the trend.

A worked example: one SaaS newsletter and one onboarding sequence

Here is our method applied to two invented results from one SaaS company, checked against published figures. The inputs are ours. The benchmarks are cited.

The newsletter. It went to 4,000 subscribers. Assume all 4,000 were delivered and 1,400 opens were recorded. It drew 70 clicks and 9 unsubscribes, and 14 of the clickers went on to complete the product's activation event.

The onboarding sequence. Five emails went to 600 trial signups, so 3,000 sends. Assume 1,200 recorded opens, 168 clicks and 15 unsubscribes across the sequence. By day 14, 132 of the 600 trials had activated.

The four numbers for each

Metric	Newsletter	Onboarding sequence	Published reference
Click rate (clicks / delivered)	70 / 4,000 = 1.75%	168 / 3,000 = 5.6%	Mailchimp all users 2.62%; Klaviyo flows 5.58%
Click-to-open rate (clicks / opens)	70 / 1,400 = 5.0%	168 / 1,200 = 14%	MailerLite median, cited earlier
Unsubscribe rate	9 / 4,000 = 0.225%	15 / 3,000 = 0.5%	Mailchimp all users 0.22%
Activation per recipient	14 / 4,000 = 0.35%	132 / 600 = 22%	Compare with your own activation history

Mailchimp's industry table lists all users at 2.62% click rate and 0.22% unsubscribe rate, with Business + Finance at 2.78% and 0.15% (Mailchimp). That data was last updated in December 2023, so treat it as a reference point.

For automation, Klaviyo reports that flows deliver over 3× higher click rates (5.58% vs. 1.69%) than campaigns. Its sample is over 183,000 Klaviyo customers in ecommerce, so it shows the size of the flow gap, not a SaaS target.

What the comparison says

The newsletter clicks at 1.75%, below Mailchimp's all-users 2.62%, while its unsubscribe rate is in line with the all-users average. In our reading, content and link placement are the likely problem, not list fatigue.

The onboarding sequence clicks at roughly the flow figure. Its unsubscribe rate is more than double the newsletter's, which in our reading points to one email in the five that trial users do not want.

Which to fix first

Size the upside in activations, not clicks.

• Newsletter: lifting click rate from 1.75% to 2.62% adds about 35 clicks. At the current 14-in-70 click-to-activation ratio, that is about 7 more activations.
• Onboarding: lifting activation from 22% to 27% of 600 trials adds 30 activations.

We would fix the onboarding sequence first. Pull per-email unsubscribe and click numbers, find the email with the spike, and test a rewrite against the current version using a growth experiment. Then compare day-14 activation by trial cohort. Return to the newsletter's links and calls to action next quarter.

What to fix first, in order

Fix the problems in the order the email travels: did it arrive, did it annoy, did it get a click, did the click turn into the action you wanted. A conversion test on a list that is half bouncing tells you nothing. This order is our own view, built on standard reporting practice.

The weekly report

Copy this into a sheet and fill one row per send or per automated flow, every Monday.

Metric	How to calculate	When to stop and fix this first (our reading)
Delivery rate	Delivered ÷ sent	It drops from your own recent weeks
Hard bounces	Count of permanent failures	Any new ones not yet removed
Unsubscribe rate	Unsubscribes ÷ delivered	It rises on one send type and not others
Human click rate	Clicks ÷ delivered, machine clicks removed	It falls while delivery holds steady
Conversion rate	Conversions ÷ delivered	Clicks hold steady and conversions fall
Reply rate (B2B sends)	Replies ÷ delivered	Track the trend only

Step 1: deliverability and bounces

Delivery rate measures the percentage of sent messages that successfully reach inboxes without bouncing or being filtered as spam. Split bounces by type. Email addresses resulting in a hard bounce should be removed from your list. Soft bounces need a look, because they generally occur when the recipient's inbox is full, due to server issues, or due to content issues triggering spam filters. The last of those causes is a content fix, not a list fix.

Step 2: unsubscribes

Customer.io notes that unsubscribe rates remain reliable and can indicate content relevance. Compare the rate by email type before you touch subject lines or layout.

Step 3: clicks, with machine activity stripped

Before you read clicks or opens, remove machine activity. Use the machine-activity flag or timing rule described earlier, and keep two columns in the sheet: raw and human. Report only the human column to the team. Keep opens for one job. They can still help you compare subject line performance within your campaigns, but the absolute numbers are no longer meaningful.

Step 4: conversion

Use Mailchimp's formula, Conversion rate = (Total conversions ÷ Total recipients) x 100, where recipients means delivered emails. For sales-led products, add replies. They can be a valuable engagement metric for specific campaigns, especially in B2B contexts.

Test one change at a time

Change one element per test and set the sample size before you send. Run your own numbers in the A/B test sample size calculator. Queue the next tests in the growth experiment backlog template, and follow the method in how to run a growth experiment.

What this page does not cover

This page does not cover how to write or design emails, benchmarks for other channels, or how to translate ecommerce revenue per email into SaaS revenue targets. It also does not address email consent or privacy rules, and nothing here is legal or financial advice.

Related on Race to Scale: What is a good activation rate? Benchmarks by product type and how to define yours; Free-to-paid conversion rate benchmarks for freemium, opt-in trials and card-required trials; SaaS churn rate benchmarks by price point and segment.