Email Volume vs Revenue Benchmarks: 79M Sends Across 16 Brands

79 million emails. $5.8M in campaign revenue. 16 brands. 12 months. This is the most detailed look at email volume vs revenue we've published.

The best month? April — $0.115 revenue per email. The worst full month? January — $0.054. Same brands. Same agency. Same playbook foundations. The only thing that changed was email send volume, timing, and what was waiting on the other side of each send.

Here's what we expected to find: a clean, positive relationship between send volume and revenue. Send more emails, make more money. That's the assumption most brands operate on when they scale their campaign cadence. It's the logic behind "let's add another send this week." It's the default advice in every email marketing course on the internet.

Here's what we actually found: the correlation between send volume and campaign revenue is 0.746. That's positive — volume and revenue do move together. But it's not strong enough to be a strategy. Volume explains some of the revenue story. It doesn't explain enough. And for the brands that treated volume as the strategy, the results were brutal.

What Is Revenue Per Email?

Revenue per email (also called revenue per send or RPE) is total campaign-attributed revenue divided by total emails sent. It measures how much revenue each email generates on average.

Across our 12-month dataset, portfolio-wide revenue per email averaged $0.074. The range across individual brands runs from $0.006 to $2.59 per email — and that range tells you more than the average ever could.

We publish monthly email benchmarks that track aggregate performance metrics. This post is the companion piece — it digs specifically into the relationship between how much you send and how much you earn. For retention and post-purchase context, see our repeat purchase benchmarks.

Email Volume and Revenue: Why 8% More Sends Produced 39% Less Revenue

This isn't one insight. It's two — and they compound.

Stat 1: From April to May, email volume increased 8% — from 7.18M sends to 7.77M sends. Campaign revenue dropped 39% — from $828,026 to $502,712. Eight percent more emails. Thirty-nine percent less money.

That's not a rounding error. That's $325,314 in lost campaign revenue in a single month while sending more.

Stat 2: Revenue per email in the first half of the dataset (March through August) averaged $0.083. In the second half (September through February), it averaged $0.061. A 27% decline in email efficiency even as total send volume climbed.

Now put those two stats together. Volume went up across the year. Revenue per email went down. The portfolio sent more emails into progressively lower-intent months and watched the returns erode.

April to May is the smoking gun because it isolates the variable. Nearly the same volume. Dramatically different results. What changed wasn't how many emails went out — it was whether anyone on the other end was ready to buy.

Should You Send More or Fewer Emails? What the Correlation Shows

The lazy answer is "send less email." You'll hear it from deliverability consultants, inbox placement tools, and anyone who's ever been on the receiving end of a daily promotional email they didn't ask for.

The other lazy answer is "send more email." You'll hear it from agencies trying to justify their campaign calendar, growth marketers chasing top-line numbers, and brands that see November's revenue and assume the email volume caused it.

Both are wrong. Or rather — both are right in specific contexts and catastrophically wrong in others.

The real answer: match volume to demand.

The correlation between send volume and campaign revenue is 0.746 (Pearson r). For the non-statisticians: that's a moderately strong positive relationship. Volume and revenue move in the same direction more often than not. But 0.746 is not 0.95. It's not even 0.85. There's a lot of variance that volume alone doesn't explain.

What explains the rest? Demand. Customer intent. Whether the person opening that email has a reason to buy today or is just clearing their inbox.

April and November — the two highest revenue-per-email months — are high-intent months. Tax refunds in April. Holiday shopping in November. Customers are primed to spend. When you send more email into high-intent periods, volume amplifies demand. Revenue per email stays flat or goes up.

May and January — the two months that punished volume the hardest — are low-intent months. Post-spring lull. Post-holiday hangover. Customers aren't looking to buy. When you send more email into low-intent periods, you're not amplifying demand. You're just wearing out your list.

The answer isn't "send less." It's "send smarter."

Monthly Email Volume and Revenue Benchmarks

Here's the full month-by-month picture. 16 brands. 12 months. $5.8M in campaign revenue.

Table: Monthly Email Volume and Campaign Revenue Benchmarks (16 DTC Brands)

*February 2026 is a partial month (~10 days of data). Do not compare directly to full months.

Read the table vertically, not just horizontally. The rev/email column tells the story. It peaks at $0.115 in April, settles into a $0.065–$0.086 range through summer and early fall, bumps to $0.085 in November, and then declines through January. February is a partial month and shouldn't be compared directly.

The email-as-percentage-of-store-revenue column is equally revealing. April hit 24.4% — email was punching well above its weight. By December, that share dropped to 14.8%, even though December had the second-highest send volume of the year. More emails. Lower share of revenue. Volume didn't translate.

November is the month that looks like it validates the "send more" playbook — highest volume (8.95M), strong revenue ($763,817), solid rev/email ($0.085). But compare it to April: April sent 20% fewer emails and generated 8% more revenue at 35% higher efficiency. November's volume worked because demand was there. April's efficiency worked because the volume was disciplined.

Email Volume Case Studies: Three Brands, Three Outcomes

The aggregate numbers show the trend. The individual brand stories show the mechanism. Here are three email volume vs revenue case studies that illustrate exactly how volume and demand interact.

Archetype 1: Scaled Without Demand (Non-Alcoholic Spirits)

This is the cautionary tale.

Table: Non-Alcoholic Spirits — Email Volume vs Revenue Per Email

A non-alcoholic spirits brand scaled send volume 6x between March and October. Revenue per email dropped 7x — from $0.29 to $0.04. That's not diminishing returns. That's negative returns. The efficiency collapse at higher volume is consistent with what happens when send volume outpaces audience intent — though we can't isolate whether it was the volume itself, the segment depth, or seasonal demand changes that drove the drop.

The recovery is just as telling. When the brand pulled volume back to March levels in February, revenue per email snapped back to $0.23. The list wasn't permanently damaged. The audience was still there. They'd just been buried under emails they didn't need.

This is the pattern that scares email managers: volume that looks like growth but is actually erosion. Total revenue may go up for a month or two as volume increases, but per-email efficiency collapses. And when you finally look at the unit economics, you realize you've been spending more (design time, content production, platform costs) to earn less per send.

Archetype 2: Scaled Into Demand (Workwear)

This is the success story.

Table: Workwear (Scale) — Email Volume vs Revenue Per Email

A workwear brand scaled send volume 3x between March and December. Revenue per email went up — from $0.080 to $0.116 in November and $0.113 in December. More emails and better efficiency. That shouldn't be possible if volume is the problem.

But volume isn't the problem. This brand scaled into a natural demand window. Workwear purchases spike in Q4 — holiday gifting, end-of-year uniform purchases, seasonal gear. The audience was ready to buy. More emails met more intent. Revenue per email rewarded the increase.

The lesson isn't "send 3x more email." It's "send 3x more email when your customers are 3x more likely to buy." The workwear brand didn't just scale randomly. It scaled into a period where the demand justified the volume.

Archetype 3: Volume Without Demand (Fashion)

This is the silent failure.

Table: Fashion (Scale) — Email Volume vs Revenue Per Email

A fashion brand scaled send volume 3.4x between March and November. Revenue per email dropped 40% — from $0.030 to $0.018. This brand sent into what should have been a high-intent month (November) and still saw efficiency decline.

Why? Because the list wasn't built for that volume. When you 3.4x your sends, you're reaching deeper into less engaged segments. You're emailing people who haven't clicked in months. You're pushing past the natural size of your active audience and into the disengaged tail. Segmenting by purchase behavior rather than engagement is one way to ensure incremental sends reach people with actual buying intent.

The fashion brand's problem wasn't timing. November should have been favorable. The problem was that the incremental sends went to the wrong people. Volume only works when the recipients have intent. Scaling into demand works. Scaling into your suppressed segments doesn't.

Revenue Per Email Benchmarks by Vertical

The aggregate revenue per email ($0.074 average across the year) hides massive vertical variation. Here's what revenue per email benchmarks actually look like at the brand level.

Table: Revenue Per Email by Vertical (12-Month Range)

The spread is enormous. A luxury home brand averages $0.30 per email. A decorating supplies brand averages $0.019. That's a 16x difference in per-email efficiency between brands managed by the same agency.

Three variables explain the range:

Product price point. The luxury home brand sells $200+ items. Every conversion is a large dollar amount. The decorating supplies brand sells $8–$15 items. Even a high conversion rate can't overcome a low AOV when you're measuring revenue per send.

List quality and engagement. The agave spirits brand sends to 5–10K highly engaged recipients. Every email reaches someone who opted in recently and opens consistently. The decorating supplies brand sends to 150K–1M recipients, reaching deep into less engaged segments. The denominator matters as much as the numerator.

Volume discipline. The luxury home brand sends a measured cadence — 53–73K per month. The decorating supplies brand fluctuates from 150K to 1M. Consistent, moderate volume to an engaged list produces consistent, high efficiency. Erratic, high volume to a broad list produces erratic, low efficiency.

If your revenue per email is below $0.03, it's worth asking whether you're over-sending, under-segmenting, or both. An automated list hygiene system can help by removing chronically disengaged profiles before they drag down your efficiency metrics.

What the 0.746 Correlation Actually Means

Let's put the statistics in plain English.

A Pearson correlation of 0.746 between send volume and campaign revenue means that email send frequency and revenue have a moderately strong positive relationship. When one goes up, the other tends to go up. About 56% of the variation in revenue can be explained by variation in volume (that's r-squared, 0.746^2 = 0.556).

But the other 44% is explained by something else.

That "something else" is the entire point of this post. It's demand. It's list quality. It's segment depth. It's whether the person receiving the email has a reason to buy today. Volume is the input. Demand is the multiplier. And when the multiplier is low, adding more input doesn't help.

Here's a useful mental model: volume is necessary but not sufficient.

The brands in this portfolio earning $0.10+ per email can scale profitably. Each incremental send still generates meaningful revenue. Their lists are engaged, their segments are tight, and their content is relevant. For these brands, sending more email in high-demand periods is smart.

The brands earning $0.02 per email are in a different position. Each incremental send generates almost nothing — and worse, it degrades deliverability, trains subscribers to ignore the brand, and accelerates list fatigue. For these brands, sending more email without fixing the underlying efficiency problem is burning list health for marginal revenue.

The 0.746 correlation says volume matters. The 0.061 average rev/email in the second half of the dataset says volume isn't enough. Both things are true. The email volume vs revenue relationship is not linear, and the brands that win are the ones that hold both truths simultaneously.

What Drives Our Send Volume Decisions

We don't use a fixed campaign calendar. The number of sends we plan for any given month is a function of two inputs: what trailing store revenue is telling us, and where we are in the seasonal cycle.

When store revenue across the portfolio is climbing — when we can see demand building in the top-line numbers — we're more comfortable scaling volume. Rising store revenue means customers are already buying. Email at that point is amplifying existing demand, not trying to manufacture it. That's the scenario where more sends tend to produce more revenue without cratering efficiency. April in this dataset is the clearest example: store revenue was the second-highest of the year ($3.4M), and email volume at 7.2M sends produced the best revenue per email we saw all year ($0.115).

When store revenue flattens or declines, we tighten segments and pull back send volume. January is the mirror image of April — store revenue dropped to $2.6M, but we kept volume at 8M sends. Revenue per email fell to $0.054. That's the pattern we try to avoid: maintaining volume after the demand signal has turned. In practice, that means fewer campaigns, narrower segments, and a higher engagement threshold for who gets the send.

The other input is the seasonal playbook. Some demand periods are predictable enough to plan around. BFCM is the obvious one — we scale into November and early December knowing the intent is there. Tax refund season in April is another. Back-to-school in late summer, depending on the brand. On the other side, we plan for lulls: January is almost always a pullback month, and mid-summer tends to be soft for most of the portfolio. None of this is rigid — we adjust based on what the trailing numbers actually show — but having a seasonal baseline keeps us from being surprised by the same patterns every year.

Every portfolio is different. The email volume vs revenue data in this post gives you the inputs to build your own volume framework. The key metric to watch is revenue per email — if it's declining while volume is climbing, something needs to change. Whether that's volume, segmentation, content, or timing depends on your brand.

Email Send Volume FAQ

Is there an ideal number of emails to send per month?

No. The ideal email send cadence depends on your list size, engagement levels, and seasonal demand. The data shows brands successfully sending anywhere from 5K to 1.3M emails per month. The question isn't how many emails to send — it's whether each incremental send generates enough revenue to justify its cost to list health. Track revenue per email as you scale to find your ceiling.

Does sending more emails hurt deliverability?

It can. The non-alcoholic spirits brand in this dataset scaled from 19K to 119K sends and saw revenue per email drop from $0.29 to $0.04. That kind of efficiency collapse typically correlates with lower engagement rates, which ISPs use as deliverability signals. But the workwear brand scaled from 440K to 1.3M and saw efficiency improve. Volume hurts deliverability when it outpaces demand. Volume paired with demand doesn't.

What is a good revenue per email?

A good revenue per email for e-commerce campaigns is $0.07 or above. Across our 16-brand portfolio, the 12-month average is $0.074. Brands above $0.10/email are generally scaling profitably. Brands below $0.03/email should investigate whether they're over-sending, under-segmenting, or both. The range across verticals is extreme — $0.006 to $2.59 — so compare against your own trailing average rather than a single benchmark number.

Should I reduce email volume when revenue per email drops?

Not necessarily. First, diagnose why it dropped. If revenue per email declined because you scaled into a low-demand period (like May or January in this dataset), pulling back volume is the right call. If it dropped because you added a new, less engaged segment, fix the segmentation. If it dropped because your content became less relevant, fix the content. "Send less" is sometimes the right answer. "Send better" is almost always part of it.

How do I calculate revenue per email?

Divide total campaign-attributed revenue by total emails sent in the same period. In Klaviyo, pull your campaign analytics for a given month. Take the total revenue and divide by total recipients. If you sent 500,000 emails and generated $40,000 in campaign revenue, your revenue per email is $0.08.

Methodology: How We Measured Email Volume vs Revenue Across 79M Sends

• Data source: Klaviyo campaign analytics, March 2025 through February 2026 (12 months). February 2026 is a partial month.
• Portfolio: 16 DTC brands across workwear, fashion, luxury home, decorating supplies, non-alcoholic spirits, agave spirits, and additional verticals.
• Revenue definition: Klaviyo-attributed campaign revenue using last-touch attribution with Klaviyo's default attribution window. Flow revenue is excluded — this post focuses specifically on campaign sends and campaign-attributed revenue.
• Revenue per email: Total campaign revenue / total emails sent for the period.
• Correlation: Pearson r calculated on monthly aggregate send volume vs monthly aggregate campaign revenue (n=12 months). r = 0.746.
• Per-brand data: Individual brand stories use the brand's own monthly send volume and campaign revenue. Brands are identified by vertical only. Brands are categorized by their primary product vertical. These labels are approximate — some brands span multiple categories.
• Anonymization: No brand names. Verticals and archetypes only.
• Partial month: February 2026 data reflects approximately the first 10 days of the month. It is included for completeness but should not be compared directly to full months.