Some brands send too many emails and burn out their list, causing unsubscribes. Others under-send and miss key moments without even knowing when they happened. So how do you know when to hit send? It’s a question that every lifecycle marketer has faced and one that deserves a strategy, not just a guess.

Most brands optimize for consistency in their email sends, a set cadence of one or two emails per week. But consistency alone shouldn’t be the goal, growing revenue while avoiding unsubscribes should be. If you’re not accounting for how each email impacts both sides of that equation you’re flying blind.

Revenue per email is easy to measure, but it only tells half the story. What’s often ignored is the cost of sending that email (and we’re not referencing the literal cost of sending which is usually a few hundredths  of a penny per email). While unsubscribes don’t show up in your budget as a line item, they represent a very real loss. Every person who leaves your list is someone you can’t reach again, which means lower future revenue and incremental acquisition cost to replace them.

Unsubscribes are traditionally evaluated retroactively, drawing attention when the unsubscribe rate spikes. But what if we treated unsubscribes as a cost from the start? What if we could pre-emptively weigh email revenue against the long-term cost of losing subscribers?

How Does Email Revenue Trend Over Time?

We analyzed data from ecommerce brands across different verticals, measuring email performance over the first 26 weeks after a customer subscribed. For each week, we looked at two key metrics: revenue per email sent and unsubscribe rate per email sent.

Revenue per email follows a dramatic curve. In week 1, email performance is at its peak, generating an average of $7 per email sent. This is when new subscribers are most engaged: welcome flows, cart recovery emails, and initial purchase intent all converge to deliver outsized results. Week 2 holds some value at $1, but from there, revenue drops sharply.

Most weeks following that early window see revenue settle between $0.20 and $0.50 per email, with a few notable spikes. Weeks 9 and 18 show brief surges in value, likely tied to replenishment cycles, new product consideration, or behavior-based triggers. These peaks suggest that even in the “flat” part of the curve, there are hidden high-value windows that brands can capitalize on if they know when to look.

How Do Email Unsubscribes Trend Over Time?

The unsubscribe rate is the highest in week 1 at 1.2% likely driven by discount-seekers or first-time buyers who churn immediately after purchase. While that might sound like a red flag, it’s a known and manageable pattern in ecommerce, and is more than offset by the revenue lift.

Unsubscribes fall more gradually than revenue, settling at a level slightly lower than 0.2% by week 12. This plateau looks safe on the surface, but it masks the potential cumulative impact of routine, untargeted sends. Even a small, steady trickle of unsubscribes can hurt long-term retention and lifetime value if not paired with corresponding revenue.

Evaluating revenue or unsubscribe rates in isolation doesn’t tell the full story. Week 1 looks like a no-brainer where revenue and unsubscribes are high, but what about week 3 where revenue has dropped and unsubscribes are still elevated? Or week 9, where revenue spikes again with minimal unsubscribe risk?

To make smarter decisions, we need a single framework that considers both upside and downside.

How to Measure Email ROI with a Revenue-to-Unsubscribe Ratio

Once we analyzed revenue per email and unsubscribes per email, we calculated a simple ratio: revenue divided by unsubscribes. It’s a concept borrowed from finance, where investors use the Sharpe Ratio to evaluate how much return they’re getting per unit of risk. Investors are generally comfortable purchasing a riskier stock as long as there is enough upside to warrant that risk.

In this case, we’re mimicking that concept by measuring the reward-to-risk ratio of sending an email. The higher the ratio, the more value you’re generating relative to the cost of list churn.

We know unsubscribes aren’t a perfect proxy for cost. But they are the most consistent signal of list churn. Every early unsubscribe means losing the chance to convert that customer and build long-term retention.

Across all of the brands we analyzed, we noticed a few consistent patterns.

Week 1 is gold. When a customer joins your email list, they’re at their most engaged. Simultaneously, it is very common for customers to join a list, take advantage of a discount, and immediately unsubscribe. The reward-to-risk ratio is at its highest: revenue is extremely high, and while unsubscribes also spike, the tradeoff is worth it.

Weeks 2 to 3 are counterintuitive to what you might expect. Revenue drops off quickly, but unsubscribes remain elevated. Customers most likely purchased in week 1-2 and are at their highest risk for churning. These weeks tend to have the worst tradeoffs: low return, high cost. This is the moment where over-sending can do real harm to your list.

Weeks 9, 18, and 26 show unexpected spikes. These are moments where revenue jumps while unsubscribes stay low. For replenishable products, this might align with usage cycles. For apparel or home goods, it could be a natural moment of renewed consideration.

How Can Personalization Improve Email ROI and Retention?

Our analysis suggests two major opportunities to improve retention and email ROI through personalization.

First: instead of resorting to basic segmentation, consider user level attributes in deciding when to engage customers.

Brands often group users into large segments based on criteria like “signed up in the last 180 days” or “made a purchase this year.” But as the data shows, email performance changes dramatically over time, even within a span of 26 weeks. This means customers within the same segment may behave entirely differently depending on when they last purchased, how engaged they are, or what products they’ve browsed. Brands will be better served by a strategy that seeks to look at each customer individually and make decisions based on their most recent data.

Second: Optimal customer engagement decisions will account for risk-reward tradeoffs tied to each interaction.

In this context, the graphs help visualize that a fixed engagement cadence will likely lead to one of two pitfalls. There are weeks where either:

1. The brand is sending emails too frequently, pushing customers away with low revenue upside
2. The brand is not sending enough emails, even though the reward/risk ratio is favorable

A comprehensive strategy will look to assess the incremental benefits and costs of each customer interaction and trigger it when the ratio between the two is optimal.

Building this kind of personalization manually is difficult. It's nearly impossible to analyze every unique user’s behavior, purchase history, and timing across dozens of segments. Even when the data exists, it’s hard to know which signals matter most, or when to act on them. Monocle’s AI agents determine the right time to send, at the individual level, based on customer behavior, product lifecycle, and purchase history. We’re not just measuring outcomes, we’re optimizing for them in real time.

Adapting Your Email Strategy by Ecommerce Vertical

This framework isn’t a one-size-fits-all solution, and we’re not arguing unsubscribes are a perfect proxy for measuring risk. But it’s a framework that provides an intuitive way to make decisions based on the expected risks and rewards of engaging with customers via email.

In our next post, we’ll break down how these trends differ by vertical and how to tailor your email cadence to your product type. 

Let’s talk.