The email metrics worth managing are click-to-open rate, conversion rate, revenue per recipient, list growth net of churn, and complaint rate. Open rate has become unreliable because privacy features preload images automatically, so treat it as a rough directional signal rather than a performance number.
Why open rate broke
Open tracking works by embedding a tiny invisible image. When the recipient's client loads that image, the request registers an open. That mechanism has been quietly falling apart since Apple introduced Mail Privacy Protection, which preloads images on the user's behalf regardless of whether they ever look at the message.
The result is inflation you cannot cleanly subtract. Every Apple Mail user with the feature on registers as an open. Some corporate security gateways do the same by scanning links and images before delivery. Meanwhile, anyone reading with images blocked never registers at all — so opens are simultaneously overcounted for one group and undercounted for another.
You should still record opens. They remain useful for two things: comparing two subject lines sent to the same audience at the same moment, and detecting a sudden collapse that signals a deliverability problem. What they cannot do anymore is tell you how many humans read your email.
The metrics that survive
| Metric | Formula | Typical range | What it actually tells you |
|---|---|---|---|
| Click-through rate | Unique clicks ÷ delivered | Roughly 1–5% | Whether the offer and copy earned an action |
| Click-to-open rate | Unique clicks ÷ unique opens | Roughly 6–15% | Content quality, once the subject line has done its job |
| Conversion rate | Conversions ÷ delivered | Roughly 0.5–3% | Whether the campaign produced the outcome you wanted |
| Revenue per recipient | Campaign revenue ÷ delivered | Varies enormously | The number to optimize if you sell anything |
| Bounce rate | Bounces ÷ sent | Under 2% | List hygiene and data quality |
| Unsubscribe rate | Unsubs ÷ delivered | Roughly 0.1–0.5% | Whether frequency and relevance match expectations |
| Complaint rate | Spam reports ÷ delivered | Keep under 0.1% | The single most dangerous metric to ignore |
| List growth rate | (New − unsubs − bounces) ÷ total | Positive is the bar | Whether acquisition outpaces natural decay |
Those ranges are broad on purpose. Benchmarks vary hugely by industry, list source, and send frequency, and any source quoting a precise universal figure is overselling. Your own trailing six-month average is a far better benchmark than anyone else's number.
Click-to-open is your content grade
Click-through rate mixes two separate questions: did the subject line get attention, and did the content earn a click? Click-to-open separates them by measuring clicks only among people who opened.
That makes it the cleanest read on your copy, offer, and layout. A campaign with a low CTR but a strong CTOR has a subject-line problem — the content works, not enough people got there. A campaign with a high CTR and a weak CTOR has a mismatch: the subject promised something the body did not deliver.
The caveat is that CTOR inherits open rate's inflation, since opens sit in the denominator. Inflated opens push CTOR down. So use it comparatively — this campaign against last month's, this segment against that one — and never against an external benchmark table.
Conversion and revenue per recipient
Clicks are a proxy. Revenue is not. Revenue per recipient (campaign revenue divided by messages delivered) is the number that lets you compare a 200-person VIP segment against a 40,000-person broadcast on equal terms.
It reliably produces uncomfortable, useful findings. A small segment with a modest click rate frequently out-earns a mass send on a per-recipient basis, which is an argument for sending less mail to better-chosen people. Tie campaign revenue back to program cost with an email marketing ROI calculator so you know what the channel is actually returning, not just what it is generating.
To make this work you need attribution set up before the campaign, not after: UTM parameters on every link, a consistent attribution window (7-day click is a common default), and agreement on whether you count assisted conversions. Pick a model, document it, and stop relitigating it every quarter.
The health metrics that protect everything else
Engagement metrics measure performance. Health metrics measure whether you will still have a channel next quarter.
- Complaint rate is the most consequential number in email. Sustained rates above roughly 0.1% put you at real risk of bulk foldering and blocklisting at the major mailbox providers. Check it per campaign, not per month, because monthly averages hide the one send that did the damage.
- Hard bounce rate above about 2% signals a list-source problem — purchased data, a broken form, or no verification at capture. Fix the source, not the symptom.
- Unsubscribe rate is not automatically bad. A spike after a frequency increase is honest feedback. A slow rise across months means relevance is decaying.
- Spam-folder placement is invisible in your dashboard. Delivered means accepted, not seen. You need seed testing or provider postmaster data to see it, which is why email marketing platforms with authentication handled automatically matter — misaligned SPF, DKIM, or DMARC will quietly destroy every other metric on this page.
Look at cohorts, not just campaigns
Campaign-level reporting hides the trend that matters most: engagement decays with subscriber age. A list that looks stable in aggregate is often a healthy new cohort masking an increasingly dead older one.
Group subscribers by signup month and track click rate at 30, 90, and 180 days for each cohort. Two questions get answered immediately. First, which acquisition sources produce subscribers who stay engaged — a lead magnet that generates volume but dies at 60 days is not a good source. Second, when should your sunset policy trigger — the point where a cohort's click rate flattens near zero is your answer, derived from data rather than guessed.
A dashboard that fits on one screen
Most email dashboards report everything and inform nothing. Build one screen with four blocks:
- This week: campaigns sent, delivered, clicks, conversions, revenue.
- Trailing 12 weeks: click-to-open, conversion rate, and revenue per recipient as trend lines, not tables. Direction beats precision.
- Health: bounce, unsubscribe, and complaint rates with hard threshold lines drawn on the chart. Anything crossing a line gets attention that week.
- List: net growth, active subscribers (clicked in 90 days) as a percentage of total, and cohort retention.
Review it weekly for anomalies and monthly for trends. Do not change strategy off one campaign — email data is noisy, seasonal, and easily distorted by a single well-timed send.
Frequently asked questions
Is open rate still useful at all?
It is useful for comparing two subject lines in the same A/B test and for spotting sudden drops that indicate a deliverability problem. It is not reliable as an absolute measure of how many people read your email.
What is a good click-to-open rate?
Roughly 6–15% is a common range, but it varies widely by industry and list quality. Your own trailing average is a much more meaningful benchmark than any published figure.
What complaint rate is dangerous?
Sustained complaint rates above roughly 0.1% put your sender reputation at real risk with major mailbox providers. Check it per campaign rather than per month, since averages hide the single send that caused the spike.
Should I remove subscribers who never click?
Yes, after a re-engagement attempt. Mailing people who have ignored you for six months lowers your aggregate engagement, which mailbox providers read as a quality signal against you.
How do I measure email revenue accurately?
Tag every link with UTM parameters, pick a single attribution window such as seven-day click, and document whether assisted conversions count. Consistency matters more than which model you choose.
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