The Mailchimp reporting dashboard for agencies running email marketing

Each campaign is listed with its type — regular, A/B test, multivariate, RSS or plain-text — the audience and segment it went to, what was delivered after bounces, and then the three rates that describe engagement: open rate, click rate and click-to-open rate. Click-to-open is the one most agencies now lead with, because it asks a question inflated open counts cannot distort: of the people who opened, how many were interested enough to click. Sorting flips between revenue, open rate, clicks and send date, so the same table answers the recap question and the diagnosis question without leaving the page.

Because Apple Mail Privacy Protection prefetches images on a recipient's behalf, some share of every open belongs to a mail client rather than a person. A toggle on the campaign table re-bases open rate and click-to-open rate onto the opens that were not prefetched, and the report is explicit that click rate is untouched, because that prefetching loads images rather than clicking links. The Links tab then aggregates the click map across every send in view, listing each destination path with how many sends carried it, the unique clicks it earned and its share of the period's clicks.

Automation reporting collapses if it only counts emails, because a four-step welcome series and a one-step birthday email are not comparable on send volume. The Journeys tab reports what entered instead: the contacts that started a journey in the period, how many reached the last step, how many are still moving through it and how many exited early. Completion rate is the derived figure, and it is the number that separates a journey people finish from one they abandon at step two. Classic automations sit in the same table as Customer Journeys, because a client cares which automation earns, not which builder made it.

Every journey also carries its own delivery, click and revenue figures, so a re-engagement journey with a strong click rate and a weak completion rate reads differently from one that nobody opens at all. Status is on the row: a paused journey is labelled as paused and still reports the emails it sent before it stopped, rather than silently looking like an automation that had a bad month. Switching the send-type filter to Journeys re-bases the summary cards, the trend line and the click mix onto automation alone, which is the fastest way to show a client what runs without anyone touching it.

Tags and segments do different jobs and the report keeps them apart. A tag is a label somebody applied — a webinar attendee, a wholesale buyer, a free-trial signup — and it is reported with its contact count, its thirty-day movement and how its open and click rates compare with the account's own. That comparison is what makes a tag actionable: a tag that reliably out-engages the account is a targeting opportunity worth building a campaign around, and one that consistently under-performs is either the wrong content, the wrong cadence, or a label somebody stopped applying two quarters ago and nobody has retired since.

Segments are rules Mailchimp resolves at send time, so their size is the more interesting number. Each segment shows its contact count, its movement and its share of the sendable audience, which is how an account team spots the two patterns that matter: an engaged segment quietly shrinking, or a never-opened segment quietly growing. Both counts are levels rather than period totals, so the send-type filter and the date range never distort them — the report says so on the tab rather than leaving an account manager to work out why a number did not move with the range.

A test is only useful if somebody can see why it was called. Each A/B and multivariate test is reported as a card: the campaign it belonged to, the variable under test — subject line, from name, content or send time — how long it ran before the winner was sent, and the metric the decision was made on. The variants sit underneath in a table with recipients, opens, open rate, clicks and click rate, and the winning arm is marked. The winner is always the arm that led on the deciding rate, never the one that happened to receive more recipients.

That structure is what turns testing from an activity into a report line. A subject-line test that moved open rate by half a point is a fair result to show a client; three subject-line tests in a row that moved nothing is the argument for testing content or send time instead. Because the arms reconcile back to the parent campaign — recipients, opens and clicks all add up to what the campaign reported — the test card can be read next to the campaign row without an account manager having to explain why two screens disagree.

Hard and soft bounces mean different things and deserve different responses, so the Hygiene tab separates them per send rather than reporting one blended bounce rate. A hard bounce is an address that does not exist, and Mailchimp cleans it from the audience so it is never sent to again — which is why the count of contacts cleaned during the period is reported next to the number of cleaned addresses sitting on the audience. A soft bounce is temporary and belongs in a different conversation, usually about sending volume or a receiving server rather than about data quality.

Abuse reports get their own figure because they are the signal a mailbox provider weighs most heavily, and unsubscribes are broken out by the reason contacts gave on Mailchimp's own form: no longer interested, too frequent, did not sign up, inappropriate content or other. Too frequent is a cadence problem; did not sign up is an acquisition problem; and telling them apart is the difference between fixing a schedule and fixing a form. A send-time chart closes the tab, showing unique opens and unique clicks across the hours of the day and the days of the week.