Mailchimp campaigns to dashboard

Your Mailchimp campaign report, with the rates recomputed.

Export your campaign report from Mailchimp and drop the CSV here. Open Rate and Click Rate arrive as text with a percent sign, so they will not sum or average anywhere else. This page parses them, recomputes each rate from Successful Deliveries rather than Total Recipients, and separates unique clicks from total clicks so a repeat clicker does not read as two people.

Prefer the full explorer? Open the app, or start from the generic dashboard builder.

Percent strings parsed

Open Rate and Click Rate are read as numbers, sign and all.

Rates recomputed on deliveries

Opens over Successful Deliveries, weighted by volume rather than averaged per campaign.

Unique against total clicks

People and clicks are kept apart, because Mailchimp reports both.

Local only, no signup

Subject lines and list names stay in your browser tab.

The rate columns are strings, and the denominator is wrong

Two things go wrong the moment a Mailchimp campaign export lands in a spreadsheet. The rate columns come through as text, because Mailchimp writes them as 42.19% rather than as a number, so any attempt to average them across campaigns returns zero or an error. And when people fix that with a find and replace, they usually then average the rates directly, which weights a send to two hundred people the same as a send to twelve thousand.

The subtler problem is the denominator. A rate averaged across campaigns is not the same as the rate you would get by dividing total opens by total deliveries, and the second is the one that answers how your list is doing. Mailchimp's own rates are computed on Successful Deliveries, which is Total Recipients minus bounces, so recomputing on Total Recipients gives a slightly lower and slightly wrong number. This page recomputes from the counts, on deliveries, every time.

How to export from Mailchimp

Open Reports in Mailchimp Sign in at mailchimp.com, then choose Campaigns and Reports from the left navigation. Confirm the audience selector at the top, because a multi-audience account reports each one separately.
Filter to the period and campaign type Regular sends, automations and A/B tests all land in the same list. If you only want newsletters, filter first, or the dashboard will compare a welcome automation against a broadcast.
Click Export as CSV The export control sits above the campaign table on the right. It gives one row per campaign with the summary counts, not per-subscriber detail.
Check the file opens with two rate columns Open Rate and Click Rate should both carry a percent sign inside the cell. That is normal, and it is the first thing this page fixes.
Drop it here List and Type become filter chips, Send Weekday becomes a natural grouping, and the recomputed rates appear alongside the ones in the file.

If you need per-subscriber behavior rather than per-campaign summaries, that is a different export from the audience section, and it is far larger. This page is written for the campaign report.

Try with sample Mailchimp data (the same columns, 52 rows, no upload needed).

What the dashboard shows

Every panel below is built from a column that is actually in the export, named the way Mailchimp names it.

Delivery quality over time. Successful Deliveries against Total Recipients, with soft and hard bounces stacked, which is the health check most people skip.
Open rate, recomputed. Unique Opens over Successful Deliveries, volume-weighted, next to the per-campaign average so the difference is visible.
Click rate and click-to-open. Unique Clicks over deliveries, and over opens, which is the metric that survives privacy proxies better.
Send weekday effect. Send Weekday as a group-by. Most lists have a weekday that is genuinely better, and most people guess it wrong.
Hard bounce accumulation. Hard Bounces per campaign over time, which is how you spot a list going stale before the provider does.
Unsubscribes per thousand delivered. Normalized, so a big send does not automatically look like a bad send.

Sample header row

Title,Subject,List,Send Date,Send Weekday,Total Recipients,Successful Deliveries,Soft Bounces,Hard Bounces,Total Bounces,Unique Opens,Open Rate,Total Clicks,Unique Clicks,Click Rate,Unsubscribes

Six things to know before you trust these numbers

Open Rate and Click Rate are text. The percent sign lives in the cell. Excel sums them to zero, and a find and replace across two columns is the usual workaround. Here they are parsed on load.
Open rates have been unreliable since 2021. Apple Mail Privacy Protection pre-fetches tracking pixels, so a large share of recorded opens are a proxy rather than a person. Trends within your own list still mean something. Absolute numbers do not.
Total Clicks and Unique Clicks are different questions. One counts clicks, the other counts people. Total Clicks divided by recipients is not a click rate, it is a click-per-recipient figure that can exceed one.
Rates use deliveries as the denominator. Not recipients. The gap is your bounce rate, which on a neglected list is large enough to matter.
Averaging rates across campaigns is wrong. It weights a 200-person send equally with a 12,000-person send. Recompute from the counts instead, which this page does.
Soft and hard bounces mean opposite things. A soft bounce is a full mailbox or a temporary failure. A hard bounce is an address that does not exist and will never work. Total Bounces hides that distinction.

Read click rate and click-to-open rather than open rate, recompute on deliveries, and watch hard bounces trend. Those three habits give you a picture of list health that survives whatever the mailbox providers do next.

Frequently asked questions

Why does Excel refuse to average my Mailchimp open rates?

Because the Open Rate column is text. Mailchimp writes the percent sign inside the cell, and a cell containing a percent sign is a string, not a number. AVERAGE over strings returns zero or an error depending on your version. Stripping the sign fixes the type, but averaging the resulting rates still weights small and large sends equally. Recomputing from the counts is the better fix.

Should I still care about open rate?

As a trend within your own list, yes. As an absolute number, no. Apple Mail Privacy Protection pre-loads tracking pixels for a large share of subscribers, which registers an open whether or not anybody read the email. That inflates the figure and makes cross-list comparison meaningless. Click rate and click-to-open rate are far more resistant, because a click requires an actual person.

What is the difference between Total Clicks and Unique Clicks?

Unique Clicks counts subscribers who clicked at least once. Total Clicks counts every click, so a reader who clicks three links contributes three. Divide Unique Clicks by Successful Deliveries for a click rate you can compare between campaigns. Total Clicks is useful for judging how much content in the email got engagement, but it is not a rate you can express per person.

Which denominator should rates use?

Successful Deliveries, which is what Mailchimp itself uses. Total Recipients includes addresses that bounced and never saw the email, so dividing by it understates your engagement by whatever your bounce rate is. On a healthy list the difference is under one percent. On a list that has not been cleaned in three years it can be substantial.

Can I compare automations against regular campaigns?

You can, but be careful. An automated welcome email goes to a subscriber at their moment of maximum interest and routinely posts engagement two or three times a broadcast. Putting them in one average makes both numbers useless. Filter by campaign type first, or use the type as a group-by so the two are shown side by side rather than blended.

Is my subject line and list data sent anywhere?

No. The CSV is parsed inside your browser tab and the charts are drawn from memory. There is no upload and no account. A campaign export is not the most sensitive file in a company, but it does describe your audience size, your engagement and your commercial calendar, which is not information most people want sitting in someone else's logs.