Welcome to the Email Productivity Benchmark Report! This data shows a range of email statistics and metrics across a random sample of EmailAnalytics customers.
You can use this data to benchmark your own team’s email performance and activity. Start a free trial to see how your team’s stats measure up (it’s free).
This data has been updated to include all data for May 2023. We will update it for June 2023 sometime in early July.
We update this post monthly with new stats, so remember to bookmark this page!
Table of Contents
This set of statistics covers ALL of May 2023, including weekends.
If we exclude weekends, we get a clearer picture of what a true workday looks like. Here’s what the stats look like if we exclude all weekends:
This data comes from a random sample of 1542 EmailAnalytics customers — both paying and trial users. All data has been aggregated and anonymized.
All @gmail.com email addresses were removed from the data, so that we are only looking at professionals.
We removed the following users from our data:
All data is localized to each user’s time zone.
About 41% of the data is from US-based customers. Here’s the geographic breakdown:
You need to be familiar with some terminology we use here at EmailAnalytics to fully understand the data.
Average response time (actual): The actual time it takes to reply to an email.
Average response time (work hours): The amount of elapsed time within work hours to respond to an email. Work hours are set individually by EmailAnalytics customers, and are, by default, set to 8am to 5pm.
Average response time (received, actual): The actual time it takes to receive replies to emails. We do not measure “work hours” response time for received responses, because work hours among participants often vary across time zones.
We have a few rules for how we count email response time:
For the full set of rules, see this page.
How we calculate response time for a single day:
Many emails are not responded to until the next day, or several days later. So, we calculate average response time for a single day as the average amount of time it takes to respond to an email received on that day.
Example:
You received exactly one email on April 1st at 10am, and later responded to it on April 3rd at 10am. Your email response time for April 1st is 48 hours, even though that is longer than a single day. Thus, your response time for a single day can be longer than a single day.
In other words, your average email response time for April 1st is the average of all your responses that you sent in response to emails you received on April 1st.
Most email activity occurs on Mondays and Tuesdays. As expected, Saturdays and Sundays have a significant drop-off of activity:
| ">Avg. Sent | ">Avg. Received | ">Response time (wh) | ">Response time (actual) | ">Response time (received, actual) | |
| ">Sunday | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[21]">4.2 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[22]">18.2 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[27]">7:39:39 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[24]">17:37:14 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[22]">17:33:42 |
| ">Monday | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[21]">28.6 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[22]">73.5 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[27]">3:46:16 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[24]">9:42:53 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[22]">13:27:07 |
| ">Tuesday | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[21]">33.5 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[22]">86.1 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[27]">3:29:44 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[24]">9:18:32 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[22]">12:56:06 |
| ">Wednesday | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[21]">33.1 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[22]">85.2 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[27]">3:19:34 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[24]">9:24:09 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[22]">13:10:01 |
| ">Thursday | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[21]">31.9 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[22]">83.3 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[27]">3:38:35 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[24]">11:57:07 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[22]">16:40:32 |
| ">Friday | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[21]">26.8 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[22]">70.9 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[27]">3:47:14 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[24]">18:24:33 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[22]">21:47:16 |
| ">Saturday | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[21]">4.5 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[22]">19.9 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[27]">8:30:14 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[24]">27:55:43 | " data-sheets-numberformat="" data-sheets-formula="=R[-28]C[22]">24:32:50 |
Here’s a chart that shows average email activity by day, for each day, excluding spam:
What does a typical workday look like in terms of email activity? Check out the chart below. The X-axis represents each of the 24 hours of a day, where 0 is midnight.
Here we see a pretty clear ramp-up beginning around 7am, with a pre-lunch peak in activity by around 11am.
There’s a dip in activity around noon, which is when most people are probably taking their lunch break. Then, we see another rise in activity, peaking around 2pm before starting a late-afternoon decline. By 7pm, email activity has leveled off for the night.
And here’s what spam emails look like over the course of the month:
Response time shows an interesting pattern. If we only record responses within work hours, then every day of the work week actually shows pretty similar response times. But if we look at it from an “actual” perspective (ie, the amount of actual time elapsed between receipt of an email and responding to it), then responses take longer as the week goes on.
Here’s the average email response time by day:
Again, we see the clear pattern of Mondays being the best day for response times, with a gradual increasing slowness as the week progresses.
Let’s take a look at how things are progressing month-by-month:
We see an interesting divergence here with response time. EmailAnalytics customers’ response time is represented by the yellow and blue lines, while the red line represents all responses received from anyone (EA and non-EA users).
EA customers’ response time is consistently faster than non-EA users.
So, what wisdom can we draw from this data?
Tuesdays and Wednesdays have the fastest response time for both recipients and senders. As the week goes on, response times get slower.
People are dealing with the most email activity on Mondays. As such, they might be more likely to send a reply quickly so they can move on to the next email in their inbox. Fridays have the lowest email volume of the work week. So your recipient is more likely to have more time to respond to your email if you send it on a Friday.
EmailAnalytics customers respond to emails over 64% faster than non-EmailAnalytics customers, and that is likely at least partially due to the Hawthorne effect.
The Hawthorne effect says that simply knowing you’re being monitored — even if you’re the one monitoring yourself — causes your behavior to change. If you monitor your email response time, it’s likely to improve. If you monitor your team’s email response time and activity, it’s likely to improve.
What gets measured gets improved.
How many emails does your team send and receive every day? What is their average response time? You can use this data to benchmark your own performance (or your team’s) in EmailAnalytics. Start a free trial and get instant access to your own email stats — no credit card required, and no software to install.
How do your team’s stats measure up to these benchmarks?
