Black Friday Email: How to Time the Send and Staff Support
Your Black Friday email creates its own wave of support tickets, and you choose when that wave starts. Split your list into segments, estimate how many tickets each send will bring, and schedule each send for a time when someone is ready to answer.
This guide covers the planning: how to split the send, how to estimate the volume and how to work out the hours your team needs. How to answer each type of ticket is covered in other guides, linked below.
Why the send time decides your support load
A promo email reaches thousands of inboxes within minutes. Some of those customers reply or open a ticket, and they all do it at about the same time. They ask for the sale price on an order they already placed. They ask why a code fails at checkout. They ask whether an item is back in stock, or where an order from last week is.
All of this lands at once, on top of your usual Black Friday queue. If the email goes out at midnight and your team starts at 9, those tickets wait all night. If you send to the whole list in one go, they all arrive in the same hour.
You control both of these. Picking the send time and the size of each send is support planning, not just marketing.
Step 1: Split your list into waves
Some customers are more likely to write in than others. Recent buyers are the obvious group, since they’re the ones who see a lower price on something they just paid for.
In Shopify you can build a segment for them. The segment editor has a last_order_date filter, and Shopify’s own example is last_order_date >= -7d for customers whose last order was in the past 7 days (Shopify segment filters). Change the number of days to match your store.
With that one filter you can build three waves:
| Wave | Segment filter | Why it’s separate |
|---|---|---|
| A: older customers | last_order_date < -7d | No recent order, so fewer questions about the price they paid |
| B: subscribers who never ordered | last_order_date IS NULL | Questions about products and codes, not existing orders |
| C: recent buyers | last_order_date >= -7d | Most likely to write in, so give them your best-staffed window |
When you create a campaign in Shopify Email, you can pick the segment it goes to, and you can set a Custom date and time for the send (Shopify Email campaign help). That’s all you need to schedule each wave separately.
The segment sizes matter as much as the waves. Write down how many customers are in each one. You need those numbers for the next step.
Step 2: Estimate the tickets each wave will bring
You need two numbers: how many people get the email, and what share of them usually write in after a campaign.
Find your reply rate
Use your own history, not an industry average. Open your last big promo email and note:
- How many customers received it.
- How many tickets came in during the hours after the send that were clearly about the email.
Divide the second number by the first. That’s your reply rate for a sale email. If you can, work it out separately for recent buyers, since their rate is likely to be higher.
If you’ve never run a big sale, you have no history. Pick a low guess and a high guess, plan your staff for the high one, and keep this year’s numbers for next time.
Turn it into tickets per wave
Expected tickets for a wave = segment size × reply rate.
Here’s an example with made-up numbers. Use your own.
- Wave C has 1,500 recent buyers.
- Your last campaign brought 3 tickets for every 100 people who got it.
- 1,500 × 3 ÷ 100 = 45 tickets expected from wave C.
Do this for each wave. You now have a ticket count for every send.
Step 3: Work out the hours and the people
Measure your handle time
Handle time is how long one ticket takes from opening to sent reply. Take it from your helpdesk reports if they track it. If they don’t, time ten tickets of each type on a normal day. Sale-price questions and “my code doesn’t work” usually take less time than a lost parcel, so measure the types you expect from this email.
Calculate the workload
Workload in hours = tickets × handle time in minutes ÷ 60.
Continuing the example: 45 tickets × 4 minutes ÷ 60 = 3 hours of work.
Divide by the time you want to answer in
Decide how soon after the send you want these tickets answered. Then:
People needed = workload in hours ÷ hours in that window.
If you want wave C answered within 2 hours: 3 ÷ 2 = 1.5 people working only on these tickets. In practice nobody spends every minute of an hour on tickets, so round up. Here that means two people.
Remember these people also have the rest of Black Friday to handle: delivery questions, failed payments, address changes. The email wave comes on top of that queue, not instead of it.
For the full monthly version of this calculation, with occupancy and working days, use our support staffing calculator.
Step 4: Set the send times
Now you can fit the waves to your team’s shifts.
- Send when the team is on. Schedule each wave so its busiest hours fall in a staffed shift. Use your store’s time zone and check where most of your customers are.
- Recent buyers go in your strongest window. Wave C brings the most tickets for its size. Send it when you have the most people working, not first thing at night.
- Leave space between waves. Don’t send the next wave until the previous one has mostly been answered. Check how long the replies kept coming after your last campaign and use that as your gap.
- Keep one person off the email waves. Someone should stay on the usual queue so an urgent delivery problem doesn’t wait behind a batch of sale questions.
A simple sale-day plan for the example store could look like this:
| Time | Send | Who is on the queue |
|---|---|---|
| Morning, shift start | Wave A | Regular team |
| Late morning | Wave B | Regular team |
| Early afternoon | Wave C, recent buyers | Regular team plus extra cover for 2 hours |
| Evening | No send | Regular team, clearing what’s left |
Your own times depend on your shifts and your customers. What matters is that every send lands in a window you’ve staffed for it.
What to do when the numbers don’t fit
Sometimes the math says you need more people than you have. You have three options:
- Split a big wave into smaller ones. Two halves of wave C, a few hours apart, halve the peak.
- Send earlier in the week. An early-access email to one segment moves part of the load off Friday.
- Take the repeat questions off your team. If most of the wave is the same few questions, that’s the part to hand over.
How to split sale tickets between automation and your team is covered in Shopify Price Adjustment Tickets on Black Friday: AI or Human? For the work to do in the weeks before the sale, see how to reduce Shopify support tickets before Black Friday.
Where Relay fits
Relay answers, refunds and resolves your Shopify tickets 24/7, following your rules.
Relay is an AI support agent that understands your product, answers customer questions, takes action, and knows when to bring in your team. It works in four steps: Understand, Decide, Act, Escalate. You stay in control through Permissions, which set what it can and cannot do.
Setup takes 3 steps: teach Relay your business, set your rules, connect and go live.
More peak season guides are in our seasonal category.
FAQ
What time should I send my Black Friday email?
When your support team is working and can cover the tickets it will bring. Send to the customers most likely to write in, usually recent buyers, during the shift where you have the most people.
How many support tickets will my sale email create?
Multiply the number of people who get it by the reply rate from your last promo email. If you have no history, plan for a high guess and record the real number this year.
How do I know how many people to put on support for the send?
Multiply expected tickets by your handle time in minutes and divide by 60 to get hours of work. Divide that by the number of hours you want them answered in, then round up.
Should I send to my whole list at once?
Not if your team can’t answer the replies in the time you want. Split the list into segments with Shopify filters such as last_order_date and schedule each one separately.
