---
title: What the 48 hours after an announce look like in the data
description: The first two days of an on-sale tell you how the show will end. Five numbers to pull at hour 48, what a healthy curve looks like next to a stalling one, and the one move to make before the weekend.
image: https://backstage.hive.co/hubfs/first48hours.png
---

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# What the 48 hours after an announce look like in the data

The first two days of an on-sale tell you how the show will end. Five numbers to pull at hour 48, what a healthy curve looks like next to a stalling one, and the one move to make before the weekend.

![](https://backstage.hive.co/hubfs/first48hours.png)

#### Published on Oct 07, 2026

#### [Written by Anthony Ramsay](https://backstage.hive.co/backstage-articles/author/anthony-ramsay)

 Category

#### [Backstage Community](https://backstage.hive.co/backstage-articles/tag/backstage-community)

By Thursday afternoon, most promoters already know whether the show that went on sale Tuesday is going to be fine. They know it the way you know a room is going to be quiet before doors. It is a feeling.

The feeling is usually right. The problem is that it is not something you can act on, hand to a booker, or explain to an artist's team on a call. The first 48 hours of an on-sale produce five numbers that say the same thing the feeling does, and once you have read them across a few shows you stop needing the feeling.

Last month we wrote about [asking your ticketing export questions with AI](https://backstage.hive.co/backstage-articles/how-to-use-ai-to-make-sense-of-your-ticketing-data). That post was about the whole year. This one is about two days.

## The five numbers

Pull them from the ticketing platform at the same hour every time, 48 hours after the public on-sale opens, and count everything from the first presale order up to that point. Consistency is what makes them comparable from show to show, and comparability is the whole point.

The first is share of capacity sold, as a percentage rather than a count. A 300-cap room at 20 percent and a 1,200-cap room at 20 percent are in the same place, even though one number is four times the other.

The second is presale share. What proportion of those sales came in before the public on-sale, from your list, the artist's list, or a partner code. This tells you how much of the early number was your own audience showing up and how much was strangers finding it.

The third is the hour-by-hour shape of day one. Every ticketing platform gives you a timestamp per order. Group them by hour. A healthy show spikes at on-sale, drops through the afternoon, and shows a second smaller bump that evening when people get home and open the link they saved. A stalling show spikes once and goes flat.

The fourth is repeat buyers. How many of the early orders came from people who have bought from you before. Your export has the answer if you match emails against last year's file, and if you built the presale list from the last post, you already have the file open.

The fifth is average order size. Pairs and groups buying early mean the show has social pull. People are going together. Singles buying early usually mean a dedicated fanbase, which is good news until it runs out.

## What healthy looks like

Your own last five on-sales set the real benchmark, and the Try it at the bottom is how to build it. Until then, here is the shape most club and theatre shows follow.

A healthy 48 hours for a normal announce tends to land somewhere in the teens or twenties as a share of capacity, with presale making up a meaningful slice of that, a visible evening bump on day one, and repeat buyers somewhere around half of the early orders. The exact numbers move with genre, market, price and the day of the week.

What does not move is the shape. If a show is going to sell out, you can usually see the curve bending upward before the weekend. If it is going to struggle, the curve tells you that on Thursday too, which is a lot earlier than the artist's agent will.

## What stalling looks like

A stalling show can post the same 20 percent as a healthy one and be in trouble, and the tell is in the other four numbers.

Presale share far above the norm means your list bought and nobody else has found the show yet. That is a reach problem and the fix is distribution. Leave the price alone.

A flat day one with no evening bump means the announce did not travel. The people who saw it bought, and nobody shared it.

Repeat buyers well under your norm, with a healthy total, means new people found the show. That is good news for the room and a warning that the usual crowd is not interested in this one.

Average order size dropping toward one means singles, and singles mean a core fanbase that will be exhausted by week two with nothing behind it.

## The one move at hour 48

Do not touch the price. Discounting at hour 48 tells the people who bought at full price on day one that they were wrong, and they are the people you need for the next show.

Instead, read which number is off and fix that one thing.

If it is presale share, the show needs to be seen by people outside your list, so the move is a paid push and an artist share, this week, while the on-sale is still news.

If it is a flat day one, the announce creative did not work. The second push needs a different hook, a different image, a different reason to care.

If it is repeat buyers, the show is reaching a new audience and the job is capturing their emails at checkout, so the next announce does not have to go looking for them again.

If it is order size, the show needs a reason to bring a friend. A pair price for the list, a plus-one code, anything that turns a single into two.

One number, one move, made before the weekend. That is what turns a feeling into a plan, and a plan is something you can say out loud to a booker. If the show is still soft at week three, the moves get bigger, and [we have written those up separately](https://backstage.hive.co/backstage-articles/why-your-tickets-arent-selling).

## Try it

Pull your last five on-sales from the ticketing platform, each as its own export, and paste this in alongside them:

> "These are ticket sales exports from five shows, plus my full export from the last twelve months. For each show, I will give you the capacity and the date and time the public on-sale opened. Look only at orders placed from the first order up to 48 hours after that public on-sale time. Ignore any row where the quantity is negative, because those are refunds. Give me a table with one row per show and these columns: tickets sold in that window, what share of capacity that is, what share of those orders were placed before the public on-sale time or carry a presale code, what share came from an email address that appears in the twelve-month export with an earlier purchase, and the average number of tickets per order. Then plot orders per hour for the first 24 hours after the public on-sale for each show."

Give it the capacities and on-sale times, then look at the table. The show that sold out and the show that struggled will look different in ways you can point to, and that difference is your benchmark from now on.

The next time you get the Thursday feeling, pull the five numbers and you will know which column is causing it. Doing that for one show is twenty minutes. Doing it for every on-sale, with the benchmark updating itself, is the point where most promoters stop exporting and start asking their platform to show it. Either way, post the shape of your best and worst curve in the [Backstage green room](https://backstage.hive.co/community?utm_source=blog&utm_medium=post&utm_campaign=first-48-hours), no numbers needed, just the line. The room has never compared curves before, and the promoter with the ugliest one usually learns the most.

*Get more of these delivered straight to your inbox. Subscribe to Backstage Dispatch.*

*Join the conversation with event marketers figuring this out together:* [*Backstage community*](https://backstage.hive.co/community?utm_source=blog&utm_medium=footer&utm_campaign=first-48-hours)

### [Anthony Ramsay](https://backstage.hive.co/backstage-articles/author/anthony-ramsay)

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