Charts

Data visualisation

Every chart we show helps a promoter, venue or festival decide something. Which show to add, when to push marketing, whether a night will sell out. These rules keep charts in the dashboard, in reports and on slides reading as one system.

Start with the decision

Name the question a client will act on before you draw anything. The title answers it. If the chart has no answer to give, it probably shouldn’t be a chart.

Put the answer in the title

Sales pace

Tracking 16 points ahead of similar shows

Put the answer in the title.

Sales to date

Sales over time

Don’t

Don’t make the reader work out the point.

Pick the form from the job

Job	Form	Ticketing example
Compare	RankedBars	Channels by tickets sold
Change over time	LineChart or BarChart	Daily sales
Pace	LineChart with a band	This show against similar shows
Part of a whole	StackedBars or capacity meter	Sold, held and available
Spread	Histogram	Days between buying and the show
Drop off	Funnel	Checkout steps
Two measures per show	Scatter	Pace against sell-through
The same chart per group	SmallMultiples	Scan rate per gate
When	Heatmap	Orders by hour and weekday
Where	RankedBars with share	Top postcodes
One number	Stat tile, not a chart	Tickets sold today

A stat tile or a two-row table often beats a plot. If there’s no shape to see, show the number.

In a dashboard, these forms sit inside cards. See dashboard design for sizing and layout.

Tickets sold

1,842

This week

VIP sell-through

Target 85%

Where buyers came from

Email brings in nearly half of orders

Colour by job

Each palette has one job. Mixing jobs is how charts start lying.

Categorical tells series apart

Use slots in order and never cycle them. A series keeps its colour when you sort or filter. Use 6 at most, then group the rest into Other. For 2 or 3 series, use --chart-pair-* or --chart-trio-*.

Light

Dark

Ticket type mix

GA carries both nights

GA
VIP
Early bird

Sequential shows how much

Stage heat runs from the surface to the strongest colour. In dark mode the busiest cells glow.

Light

Dark

When fans buy

Fans buy most on Friday evenings

Fewer
More

Diverging shows ahead of or behind

Cool is ahead, warm is behind, grey is on the benchmark.

Light

pos-4

pos-3

pos-2

pos-1

neg-1

neg-2

neg-3

neg-4

Dark

pos-4

pos-3

pos-2

pos-1

neg-1

neg-2

neg-3

neg-4

Pace by section

The balconies are behind

Behind
Ahead

Status carries meaning

Good, warning, serious and critical. Only for meaning, never as a series colour, and always with an icon or label.

Light

good

warning

serious

critical

Dark

good

warning

serious

critical

Sell-through

61%up 9 points, better

Target 85%

Gross revenue

$118.4kdown 4%, worse

This week

Orders

1,310up 18%, better

This week

Status colour always comes with an arrow and words.

Emphasis and ink

Highlight the story series in --chart-highlight and put context in --chart-context.

Don’t

Don’t colour text in a series colour. Labels and values use chart ink.

Reading aids

Four ideas come up in almost every ticketing chart. Draw them the same way everywhere.

Compared with is a soft band for the middle half of similar shows, with a dashed median. Say what the comparison is built from.
This show is one 2px line in the highlight colour.
Prediction is a dotted continuation inside a faint cone that shows the range.
Goal is a short tick at the right edge with its label.
Now is a labelled dot, not a line across the chart.

Pace against similar shows

Tracking ahead of similar shows

Keep it honest

Use one y-axis. Two measures on different scales become two charts, or both indexed to 100.
Start bars at zero.
Measure sell-through against sellable capacity, not gross capacity.
Show a range with every forecast.
Name the comparison period and the set of similar shows.

Labels and type

Charts use Intermission with tabular figures, so columns and ticks line up.
Every chart has a title, a subtitle for scope and a source line.
Label up to 4 series directly. Two or more series also get a legend. A single series needs neither.
Format dates and times with the date and time rules.

Writing for charts

Chart copy follows the Oztix voice and tone guidelines, the language and grammar guidelines, and unslop. No dashes as punctuation, anywhere.

Part	Rule	Do	Don’t
Title	States the takeaway with a number or direction. Sentence case, active voice, no colon, no full stop.	Presales doubled after the lineup drop	Presales over time
Subtitle	One full sentence with what was measured, where and when.	Tickets sold per day across all Hollow Pines Festival ticket types.	tix/day, all TTs
Numbers	Numerals for every value, commas for thousands, closed-up percent.	12,480 tickets, 68%	twelve thousand, 68 %
Money	Dollar sign with no space, cents only when the amount isn’t round.	$19.95, $1,200	$ 20.00
Spans	Use “to”, never a dash.	$50 to $100, Fri 27 to Sun 29 Nov	$50–$100
Dates and times	Follow the date and time foundation. 24-hour time only on dense axes and in exports.	Fri 27 Nov, 7:30pm	27th Nov, 7:30 PM
Terms	Use house terms and one name per metric everywhere it appears.	Ticket type, access code, scan rate	Ticket category, promo code, entries
Annotations	Short full sentences that name the cause. No arrows or symbols.	Lineup announced. Sales tripled that day.	Lineup drop → 3x!!
Empty states	Say what happened and what to do next.	No sales yet. Tickets go on sale Fri 27 Nov at 9:00am AEST.	No data available
Source line	Name the data and when it was pulled.	Source: Oztix ticketing data, pulled Mon 21 Sep.	Source: internal data
Summaries and alt text	Takeaway and numbers first, then the comparison, then the scope. No AI vocabulary or hedging.	Saltwater Sessions sold 4,200 tickets in Sydney, 1,100 more than Melbourne.	This chart showcases the vibrant sales landscape.

Accessibility

Never identify a series by colour alone. Pair it with a label, a legend or a pattern.
Offer a table view of every chart.
Write a text summary that doubles as alt text.
Light categorical slots never carry text. The tokens page lists which ones.
Leave a 2px --chart-gap between touching fills.
Hover adds detail. It is never the only way to read a value.
Respect reduced motion.

One chart, three places

Dashboard. Interactive, with filters in one row above the charts.
Report. Static and readable without hover. The source line is required.
Slides. Fewer marks, bigger type and one takeaway per slide. Build up in steps if it helps the story.

Ticketing reference

Terms

Term	What it means	Chart
Pace index	Tickets sold divided by what similar shows had sold at the same days out, times 100. Over 100 is ahead.	LineChart, stat tile
Sell-through	Tickets sold as a share of sellable capacity, after holds and kills. Never gross capacity.	Capacity meter
On-sale spike	Orders in the first hours after tickets go on sale, often shown against the forecast for that hour.	Heatmap, annotated BarChart
Presale and general	Tickets sold before and after the public on-sale.	StackedBars
Walk-up	Tickets sold on the day of the show.	The end of the LineChart
No-show rate	Tickets issued but never scanned, as a share of tickets issued.	Stat tile, RankedBars with reference
Scan rate	Tickets scanned per 15 minutes at each gate, with the running share of fans inside.	BarChart with line, SmallMultiples per gate
Resale share	Tickets resold as a share of tickets sold.	StackedBars
Ticket type mix	Tickets sold by ticket type, such as GA, VIP or early-bird.	StackedBars, using the pair or trio sets
Channel and referrer	Where buyers came from before they bought.	RankedBars
Postcode	Where buyers live, from billing postcodes. Report shares, not counts, when totals differ.	RankedBars
When fans buy	Orders by hour and weekday, in the venue timezone.	Heatmap

Questions and charts

Question	Term	Chart
Is the show selling on pace?	Pace index	LineChart with band, median, forecast, target and today
How many have sold, and when?	Sales to date, daily orders	LineChart (cumulative), BarChart by day
What moved sales?	Announcements, line-up drops, email sends, ad bursts, price releases	Annotations on any time chart
How did the on-sale go?	On-sale spike	BarChart by hour, annotated
Which ticket types are selling?	Ticket type mix	StackedBars using the pair or trio sets
How much sold before general sale?	Presale and general	StackedBars
Where did buyers come from?	Channel, referrer, campaign	RankedBars
Where do buyers live?	Postcode, region, travel distance	RankedBars with share
Who are the buyers?	Age and gender brackets, new and returning	RankedBars or StackedBars
How far ahead do fans buy?	Booking lead time	Histogram
How many tickets per order?	Order size	Histogram
Where do buyers drop out?	Checkout conversion, waitlist to purchase	Funnel
When do fans buy?	Orders by hour and weekday, venue timezone	Heatmap
Which sections are filling?	Sell-through by section or zone	Heatmap, or a table of meters
Is there unmet demand?	Sell-out time by release, waitlist sign-ups, abandoned checkouts	BarChart with annotations, Funnel
How do buyers respond to price?	Sales by price tier, jumps at each price release	RankedBars, annotated BarChart
What else do our buyers go to?	Also bought	RankedBars
How are all my shows tracking?	Portfolio pace against sell-through	Scatter with quadrants
How did entry go?	Scan rate per 15 minutes, share of fans inside	BarChart with line, SmallMultiples per gate
Did people turn up?	Attendance against sold, no-show rate	RankedBars with reference (similar shows)
How good was the forecast?	Forecast against actual	LineChart
How much was resold?	Resale share	StackedBars

Every token and its value are on the data visualisation tokens page.

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