What it means
At its simplest a demand schedule has two columns: a price, and the quantity buyers would take at that price over a stated period. Everything else is assumed to stay still, which is exactly what makes the comparison between rows meaningful.
There are two versions worth separating. An individual demand schedule covers a single buyer, while a market demand schedule adds up every buyer's quantity at each price, and it is the market version that matters for a pricing decision.
Multiplying each price by its quantity turns the schedule into a revenue table, and that is where it earns its keep. Revenue usually rises as price falls up to a point and then falls again, so the schedule shows roughly where that peak sits.
Real businesses rarely have a neat schedule handed to them. They build one from past price changes, from tests on a website, from customer surveys and from competitor price points, accepting that the result is an estimate with a range rather than a precise line.
The important nuance is what a schedule does not capture. It holds income, tastes, competitor pricing and the wider economy constant, so when any of those move the whole schedule shifts and yesterday's table stops being a reliable guide.
In practice
Real-world examples.
Example
A cinema chain builds a demand schedule for Tuesday evenings from three years of ticket data. It finds that dropping the price from $14 to $9 more than doubles attendance, and that concession spending rises with the extra footfall.
Example
A parts distributor sets volume tiers by reading its own order history as a demand schedule. Orders cluster just above each discount threshold, which tells the sales director where buyers are genuinely price sensitive.
Example
A boutique hotel builds a separate demand schedule for weekdays and weekends. Weekday business travellers barely react to a $20 rate change, while weekend leisure guests react sharply, so the hotel prices the two segments differently.
Formula
Calculation
Revenue at each price = price x quantity. Price elasticity between two rows = percentage change in quantity / percentage change in price.
A subscription software company tests five monthly price points and estimates the following demand schedule, with revenue calculated for each row.
At $30: 200 subscribers, revenue 30 x 200 = $6,000.
At $25: 300 subscribers, revenue 25 x 300 = $7,500.
At $20: 420 subscribers, revenue 20 x 420 = $8,400.
At $15: 520 subscribers, revenue 15 x 520 = $7,800.
At $10: 600 subscribers, revenue 10 x 600 = $6,000.
Revenue peaks at $20 with $8,400 a month. Between $25 and $20, quantity rises by 120 / 300 = 40% while price falls by 5 / 25 = 20%, giving an elasticity of 40 / -20 = -2.0, so demand is elastic there and cutting price lifts revenue.
Lower down the table the picture reverses. Between $15 and $10, quantity rises by 80 / 520 = 15.4% while price falls by 5 / 15 = 33.3%, an elasticity of about -0.46, so demand is inelastic and the price cut destroys revenue, taking it from $7,800 down to $6,000.Case study
Seen in the real world.
Halcyon Garden Tools is an illustrative, invented manufacturer used to show why revenue and profit peak in different places. Preparing to launch a pruning set, the team estimated a seasonal demand schedule: $60 would sell 1,200 units for revenue of $72,000, $50 would sell 1,800 for $90,000, $40 would sell 2,300 for $92,000, and $30 would sell 2,700 for $81,000.
The sales director argued for $40, since it produced the highest revenue at $92,000. The finance manager added the unit cost of $22 and recalculated on contribution instead. At $60 the contribution was 38 x 1,200 = $45,600, at $50 it was 28 x 1,800 = $50,400, at $40 it was 18 x 2,300 = $41,400, and at $30 it was only 8 x 2,700 = $21,600.
Halcyon launched at $50. It gave up $2,000 of revenue against the $40 option but earned $9,000 more contribution, shipped 500 fewer units, and used less factory capacity in its busiest season. The team now builds every schedule with a contribution column beside the revenue column.
Watch out
Common mistakes.
- Optimising the schedule for revenue when contribution after variable costs is what actually pays the bills.
- Treating an estimated schedule as fact, when most are built from limited data and deserve to be shown as a range.
- Confusing a shift of the whole schedule, caused by a change in incomes or competitors, with a movement along it caused by your own price change.
Questions
People also ask.
What is the difference between a demand schedule and a demand curve?
None in substance; the schedule is the table of numbers and the curve is the same numbers plotted on a chart.
How do I build one without years of history?
Use structured price tests, customer willingness-to-pay surveys, competitor price points and any past promotions, then treat the result as a working estimate to refine.
Does a demand schedule apply to services and subscriptions?
Yes, and it is often easier to build there, because online pricing tests give clean readings of how sign-ups respond to each price point.
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