A 20% Discount Can Require Twice as Many Orders to Break Even

A 20% discount can cut contribution per order in half. When that happens, the campaign needs twice as many orders just to preserve contribution before any extra marketing expense enters the calculation.

The arithmetic is easy to miss because promotional reports usually lead with revenue, conversion rate, and orders. Each can improve while the money available to pay salaries and rent shrinks. If you approve the promotion from those metrics alone, you can reward a campaign for making the business busier at worse economics.

Consider an independent backpack retailer selling through Shopify. This is a synthetic planning example, not a report about Shopify or any named merchant. Every order contains one backpack, and we'll assume identical products, no returns, and no shipping revenue so the calculation stays visible.

Start with the dollars left per order

The backpack sells for $100. Product cost, fulfillment, payment processing, and other variable order costs total $60. That leaves $40 in contribution before marketing and fixed overhead.

A 20% discount reduces the selling price to $80. For this simplified example, the combined variable cost remains $60; a real model should adjust any fees that move with price. Contribution falls to $20 per order.

You've reduced price by one-fifth and contribution by one-half. If the retailer would ordinarily sell 1,000 backpacks during the period, baseline revenue is $100,000 and contribution before marketing is $40,000.

At the discounted price, preserving that $40,000 requires 2,000 orders because $40,000 divided by $20 equals 2,000. That's a 100% increase in orders. The required lift depends on the starting margin, so you shouldn't apply this result as a universal rule for every 20% promotion.

The general planning calculation is baseline contribution divided by discounted contribution per order. If the denominator is zero or negative, adding volume won't recover the baseline contribution within this model.

Revenue can congratulate the wrong decision

Suppose the promotion generates 1,500 orders. That's a 50% order increase, which would make an attractive headline in the campaign recap.

Revenue reaches 1,500 multiplied by $80, or $120,000, up 20% from the $100,000 baseline. Contribution before marketing reaches 1,500 multiplied by $20, or $30,000. That is $10,000 below baseline, a 25% decline.

But the team may also spend an additional $5,000 promoting the sale. Now the contribution difference after that extra campaign expense is negative $15,000. Existing marketing costs assumed to be identical between scenarios cancel out; the incremental spend needs to be included.

To preserve baseline contribution after the extra $5,000, the retailer needs $45,000 before that expense. At $20 per order, the new threshold is 2,250 orders, or 125% more than the baseline 1,000.

The promotion can still be worthwhile for a reason the model hasn't captured. Clearing inventory that would otherwise be written down, for example, changes the relevant comparison. State that objective explicitly and put its economics in the model instead of asking revenue growth to stand in for profit.

Ask what would have happened without the offer

The baseline 1,000 orders is an assumption. Last week's sales alone can't establish what would have happened during this week's promotion, especially if demand is changing with holidays, weather, or product availability.

Where practical, compare eligible customers randomly assigned to receive an offer with a comparable group that doesn't receive it. Decide the assignment rules and measurement window before launch, and consider whether customers can share the discount across groups. Spillover weakens the comparison.

Measure contribution across the whole eligible group, including people who never purchased. Looking only at customers who used the code removes nonbuyers and makes the campaign seem more productive than the original audience-level decision warrants.

You also need a window after the sale. If customers bring next month's planned purchase forward, this week's lift may be followed by a shortfall. The size and duration of that effect will vary, so report the observed follow-up period rather than claiming you've captured every future consequence.

Give acquisition its own evidence

A team may accept lower first-order contribution to acquire customers who return later. That argument needs evidence about the people acquired through the promotion: their repeat purchases, later contribution, and the cost of serving them.

Avoid crediting every discounted order as a new customer. Existing customers receiving a lower price are part of the economics, and shoppers who would have purchased at full price create a different trade-off from people the offer actually persuades to buy.

For the backpack retailer, keep the initial decision anchored to the $20 contribution per discounted order. Add later value only when you have a credible estimate and a clearly stated horizon. An optimistic repeat-purchase assumption shouldn't disappear into the spreadsheet as if it were cash already collected.

Before the next promotion, write down full-price contribution, discounted contribution, expected baseline orders, and extra campaign cost. Calculate the required order lift, then ask the campaign owner what evidence supports reaching it. That gives the approval meeting a threshold the eventual campaign report can actually test.