A price point is a specific price level chosen for a product or offer, usually from several possible prices. If you consider selling the same item for $39, $49 or $59, those are three candidate price points. Once you choose $49, that becomes the offer’s price point.
Choosing a level does not prove that it will produce the most profit. You still need to know how much each sale contributes after variable costs and whether enough customers will buy at that price.
Key takeaways
- Compare price points for the same product, quantity and purchase terms.
- Calculate the sales volume each price needs to reach a shared contribution target.
- Use customer research to narrow the candidates, then check actual purchasing behavior.
What does price point mean in practice?
The term emphasizes a choice among possible amounts. A price point can be $49, $50 or $49.95; it does not require a particular ending. A price range, such as $40–$60, contains several possible price points.
“High” and “low” price points depend on the offer and its market. A $59 price for one item cannot be compared fairly with $39 for a different size, quantity or service package. Define what the customer receives before comparing the numbers.
A pricing strategy provides the broader approach to setting prices across a business. A price point is the amount selected for a particular offer. For help choosing that broader approach, see our guide to ecommerce pricing strategies. The decision here is narrower: which amounts should you test for one product?
Price point example: $39, $49 or $59
Each candidate price creates a different requirement for sales volume. You can calculate that requirement before you know demand, provided you keep the assumptions explicit.
Hypothetical example: an imaginary retailer sells one item per order. Its variable cost is $28 per sale, made up of:
- $20 to buy the item, including inbound freight.
- $2 for packaging and picking.
- $4 for delivery paid by the retailer.
- $2 for payment and order processing, assumed to be a flat amount in this exercise.
The product, delivery terms and purchase conditions stay the same at all three prices. All amounts are in US dollars. Prices exclude sales tax or VAT collected for remittance, and the model assumes no discounts, returns or variable customer acquisition costs.
Subtracting $28 from the price gives the contribution per unit: the amount left to cover fixed costs and then profit. Rent, fixed salaries and software subscriptions are outside the $28. They have not been deducted from the figures below, nor has income tax. These are contribution amounts, not net profit.
The comparison uses a target contribution of $2,100 for the same hypothetical sales period.
| Price | Contribution after the stated variable costs | Units needed for at least $2,100 contribution |
|---|---|---|
| $39 | $39 − $28 = $11 | 191 |
| $49 | $49 − $28 = $21 | 100 |
| $59 | $59 − $28 = $31 | 68 |
The formula is:
Required units = ceiling(target contribution / contribution per unit)
$39: $2,100 / $11 = 190.909... → 191 units
$49: $2,100 / $21 = 100 → 100 units
$59: $2,100 / $31 = 67.741... → 68 units
Ceiling means rounding up to the next whole unit whenever there is a fraction. Rounding to 190 or 67 would leave the target unmet. The rounded volumes produce contributions of $2,101, $2,100 and $2,108, respectively.
The $2,100 target is an arithmetic reference: 100 sales at $49 would produce that contribution. Those 100 sales have not happened. Neither the target nor the other volumes represent observed demand, a forecast or a SkuPeek customer result.
For more detail on cost, markup and margin calculations, see wholesale vs. retail price.
What the required sales volumes tell you
The table gives each candidate a question to answer. Could the product sell 191 units at $39, 100 at $49 or 68 at $59 within the same period and comparable selling conditions?
The $59 option needs the fewest sales to reach the target, but there is no evidence that 68 buyers will accept it. Likewise, the $39 option cannot rely on an assumed surge in orders to offset its lower contribution per sale. None of the three prices is a demonstrated winner.
A sales requirement is not a demand forecast. The calculation tells you what must happen for a candidate to reach the target. Research and sales evidence help you judge whether it can happen.
The comparison also depends on its costs. Percentage-based payment fees, returns, marketplace commissions or extra advertising can change the amount each sale contributes. Recalculate variable costs for each scenario when they differ. If contribution per unit is zero or negative, no sales volume will reach a positive contribution target under that model.
How to choose price points worth testing
Start with an objective, a defined offer and evidence that makes each candidate worth considering.
Set an objective and a review period
Decide what the price needs to achieve. For an ongoing product, that might be a contribution target over a month. For stock with a deadline, it could include a minimum number of units sold by that date.
Keep the objective separate from the forecast. Writing down a target of $2,100 does not make it achievable. Also decide which constraints matter, such as available stock, fulfillment capacity and the budget needed to attract buyers.
Check the economics of each candidate
Use the amount the retailer actually retains from the sale, after discounts and excluding tax collected for remittance. Include the variable expenses associated with that order.
A positive contribution is only a starting condition. Across the relevant period, total contribution must also cover fixed costs before the business earns an operating profit. The same unit calculation can therefore support different decisions depending on the costs and objective you enter.
Compare equivalent offers in the same market
Competitor prices help you understand the alternatives a shopper can choose. Match the product and variant, pack size, condition, currency, availability and delivery terms. Record when you observed each offer; a temporary promotion may provide a different reference from a regular price.
Use price benchmarking to put the candidates in context, while keeping the demand question open. A competitor’s advertised price does not reveal its sales volume or show what your customers will pay. A market median is also a comparison point, not a proven optimum for your store.
Choose a research method for the question you need answered
Customer research can help narrow the candidates. The method should match the uncertainty: an acceptable range, reactions to specific prices, or trade-offs between price and features.
| Method | Question it helps explore | Limit of the evidence |
|---|---|---|
| Van Westendorp | Which prices feel suspiciously cheap, good value, expensive or too expensive? | Describes perceived acceptability; does not establish purchase volume. |
| Gabor-Granger | How does stated willingness to buy change across specified prices? | Results depend on the sample, questions and tested prices; stated demand is not recorded sales. |
| Conjoint analysis | How do people trade price against features or other offer attributes? | Models choices among defined alternatives; cannot guarantee market behavior. |
| Monadic price testing | How do separate groups respond when each sees the same offer at only one price? | Avoids showing every respondent all candidates, but survey responses still require behavioral validation. |
For the unchanged product in the example, a study of specific prices may answer the immediate question more directly than a study that varies product features. Conjoint becomes relevant when the offer itself is also under consideration.
A research tool may label an output an “optimal price point.” Read that as a result within its method and assumptions. It is not proof of the price that maximizes your store’s profit.
Recruit people who resemble the intended buyers and describe the complete offer consistently. Use their answers to decide what to test; a stated intention to purchase is not a completed order.
Validate the choice with purchasing behavior
A purchase test observes what customers actually do at the tested prices. Design it so that changes in the audience or offer do not overwhelm the price comparison.
Before running live price experiments, check applicable consumer rules and make sure the test does not create inconsistent or misleading pricing across the purchase journey.
For a controlled test, randomly assign comparable eligible shoppers to price groups where feasible. Keep each shopper’s assigned price consistent through checkout and return visits. Hold the product, delivery terms, page content and promotional treatment constant.
Before starting, specify:
- The candidates and costs. Record the price and applicable variable costs for each group.
- The main outcome. Track contribution per eligible visitor alongside orders and conversion. This helps compare groups receiving different amounts of traffic.
- The evaluation plan. Set the sample requirements, decision rule and stopping conditions in advance, allowing for delayed purchases and returns.
- The operating checks. Record stockouts, channel changes and fulfillment problems that could distort the comparison.
Changing the price one week and comparing sales with the previous week provides weaker evidence of a price effect. Traffic, promotions, seasonality and competitor availability may have changed too. Record those differences if a controlled test is impractical.
Even a well-run test compares a finite set of candidates in particular conditions. An inconclusive result does not establish that the prices perform equally. The best-supported candidate also remains a choice among the tested options, rather than proof of a universal optimum.
Choose the price point supported by the economics and the purchasing evidence available. Record the assumptions, remaining uncertainty and next review date. Revisit the choice when costs, the offer or market conditions materially change.
See how your own catalog compares with the market
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