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Discount Calculator — Sale Price & Percent Off Online

Last verified May 2026 — runs in your browser
Calculate sale price after discount
%
You Save
$24
Final Price
$96
Discount
20% off
Use this result in: Tax VAT Tip Margin

Common Discounts

Discount Formula

Savings = Original Price × (Discount% / 100) • Final Price = Original − Savings

120 × (20 / 100) = 24 saved • Final: $96

Discount Calculator — Calculate Sale Price & Savings Online

Type the original price and a discount percentage (or pick a preset: 10%, 20%, 25%, 30%, 40%, 50%, 70% off — the most common sale tiers in retail) and the page returns the sale price plus the savings amount. Useful for sanity-checking a Black Friday claim, comparing two sale prices on the same item across stores, computing the real cost after a coupon code, or settling whether a 30%-off membership tier is actually cheaper than the supposedly-better 25%-off one (often it's not, depending on what's stacked). Stacked discounts (e.g. 30% off PLUS additional 20%) are NOT additive — a 30% discount followed by a 20% discount is 1 − 0.7 × 0.8 = 44% effective, not 50%; the tool handles single-discount math but knowing the stacking rule prevents over-estimation.

About this tool

Math: sale_price = original × (1 − discount/100), savings = original − sale_price. So a $100 jacket at 25% off becomes $75 with $25 saved. Discount stacking (when a coupon stacks on top of a percent-off-the-tag price) compounds multiplicatively: 30% off + 20% extra coupon = original × 0.7 × 0.8 = original × 0.56, equivalent to 44% off, not the intuitive 50%. Most retail "40% off when you spend $100" tier promotions stack OVER the percent-off-tag, so the savings are smaller than they appear; the tool's preset buttons stop at 75% because past that you're usually looking at clearance final-sale or close-out math (which behaves the same way mathematically). Reverse-direction useful trick: if you're advertised a final price and want to know the original, compute original = final / (1 − discount/100). Useful for retail shopping audit, deal-comparison shopping, marketing-claim verification, store coupon stacking decisions.

  • Quick discount presets at 10/20/25/30/40/50/70% — common retail tiers
  • Custom percentage input for any non-standard discount
  • Returns sale price + savings amount + savings as % of original
  • Step-by-step formula breakdown shown under the result
  • Reactive — recalcs as you change price or discount
  • Documents the multiplicative-stacking rule (30% + 20% ≠ 50%, it's 44%)
  • Decimal currency precision for rounded retail prices
  • Copy result with one click
  • No upload — your price negotiations stay in your browser
  • Useful for retail audit, BOGOF math, coupon stacking, marketing claim checks

Free. No signup. Your inputs stay in your browser. Ads via Google AdSense (consent required).

Frequently asked questions

How does the (1 − discount/100) sale-price formula work?

Sale price = original × (1 − discount/100); savings = original × discount/100; the two together equal the original. Worked example: 30% off €80 gives sale price = 80 × (1 − 0.30) = 80 × 0.70 = €56 with €24 savings. The percent symbol % is dimensionless per ISO 80000-1:2022 (1 % = 0.01), so the math is unit-agnostic and works the same whether prices are in euros, dollars, yen, or pesos. The page rounds to two decimals (currency precision) and supports decimal discount inputs (e.g., 12.5% off, 33.7% off) for cases where retailers advertise non-round-number reductions.

Why does stacking 20% + 10% give 28%, not 30%?

Discounts compose multiplicatively, not additively: a 20% store discount followed by an extra 10% loyalty coupon equals 0.80 × 0.90 = 0.72, meaning a 28% total reduction from the original price. The naïve "add the percentages" approach (20% + 10% = 30%) overstates the effective discount by 2 percentage points because the 10% coupon applies to the already-discounted price, not the original. Generalisation: stacking N discounts d₁, d₂, ... dₙ yields effective total = 1 − ∏(1 − dᵢ/100). The error compounds with more layers: 20% + 10% + 5% naively = 35% but actually = 1 − 0.80 × 0.90 × 0.95 = 31.6%. The discount-calculator handles arbitrary stacking automatically so users avoid the common pricing error.

What rules govern advertised discounts in regulated jurisdictions?

United States: FTC 16 CFR Part 233 Guides Against Deceptive Pricing remain codified (rarely enforced by the FTC since the 1970s, but FTC Act § 5 unfair-or-deceptive-practices authority applies regardless), plus 16 CFR Part 238 Bait Advertising (originally 8 November 1967). European Union: Price Indication Directive 98/6/EC Article 6a (introduced by Omnibus Directive 2019/2161, effective 28 May 2022) requires any advertised "reduction" reference the lowest price applied during the preceding 30 days, preventing the inflated-price-then-discount tactic common before the rule. United Kingdom: post-Brexit, the Consumer Protection from Unfair Trading Regulations 2008 (CPRs) and CMA Pricing Practices guidance enforce equivalent reference-price genuineness requirements — "was £X, now £Y" must reflect a real prior selling price recently in force.

Why is a 100% markup equivalent to a 50% discount, not 100%?

Markup and discount use different bases: markup multiplies the cost upward to the selling price, while discount multiplies the selling price downward to the sale price. Pricing a €50 item with a 100% markup: selling price = 50 × (1 + 100/100) = €100. Discounting that €100 selling price by 50%: sale price = 100 × (1 − 50/100) = €50 — back to cost. So a 100% markup is exactly reversed by a 50% discount, NOT a 100% discount. The asymmetry confuses small-business pricing decisions and surfaces in retail tactics like "buy one get one half off" (BOGO 50%) which is a 25% effective discount on the pair, not 50%. The sister markup-calculator (also in this portfolio) computes the inverse direction explicitly.

Why do retailers advertise prices ending in .99 (charm pricing)?

Prices ending in .99 (or .95, .49) — "charm pricing" or "odd pricing" — exploit a left-digit bias: shoppers anchor on the leading digit and perceive €9.99 as substantively cheaper than €10.00 even though the difference is one cent. Anderson (Chicago GSB) & Simester (MIT Sloan) 2003 retail field experiments published in Quantitative Marketing and Economics 1(1), 93-110, showed about a one-third increase in unit sales when items were repriced from $34 to $39 (no demand difference between $34 and $44 in companion conditions), and the effect held when the .99 ending was bundled with sale signage. Thomas & Morwitz 2005, Journal of Consumer Research 32(1), 54-64, decomposed the left-digit-effect bias into multiple cognitive shortcuts. The discount-calculator does not nudge users toward charm pricing — it shows exact arithmetic — but understanding the anchor lets buyers compare two seemingly different reductions ("25% off €40" vs "€29.99 now") on equal footing.

Sources (7)
  • International Organization for Standardization (2022). ISO 80000-1:2022 — Quantities and units, Part 1: General; defines the percent symbol % as a dimensionless ratio (1 % = 0.01) underlying the (1 − discount/100) sale-price formula and multiplicative discount-stacking arithmetic. ISO Technical Committee 12 (TC 12) Quantities and units; supersedes ISO 80000-1:2009 + ISO 31-0.
  • U.S. Federal Trade Commission (1967). FTC 16 CFR Part 233 Guides Against Deceptive Pricing (still codified, rarely enforced by the FTC since the 1970s) + 16 CFR Part 238 Guides Against Bait Advertising (originally published 8 November 1967, codified at 32 FR 15540) — federal enforcement authority retained via FTC Act § 5 unfair-or-deceptive-practices regardless of the Guides' enforcement posture. Federal Trade Commission Act § 5 (15 U.S.C. § 45); 16 CFR Part 233 originally promulgated 1958, Part 238 originally 1967.
  • European Parliament & Council (2019). EU Price Indication Directive 98/6/EC Article 6a (introduced by Omnibus Directive 2019/2161, effective 28 May 2022) — any advertised price reduction must reference the lowest price applied during the preceding 30 days; designed to prevent the inflated-price-then-discount tactic common before the rule. Directive (EU) 2019/2161 of the European Parliament and of the Council of 27 November 2019 amending PID 98/6/EC + Consumer Rights Directive 2011/83/EU + UCPD 2005/29/EC + UCT 93/13/EEC.
  • UK Chartered Trading Standards Institute (CTSI) + Competition & Markets Authority (CMA) (2016). Guidance for Traders on Pricing Practices (CTSI 2016, replacing the 2010 BIS Pricing Practices Guide) + Consumer Protection from Unfair Trading Regulations 2008 (CPUTR, SI 2008/1277) — UK enforcement of the unfair-commercial-practices framework post-Brexit; reference-price comparisons ("was £X, now £Y") must reflect a genuine prior selling price recently in force; CMA209 Price Transparency guidance applies under the Digital Markets, Competition and Consumers Act 2024. CTSI 2016 guidance on behalf of BEIS; CPUTR 2008 (SI 2008/1277) implementing Unfair Commercial Practices Directive 2005/29/EC; retained EU law post-Brexit via the European Union (Withdrawal) Act 2018.
  • Anderson, E. T., & Simester, D. I. (2003). Effects of $9 Price Endings on Retail Sales: Evidence from Field Experiments — Quantitative Marketing and Economics 1(1), 93-110; controlled mail-order catalogue field experiments (Anderson at University of Chicago Graduate School of Business; Simester at MIT Sloan School of Management) showed about a one-third increase in unit sales when items were repriced from $34 to $39 (no demand difference between $34 and $44 in companion conditions); foundational evidence for charm pricing / left-digit bias. Quantitative Marketing and Economics 1(1) (Springer/Kluwer), pp. 93-110, 2003.
  • Thomas, M., & Morwitz, V. (2005). Penny Wise and Pound Foolish: The Left-Digit Effect in Price Cognition — Journal of Consumer Research 32(1), 54-64; decomposed the left-digit effect into multiple cognitive shortcuts (anchoring on leading digit, magnitude representation, perceived savings) explaining charm-pricing efficacy beyond the simple round-down hypothesis. Journal of Consumer Research 32(1) (Oxford University Press for the Association for Consumer Research), pp. 54-64, June 2005; authors at Cornell University + New York University Stern School of Business.
  • World Wide Web Consortium (W3C) (2018). Web Content Accessibility Guidelines (WCAG) 2.1 — Success Criterion 4.1.3 Status Messages. W3C Recommendation 5 June 2018; carried unchanged into WCAG 2.2 (Recommendation 5 October 2023).

These are the original publications the formulas in this tool are based on. Locate them by journal name and year on Google Scholar or PubMed.