A/B Price Test Sample Size Calculator
Calculate the minimum sample size needed for a statistically significant A/B price test. Set confidence level, power, and minimum detectable effect.
Optimize multi-step markdown schedules to maximize total revenue. Model demand response at each markdown level and find the best cadence.
| Step | Price | MD% | Sold | Revenue | Profit | Cum. Profit | Remaining | |
|---|---|---|---|---|---|---|---|---|
| Step 1 | $79.99 | — | 80.00 | $6,399.20 | $3,999.20 | $3,999.20 | 120.00 | |
| Step 2 | $59.99 | 25% | 60.00 | $3,599.40 | $1,799.40 | $5,798.60 | 60.00 | |
| Step 3 | $39.99 | 50% | 40.00 | $1,599.60 | $399.60 | $6,198.20 | 20.00 | OPTIMAL STOP |
| Step 4 | $24.99 | 68.8% | 20.00 | $499.80 | -$100.20 | $6,098.00 | 0.00 | BELOW COST |
Markdown optimization is the science of determining the right sequence of price reductions to sell through inventory while maximizing total revenue. Rather than guessing markdown depth, retailers can model how demand changes at each price point and find the schedule that produces the highest total gross profit.
Our Markdown Optimization Calculator lets you define up to 6 markdown steps, each with a price and estimated units sold at that price. It calculates total revenue, gross profit, and sell-through at each stage, then highlights the optimal stopping point — the step where continuing to mark down further would reduce cumulative profit.
This calculator is designed for retail merchandisers, inventory planners, and pricing analysts who need to plan seasonal markdown cadences, liquidation schedules, or clearance strategies with a data-driven approach.
Use the result to compare scenarios, test assumptions, and revisit the model when pricing, volume, or financing inputs change.
Unoptimized markdowns are one of the largest profit leaks in retail. Studies show that retailers lose 10–15% of potential profit through poor markdown timing and depth. By modeling different markdown scenarios before committing, you can find the schedule that balances sell-through velocity with profit preservation — clearing inventory without giving away more margin than necessary.
Step Revenueᵢ = Priceᵢ × Units Soldᵢ
Step Profitᵢ = (Priceᵢ − Cost) × Units Soldᵢ
Cumulative Profit = Σ Step Profitᵢ
Optimal Stop = step where Cumulative Profit is maximized
Sell-Through % = Total Units Sold ÷ Initial Inventory × 100Result: Optimal stop at Step 3 — $6,598 cumulative profit
Step 1: 80 × ($79.99 − $30) = $3,999 profit. Step 2: 60 × ($59.99 − $30) = $1,799 profit (cumulative $5,798). Step 3: 40 × ($39.99 − $30) = $400 profit (cumulative $6,198). Step 4: 20 × ($24.99 − $30) = −$100 loss (cumulative $6,098). The optimal stopping point is Step 3, which achieves 90% sell-through at maximum cumulative profit.
Many retailers fall into a markdown spiral: small initial discounts fail to move inventory, leading to progressively deeper cuts, customer wait-and-see behavior, and margin destruction. By modeling the full markdown path upfront, you can avoid this trap and commit to a schedule that maximizes total value recovered.
Not all products respond equally to markdowns. Elastic products (fashion, seasonal decor) see sharp demand increases at each price reduction. Inelastic products (basics, essentials) see modest demand changes. Understanding your product's elasticity determines whether shallow, frequent markdowns or deep, infrequent ones maximize recovery.
Markdown planning should inform buying decisions. If your markdown analysis shows that 25% of revenue will come from marked-down inventory, plan your initial buy to account for that. This creates a feedback loop: better markdown data leads to better buying, which reduces the need for markdowns in the first place.
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Markdown optimization is the process of determining the ideal sequence and depth of price reductions to maximize total revenue or profit while clearing inventory within a defined time window. It balances the competing goals of selling fast and preserving margin.
Stop when the next markdown step would sell below cost (negative profit contribution) or when the incremental revenue doesn't justify the margin erosion. This calculator identifies that point automatically.
Use historical data from similar products, category sell-through rates at various discount depths, or price elasticity estimates. If data is limited, assume that each 10% price reduction increases demand by 20–40% — a common rule of thumb in fashion and general merchandise.
Generally, fewer, bolder markdowns outperform "drip" markdowns. One decisive cut of 30% often moves more units and generates more total profit than sequential cuts of 10%, 15%, 20%. However, the optimal strategy depends on your specific demand curve.
Unsold inventory can be liquidated to off-price retailers (typically 10–20% of cost), donated for tax benefits, bundled with full-price items, or held for future seasons. Include these recovery values in your final step calculation.
Holding costs (warehousing, capital cost, obsolescence risk) mean that unsold inventory gets more expensive over time. Include a weekly or monthly holding cost estimate to make the comparison between long, gentle markdowns vs. quick, deep ones more accurate.
Calculate the minimum sample size needed for a statistically significant A/B price test. Set confidence level, power, and minimum detectable effect.
Calculate the minimum price needed to cover all costs. Enter fixed costs, variable costs, and expected sales volume to find your break-even price per unit and profitability at different price points.
Calculate optimal bundle pricing with discount analysis. Enter individual product prices, set a bundle discount, and see revenue impact, perceived savings, and break-even volume increases.