Demand-responsive pricing.

Dynamic pricing adjusts product price based on demand, inventory, and competitive position. Tools like airline pricing engines, hotel revenue management, and ride-hailing surge pricing all use variants. This calculator shows how demand index (100 = baseline, 150 = high demand) and inventory level (100% = full stock, 30% = nearly sold out) should nudge price around a base.

100 base × demand index 120 (20% above normal) × sensitivity 10% = +2% adjustment = 102. Plus inventory at 50% (half gone) × sensitivity 5% = +2.5% = 104.55. Modest adjustment but compounds in high-demand scenarios. Rent-a-car pricing can swing ±40% through the week using these mechanics.

Dynamic pricing works for perishable inventory (seats, rooms, rentals) and commodity products with active competitors. It struggles for relationship-based sales and subscription businesses where customers notice and resent price changes. Retail pricing changes weekly rather than hourly generally works; hourly changes train customers to time purchases.

Run it with sensible defaults

Using base price of 100, demand index of 120, competitor price of 0, inventory level of 50%, the calculation works out to 104.50. Nudge the inputs toward your own situation and the output recalculates instantly. The defaults are meant as a starting point, not a recommendation.

The levers in this calculation

The inputs — Base Price, Demand Index, Competitor Price, Inventory Level %, and Price Sensitivity % — do not pull with equal force. Not every input has equal weight. Flip one at a time toward extreme values to feel which ones move the needle most for your situation.

How the math works

Demand adj = (demand - 100) ÷ 100 × sensitivity. Inventory adj = (100 - inventory) ÷ 100 × sensitivity ÷ 2. Price = base × (1 + demand + inventory), capped within 80-120% of competitor. The working is transparent — you can verify every step yourself in the formula section below. No black box, no opaque "proprietary model".

What to do with a low result

A disappointing result is information, not a judgement. Pick the single input that dragged the figure down most and focus the next quarter on that one factor. Breadth-first improvement rarely works; depth-first on the worst input usually does.

What this doesn't capture

The score is a composite of the inputs you provide. Life context — job security, family obligations, health, housing — doesn't appear in the math but shapes the real picture. Use the number as a prompt, not a verdict.