Dynamic Pricing

Overview

Dynamic pricing adjusts prices in real-time based on demand signals, time, inventory, and competitive conditions. Common in airlines, hotels, ride-sharing, and e-commerce. Objective: maximize revenue (or profit) subject to capacity/inventory constraints.

When to Use

Trigger conditions:

Pricing perishable inventory (hotel rooms, airline seats, event tickets)
Implementing demand-responsive pricing for e-commerce
Building surge pricing or time-based pricing systems

When NOT to use:

For one-time pricing decisions (use Van Westendorp or conjoint)
When price changes are impractical (regulated markets, long-term contracts)

Algorithm

IRON LAW: Dynamic Pricing Requires REAL-TIME Data
Stale data produces prices optimal for PAST conditions, not current ones.
Three data streams must be current:
1. Demand signal (bookings, searches, cart additions)
2. Inventory/capacity status
3. Competitive prices (where applicable)
Update frequency: minutes for ride-sharing, hours for hotels, daily for retail.

Phase 1: Input Validation

Collect: current demand indicators, remaining inventory/capacity, time until expiration/event, competitor prices, price floor/ceiling constraints. Gate: Real-time data feeds connected, business rules defined.

Phase 2: Core Algorithm

Rule-based: If demand > threshold, increase price by X%. Tiered rules by inventory level.

Demand-curve based: 1. Estimate demand curve at current conditions. 2. Find price that maximizes revenue = P × Q(P). 3. Apply inventory constraint: if capacity is scarce, price up; if excess, price down.

ML-based: Train model to predict demand at each price point given context features. Optimize over predicted demand curve.

Phase 3: Verification

Monitor: revenue per unit, booking pace, customer complaints, competitive position. A/B test new pricing rules. Gate: Revenue improved without significant volume loss or customer backlash.

Phase 4: Output

Return recommended price with reasoning and expected impact.

Output Format

{
  "recommended_price": 1200,
  "current_price": 999,
  "reasoning": {"demand_signal": "high", "inventory_remaining_pct": 15, "competitor_avg": 1100},
  "expected_impact": {"revenue_change_pct": 18, "volume_change_pct": -5},
  "metadata": {"strategy": "demand-curve", "update_frequency": "hourly"}
}

Examples

Sample I/O

Input: Hotel room, 3 days until date, 85% occupancy, average competitor price $150 Expected: Price above competitor ($160-170) due to high occupancy, short time horizon.

Edge Cases

Input	Expected	Why
Zero demand	Drop to floor price	Stimulate demand, recover some revenue
Last unit available	Price near ceiling	Scarcity maximizes willingness to pay
Competitor flash sale	Don't auto-match if unnecessary	Avoid price war; assess if your product differentiates

Gotchas

Customer fairness perception: Visible price discrimination (same product, different prices for different users) generates backlash. Segment by time, channel, or bundle — not by individual.
Price war spiraling: Automated competitive pricing can create a race to the bottom. Set absolute floors and rate-of-change limits.
Demand cannibalization: If customers learn prices drop later, they wait. This is the "strategic customer" problem — don't train customers to delay.
Regulatory risk: Dynamic pricing may violate anti-gouging laws during emergencies. Build in legal constraint rules.
A/B testing bias: Testing different prices creates revenue measurement challenges. The control group at the "wrong" price loses money by design.

References

For revenue management models (airline/hotel), see references/revenue-management.md
For fairness constraints in dynamic pricing, see references/fairness-constraints.md

algo-price-dynamic