Algo Price Dynamic Agent Skill

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