AgentSkillsCN

Calculate Hotel Revenue

计算酒店收入

SKILL.md

Skill: Calculate Hotel Revenue Management

Domain

travel_leisure

Description

Optimizes hotel room pricing and inventory allocation using demand forecasting, competitive positioning, and revenue management algorithms.

Tags

hospitality, revenue-management, pricing, demand-forecasting, travel

Use Cases

  • •Dynamic pricing optimization
  • •Demand forecasting
  • •Inventory allocation by channel
  • •Competitive rate analysis

Proprietary Business Rules

Rule 1: Demand Forecasting

Historical booking patterns with day-of-week and seasonal adjustments.

Rule 2: Price Elasticity

Room type and segment-specific price sensitivity curves.

Rule 3: Minimum Length of Stay

MLOS rules based on demand intensity periods.

Rule 4: Channel Mix Optimization

Rate parity and channel allocation strategies.

Input Parameters

  • •property_id (string): Hotel property identifier
  • •room_type (string): Room category
  • •stay_date (string): Date for pricing
  • •current_occupancy (float): Current booking level
  • •competitor_rates (list): Competitive rate data
  • •days_out (int): Days until stay date
  • •demand_indicators (dict): Events, seasonality flags
  • •channel (string): Distribution channel

Output

  • •recommended_rate (float): Optimal room rate
  • •rate_range (dict): Min/max rate bounds
  • •occupancy_forecast (float): Expected occupancy
  • •revenue_forecast (float): Expected revenue
  • •pricing_strategy (string): Strategy recommendation

Implementation

The revenue logic is implemented in revenue_manager.py and references demand curves from CSV files:

  • •room_types.csv - Reference data
  • •channels.csv - Reference data
  • •booking_curves.csv - Reference data
  • •seasonality.csv - Reference data
  • •strategies.csv - Reference data
  • •parameters.csv - Reference data.

Usage Example

python
from revenue_manager import calculate_revenue

result = calculate_revenue(
    property_id="HOTEL-001",
    room_type="standard_king",
    stay_date="2026-02-14",
    current_occupancy=0.45,
    competitor_rates=[189, 199, 175, 209],
    days_out=21,
    demand_indicators={"special_event": True, "season": "high"},
    channel="direct"
)

print(f"Recommended Rate: ${result['recommended_rate']}")

Test Execution

python
from revenue_manager import calculate_revenue

result = calculate_revenue(
    property_id=input_data.get('property_id'),
    room_type=input_data.get('room_type'),
    stay_date=input_data.get('stay_date'),
    current_occupancy=input_data.get('current_occupancy', 0),
    competitor_rates=input_data.get('competitor_rates', []),
    days_out=input_data.get('days_out', 30),
    demand_indicators=input_data.get('demand_indicators', {}),
    channel=input_data.get('channel', 'ota')
)