Skill: Optimize Product Pricing

Domain

consumer_products

Description

Calculates optimal product pricing using demand elasticity models, competitive positioning analysis, and margin optimization algorithms specific to consumer product categories.

Tags

consumer-products, pricing-strategy, revenue-optimization, demand-modeling, margin-analysis

Use Cases

• •New product price setting
• •Promotional pricing optimization
• •Competitive price response
• •Channel-specific pricing

Proprietary Business Rules

Rule 1: Price Elasticity Application

Category-specific elasticity curves determine price-demand relationships for optimal revenue calculation.

Rule 2: Competitive Position Bands

Price positioning relative to competitors based on brand tier and product attributes.

Rule 3: Channel Margin Requirements

Different margin requirements for retail, e-commerce, and wholesale channels.

Rule 4: Promotional Lift Modeling

Price reduction impact on volume with diminishing returns above threshold discounts.

Input Parameters

• •product_id (string): Product identifier
• •product_category (string): Product category
• •base_cost (float): Product unit cost
• •competitor_prices (list): Competitor price points
• •brand_tier (string): Premium, mainstream, value
• •sales_channel (string): Retail, ecommerce, wholesale
• •current_price (float): Current selling price
• •target_margin (float): Target profit margin percentage

Output

• •optimal_price (float): Recommended price point
• •expected_margin (float): Expected profit margin
• •price_band (dict): Min/max price range
• •elasticity_impact (dict): Volume impact projections
• •competitive_position (string): Position vs competitors

Implementation

The pricing logic is implemented in pricing_optimizer.py and references elasticity data from CSV files:

• •categories.csv - Reference data
• •brand_tiers.csv - Reference data
• •channels.csv - Reference data
• •parameters.csv - Reference data.

Usage Example

from pricing_optimizer import optimize_pricing

result = optimize_pricing(
    product_id="SKU-12345",
    product_category="personal_care",
    base_cost=4.50,
    competitor_prices=[8.99, 9.49, 7.99],
    brand_tier="mainstream",
    sales_channel="retail",
    current_price=8.99,
    target_margin=0.40
)

print(f"Optimal Price: ${result['optimal_price']}")

Test Execution

from pricing_optimizer import optimize_pricing

result = optimize_pricing(
    product_id=input_data.get('product_id'),
    product_category=input_data.get('product_category'),
    base_cost=input_data.get('base_cost'),
    competitor_prices=input_data.get('competitor_prices', []),
    brand_tier=input_data.get('brand_tier', 'mainstream'),
    sales_channel=input_data.get('sales_channel', 'retail'),
    current_price=input_data.get('current_price'),
    target_margin=input_data.get('target_margin', 0.30)
)