Data Science and Analytics Tools - Dynamic Pricing Strategy Optimization

 Notes:

• What problem does it solve?
  Helps businesses dynamically adjust prices based on demand, competition, and other factors, optimizing revenue and market share.

• How can businesses or users benefit from customizing the code?
  Businesses can adjust the factors used for pricing (e.g., competitor pricing, demand elasticity) and test different pricing models.

• How can businesses or users adopt the solution further, if needed?
  The solution can be integrated into e-commerce platforms, offering real-time dynamic pricing based on market conditions.

Actual Python Code:

import pandas as pd

from scipy.optimize import linprog

# Define the parameters (assumed to have columns 'Cost', 'Demand', 'Competitor_Price')

data = pd.read_csv('pricing_data.csv')

# Set the objective function coefficients (maximize revenue = Price * Demand)

objective = -data['Demand']  # Negative for maximization in linprog

# Set the constraints (e.g., price must be higher than cost and within a range)

lhs = [[1], [-1]]  # Price >= Cost, Price <= Competitor_Price

rhs = [data['Cost'].mean(), data['Competitor_Price'].mean()]

# Optimize price using linear programming

result = linprog(c=objective, A_ub=lhs, b_ub=rhs, method='highs')

# Display the optimal price

optimal_price = result.x[0]

print(f'Optimal Price: ${optimal_price:.2f}')