Merchant Revenue Forecast

Forecast e-commerce merchant revenue with AI-powered time-series simulation

The Merchant Revenue Forecast simulation models revenue trajectory over time, considering business characteristics, market dynamics, seasonality, and customer behavior.

Overview

This simulation predicts merchant revenue metrics including:

• Monthly Recurring Revenue (MRR)
• Daily revenue
• Customer count and churn rate
• Average order value
• Growth rates and market sentiment

Example

from upsonic.simulation import Simulation
from upsonic.simulation.scenarios import MerchantRevenueForecastSimulation

# Create simulation scenario
sim_object = MerchantRevenueForecastSimulation(
    merchant_name="TechCo",
    shareholders=["Alice", "Bob"],
    sector="E-commerce",
    location="San Francisco",
    current_monthly_revenue_usd=50000,
    current_customer_count=500,
    average_order_value=75.0
)

# Initialize simulation
simulation = Simulation(
    sim_object,
    model="anthropic/claude-sonnet-4-5",
    time_step="daily",
    simulation_duration=100,
    metrics_to_track=["monthly recurring revenue", "customer count"]
)

# Run simulation
result = simulation.run()

# Export summary report to JSON
result.report("summary").to_json("revenue_forecast_summary.json")

# Other available report file types (commented out):
# result.report("summary").to_csv("revenue_forecast_summary.csv")
# result.report("summary").to_html("revenue_forecast_summary.html")
# result.report("summary").to_pdf("revenue_forecast_summary.pdf")
# result.report("summary").show()

# Other available report types:
# result.report("detailed").to_json("detailed_report.json")  # Step-by-step data
# result.report("visual").to_html("charts.html")  # Interactive charts
# result.report("statistical").to_json("stats.json")  # Statistical analysis

# Save all reports at once:
# result.reports().save_all(directory="./reports", format="json")