Oasis Browser 2026 Sensitivity Analysis Model

A comprehensive financial model and sensitivity analysis tool for Oasis Browser's 2026 projections, built in Python.

Purpose & Scope

Purpose: This model enables rapid sensitivity analysis for Oasis Browser's 2026 financial planning, allowing quick assessment of how changes in key business drivers affect revenue, margins, cash flow, and runway.

Scope: The model covers:

• B2C Subscriptions: Monthly recurring revenue from individual users ($20/month, ~1500 text or 150 voice prompts included)
• B2B Pilots: Annual contracts with enterprise clients ($50k/year average, 250 included developer hours)
• Operating Expenses: Headcount, fixed overhead, and infrastructure costs
• Cash Flow & Runway: Monthly cash flow projections and months of runway calculations

Time Period: Full year 2026 (12 months, January through December)

Outputs: All monetary values are in USD. Time periods are monthly for B2C/operating, annual for B2B (distributed monthly).

Overview

This model projects financial performance for Oasis Browser across two revenue streams:

• B2C Subscriptions: Monthly recurring revenue from individual users
• B2B Pilots: Annual contracts with enterprise clients

The model calculates revenue, costs, gross margins, cash flow, and runway under various scenarios and sensitivity assumptions.

Quick Start

Installation

pip install -r requirements.txt

Run the Analysis

Run all analyses:

python run_analysis.py

Run specific scenario only:

python run_analysis.py --scenario base
python run_analysis.py --scenario conservative
python run_analysis.py --scenario aggressive

Skip Excel export:

python run_analysis.py --no-export

Custom output filename:

python run_analysis.py --output my_analysis.xlsx

Combined options:

python run_analysis.py --scenario aggressive --no-export

This will:

1. Display base case 2026 projections
2. Run one-way sensitivity analyses on key drivers
3. Run two-way sensitivity tables
4. Compare scenarios (all or selected)
5. Run tripwire/red-line analysis
6. Export results to Excel (unless --no-export is used)

Model Structure

Core Modules

• oasis_model.py: Core financial model with B2C, B2B, and cash flow calculations
• sensitivity.py: One-way and two-way sensitivity analysis functions
• scenarios.py: Pre-defined scenario configurations (Conservative, Base, Aggressive)
• run_analysis.py: Main runner script with example usage

Key Drivers

The following drivers have the most significant impact on financial outcomes and are the primary focus of sensitivity analysis:

B2C Key Drivers

1. starting_subscribers (Location: oasis_model.py → B2CAssumptions)

   • What: Initial paying subscriber count in January 2026
   • Recommended Range: 50-200 (low: 50-75, base: 100, high: 150-200)
   • Impact: Directly affects starting revenue; compounds with growth
   • Sensitivity Function: sensitivity_starting_subscribers()

2. new_subscribers_per_month (Location: oasis_model.py → B2CAssumptions)

   • What: New paying subscribers added each month (linear growth)
   • Recommended Range: 20-100/month (low: 20-30, base: 50, high: 75-100)
   • Impact: Determines subscriber growth trajectory
   • Sensitivity Function: sensitivity_monthly_new_subscribers()

3. monthly_churn_rate (Location: oasis_model.py → B2CAssumptions)

   • What: Percentage of subscribers cancelling each month (applied before new subs)
   • Recommended Range: 3%-15% (low: 3-5%, base: 7%, high: 10-15%)
   • Impact: Critical for subscriber retention and revenue sustainability
   • Sensitivity Function: sensitivity_monthly_churn_rate()

4. avg_usage_pct_of_allowance (Location: oasis_model.py → B2CAssumptions)

   • What: Average usage as % of included allowance (1500 text or 150 voice)
   • Recommended Range: 50%-150% (low: 50%, base: 100%, high: 150%)
   • Impact: Directly affects API costs and gross margin
   • Sensitivity Function: sensitivity_average_usage_intensity()

5. price_per_month (Location: oasis_model.py → B2CAssumptions)

   • What: Subscription price per month (USD)
   • Recommended Range: $15-$25 (low: $15, base: $20, high: $25)
   • Impact: Direct revenue driver; affects gross margin significantly
   • Sensitivity Function: sensitivity_subscription_price()

B2B Key Drivers

1. num_pilots (Location: oasis_model.py → B2BAssumptions)

   • What: Number of B2B pilot contracts in 2026
   • Recommended Range: 0-10 (low: 0-2, base: 5, high: 8-10)
   • Impact: Total B2B revenue and costs
   • Sensitivity Function: sensitivity_number_of_pilots()

2. developer_hourly_cost (Location: oasis_model.py → B2BAssumptions)

   • What: Cost per developer hour (USD/hour)
   • Recommended Range: $40-$80 (low: $40, base: $40-50, high: $60-80)
   • Impact: Major cost driver affecting B2B gross margin
   • Sensitivity Function: sensitivity_developer_hourly_cost_detailed()

3. included_hours_per_pilot_per_year (Location: oasis_model.py → B2BAssumptions)

   • What: Developer hours included in base contract per pilot
   • Recommended Range: 200-300 hours (target: 250 for 80% margin)
   • Impact: Determines cost structure and overage calculations
   • Sensitivity Function: sensitivity_included_hours_per_pilot()

Operating Key Drivers

1. starting_cash_balance (Location: oasis_model.py → OperatingAssumptions)

   • What: Initial cash balance at start of 2026 (USD)
   • Recommended Range: $300k-$800k (varies by funding stage)
   • Impact: Determines runway and cash buffer

2. Headcount and Salaries (Location: oasis_model.py → OperatingAssumptions)

   • What: Number of FTE by role and monthly salaries
   • Impact: Major operating expense driver

How to Change Drivers

Method 1: Direct Code Edit

• Edit default values in oasis_model.py at the top (see B2C_DEFAULTS, B2B_DEFAULTS, OPERATING_DEFAULTS)
• Or modify dataclass defaults directly

Method 2: Config Dictionary (Recommended)

from oasis_model import create_assumptions_from_config

config = {
    'b2c': {'starting_subscribers': 150, 'monthly_churn_rate': 0.05},
    'b2b': {'num_pilots': 8}
}
assumptions = create_assumptions_from_config(config)

Method 3: Programmatic Override

from scenarios import create_base_scenario

assumptions = create_base_scenario()
assumptions.b2c.starting_subscribers = 150
assumptions.b2c.monthly_churn_rate = 0.05

Usage Examples

Custom Analysis

from oasis_model import OasisModel, ModelAssumptions, B2CAssumptions, B2BAssumptions
from scenarios import create_base_scenario

# Start with base scenario
assumptions = create_base_scenario()

# Modify specific assumptions
assumptions.b2c.starting_subscribers = 150
assumptions.b2c.monthly_churn_rate = 0.05
assumptions.b2b.num_pilots = 8

# Run model
model = OasisModel(assumptions)
results = model.run_full_model()

# Access results
print(f"Total Revenue: ${results['summary']['total_revenue']:,.2f}")
print(f"Gross Margin: {results['summary']['total_gross_margin_pct']:.1f}%")

One-Way Sensitivity Analysis

from sensitivity import one_way_sensitivity
from scenarios import create_base_scenario

base = create_base_scenario()

# Vary churn rate
values = [0.03, 0.05, 0.07, 0.10, 0.15]
metric = lambda m: m.run_full_model()['summary']['ending_subscribers']

results = one_way_sensitivity(
    base,
    'b2c.monthly_churn_rate',
    values,
    metric,
    'Ending Subscribers'
)
print(results)

Two-Way Sensitivity Table

from sensitivity import two_way_sensitivity
from scenarios import create_base_scenario

base = create_base_scenario()

row_driver = ('b2c.starting_subscribers', [50, 100, 150, 200])
col_driver = ('b2c.avg_usage_pct_of_allowance', [0.5, 1.0, 1.5])

metric = lambda m: m.run_full_model()['summary']['total_b2c_gross_margin_pct']

results = two_way_sensitivity(base, row_driver, col_driver, metric)
print(results)

Scenario Comparison

from scenarios import compare_scenarios, create_scenario_summary_table

# Compare all scenarios
scenario_results = compare_scenarios()
summary = create_scenario_summary_table(scenario_results)
print(summary)

Key Outputs

The model provides:

1. Monthly B2C Metrics:

   • Subscriber count (with churn and growth)
   • Revenue (gross and net after Stripe fees)
   • API costs (Gemini, Deepgram)
   • AWS costs
   • Gross profit and margin %

2. Annual B2B Metrics:

   • Total revenue from pilots
   • Developer/support costs (included + overage hours)
   • Gross profit and margin %

3. Cash Flow & Runway:

   • Monthly cash flow
   • Cumulative cash balance
   • Months of runway (based on burn rate)

4. Sensitivity Analyses:

   • One-way: Impact of varying single drivers
   • Two-way: Pivot tables showing interactions between drivers

5. Scenario Comparisons:

   • Side-by-side comparison of Conservative, Base, and Aggressive cases
   • Extended metrics: minimum monthly cash, runway at start vs year-end

6. Tripwire & Red-Line Analysis:

   • Checks violations of minimum cash, minimum gross margin, maximum burn thresholds
   • Identifies which scenarios violate thresholds and in which months

Customization Guide

Adding New Drivers

1. Add the assumption to the appropriate @dataclass in oasis_model.py
2. Use it in the calculation methods
3. Create a sensitivity function in sensitivity.py if desired

Adding New Scenarios

1. Create a new function in scenarios.py following the pattern of create_base_scenario()
2. Add it to the compare_scenarios() function
3. Update run_analysis.py to include it in exports

Modifying Calculations

All calculation logic is in OasisModel class methods:

• calculate_b2c_monthly(): B2C revenue and costs
• calculate_b2b_annual(): B2B revenue and costs
• calculate_operating_expenses_monthly(): Operating expenses
• calculate_cash_flow(): Cash flow and runway

Default Assumptions

B2C (Base Scenario)

• Starting subscribers: 100
• New subscribers/month: 50
• Monthly churn: 7%
• Price: $20/month
• Usage: 100% of allowance (1500 text or 150 voice prompts)
• API costs: $0.002/text prompt, $0.016/voice minute
• AWS: $15/month fixed

B2B (Base Scenario)

• Number of pilots: 5
• Average contract value: $50,000/year
• Developer cost: $40/hour
• Included hours: 250 hours/pilot/year

Operating (Base Scenario)

• Headcount: 2 founders, 1 developer, 0.5 support
• Fixed overhead: $2,000/month
• Starting cash: $500,000

Notes

• All costs and revenues are in USD
• B2B revenue is distributed across months based on pilot start dates
• Runway calculation assumes current burn rate continues
• The model assumes linear growth and churn (can be extended for more complex patterns)

License

Internal use for Oasis Browser financial planning.