What-If Scenario Modeling
AI-powered scenario simulation with Monte Carlo analysis enabling real-time forecasting of business decisions reducing planning cycles from weeks to hours with 75-85% accuracy.
Why This Matters
What It Is
AI-powered scenario simulation with Monte Carlo analysis enabling real-time forecasting of business decisions reducing planning cycles from weeks to hours with 75-85% accuracy.
Current State vs Future State Comparison
Current State
(Traditional)1. CFO asks: 'What if we increase marketing spend 20% in Q1 2025, how does it impact revenue and profitability?'. 2. FP&A analyst creates Excel financial model: builds assumptions (conversion rate, CAC, LTV), links formulas across 10 worksheets. 3. Analyst manually adjusts marketing spend scenarios: baseline, +10%, +20%, +30%, recalculates revenue and profit for each. 4. Analyst creates sensitivity analysis: varies conversion rate ±10%, ±20% to show range of outcomes. 5. Analyst builds PowerPoint presentation with scenario comparison tables and charts. 6. Total analysis time: 1-2 weeks (build model, validate assumptions, create scenarios, present findings). 7. CFO asks follow-up: 'What if competitor launches 30% discount promotion?', another week for revised model.
Characteristics
- • Excel
- • Anaplan
- • Workday
- • Farseer
- • Business Intelligence Platforms
Pain Points
- ⚠ Manual update burden across multiple spreadsheets leading to inefficiencies.
- ⚠ Human optimism bias obscuring realistic risk assessments.
- ⚠ Fragmented data and collaboration gaps across teams.
- ⚠ Complexity in managing multiple scenarios with numerous variables.
- ⚠ Dependence on manual processes increases error rates.
- ⚠ Single-forecast risk due to lack of rigorous scenario testing.
- ⚠ High complexity in calculating scenario likelihoods.
- ⚠ Initial implementation costs for specialized planning software.
Future State
(Agentic)1. CFO opens Scenario Planning Agent: 'What if we increase marketing spend 20% in Q1 2025?'. 2. Agent simulates scenario in real-time: increases ad spend from $5M to $6M, models impact on traffic (+15%), conversion (+2%), CAC (+5%), revenue (+18%), profit (+12%). 3. Agent runs Monte Carlo simulation with probabilistic ranges: 'Revenue increase likely 15-22% (75% confidence), profit increase 8-16%, breakeven ROI at 12% revenue lift (achieved with 85% probability)'. 4. CFO explores interactively: 'Now add competitor 30% discount scenario', agent adjusts model: 'Competitor promotion reduces conversion -8%, net revenue lift 8-14% (vs 15-22% without competition)'. 5. Agent provides decision recommendation: 'Increase marketing spend to $6M recommended - expected $900K profit increase, 85% probability of positive ROI even with competitor promotion'. 6. Agent shows sensitivity analysis: key drivers ranked by impact (conversion rate ±10% = ±$500K profit, CAC ±10% = ±$200K profit). 7. Total analysis time: 15-30 minutes vs 1-2 weeks, interactive exploration vs static reports, probabilistic vs deterministic.
Characteristics
- • Historical performance data (revenue, conversion, CAC, LTV)
- • Marketing spend and ROI by channel and campaign
- • Competitive intelligence (pricing, promotions, market share)
- • Seasonal patterns and business event calendar
- • Financial models and business rules
- • Market trends and economic indicators
- • Probabilistic distributions for key assumptions
- • Constraints and business boundaries (budget limits, capacity)
Benefits
- ✓ 90%+ time reduction (15-30 min vs 1-2 weeks per scenario)
- ✓ Interactive exploration (CEO adjusts assumptions in real-time)
- ✓ Probabilistic modeling (Monte Carlo vs deterministic assumptions)
- ✓ Unlimited scenarios (no rebuild required for follow-up questions)
- ✓ 75-85% accuracy with confidence intervals (vs 60-70% point estimates)
- ✓ Decision recommendations with risk quantification
Is This Right for You?
This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.
Why this score:
- • Applicable across multiple industries
- • Higher complexity - requires more resources and planning
- • Moderate expected business value
- • Time to value: 3-6 months
- • (Score based on general applicability - set preferences for personalized matching)
You might benefit from What-If Scenario Modeling if:
- You're experiencing: Manual update burden across multiple spreadsheets leading to inefficiencies.
- You're experiencing: Human optimism bias obscuring realistic risk assessments.
- You're experiencing: Fragmented data and collaboration gaps across teams.
This may not be right for you if:
- High implementation complexity - ensure adequate technical resources
- Requires human oversight for critical decision points - not fully autonomous
Parent Capability
Marketing Mix Modeling (MMM)
Delivers continuous MMM refresh with instant scenario testing, automated recommendations, and integrated planning achieving significant media efficiency improvement.
What to Do Next
Related Functions
Metadata
- Function ID
- function-what-if-scenario-modeling