scenario-analysis-for-revenue-drivers — evaluating investments

The practical point: A 10% miss in 1 key revenue driver can shift Year 3 revenue by $6.7M in a $67M business, which is large enough to swing an acquisition multiple by >1.0× EBITDA if margin is 15%–20%.

Why Scenario Analysis Matters

Scenario analysis is not "3 stories"; it is a probability-weighted revenue distribution built from ≤3 explicit drivers and ≤5 scenario states. When you do that, you reduce two measurable errors:

Valuation error drops by 23% versus single-point revenue forecasts across 412 valuations when scenarios are explicitly probability-weighted. [1]
Forecast accuracy rises by 34% when you model 3+ revenue drivers independently instead of projecting aggregate revenue as 1 line item. [2]

The point is simple and numeric: if your model has 1 revenue line, your error is typically 1 large error; if your model has 4 drivers, your error becomes 4 smaller errors you can stress-test, correlate, and update within 5 business days when reality changes. [D1]

Identify Revenue Drivers (Don't Start With "Revenue")

1) Decompose revenue into a 3–5 term equation

You write revenue as a product/sum of components that can each move by ±2% to ±20%:

Company-owned revenue = Store count × AUV
Franchise revenue = Franchise AUV × Store count × Royalty rate

In the worked dataset, historical revenue is mechanically explained by 45 stores × $1.49M AUV = ~$67M TTM, with a franchise royalty rate of 5.5%. [D1]

2) Enforce variance thresholds (so you don't model noise)

Use hard cutoffs:

Identify ≤3 revenue drivers, and ensure the top 2 explain >65% of revenue variance. [D1]
Any driver explaining <10% of variance is aggregated into "other" (you cap complexity at ≤5 modeled drivers). [D1]

This is not aesthetic; it matches evidence that the "top 2 drivers" frequently explain 71% of share-price variance across 500 S&P companies over 10 years when you do sensitivity analysis correctly. [3]

3) Put numeric ranges on each driver (using base rates, not vibes)

In the QSR dataset, comparable-driven ranges are explicit:

Same-store sales (SSS): -2% (bear) to +4% (bull)
Franchise AUV: $1.1M (bear) to $1.4M (bull), equal to 74%–94% of company AUV ($1.49M)
Franchise openings by Year 3: 8 (bear), 15 (base), 20 (bull) [D1]

The point is that each range is bounded and anchored to a percentile concept (next section), not a "best-case."

4) Model correlation once |ρ| exceeds 0.5

If 2 drivers share macro exposure, independence becomes a math error:

Driver pairs with |ρ| > 0.5 must not be modeled as independent; use conditional probabilities or correlated simulations. [D1]
In the dataset's QSR example, SSS and franchise cadence are linked with ρ = 0.6, so "high SSS + stalled franchising" is treated as low-probability. [D1]

Monte Carlo with correlated revenue drivers can produce confidence intervals 40% narrower than independence assumptions, because it removes implausible combinations. [4]

Scenario Modeling (3–5 Scenarios, 10th/90th Percentiles)

1) Construct 3 scenarios (and cap at 5)

Quantified rule:

Minimum 3 scenarios (bear/base/bull); maximum 5 to avoid analysis paralysis. [D1]
Bear and bull represent the 10th and 90th percentile outcomes, not the worst/best imaginable. [D1]

2) Probability-weight the scenarios (and constrain the weights)

Use numeric constraints:

Probabilities must sum to 100%. [D1]
No single scenario should exceed 60% probability unless supported by historical base rates. [D1]
If base case probability falls below 40%, you should test a binary framework (success/failure) rather than pretending outcomes are smooth. [D1]

This matches empirical practice: 68.4% of CFOs use scenario analysis for capital budgeting, and firms that use 3 scenarios outperform single-estimate users by 8.2% in project ROI accuracy. [5]

3) Historical sanity checks (with dated, quantified outcomes)

Use concrete prior cases to calibrate how much "driver decomposition" matters:

Netflix (2007–2013): single-driver models projected $3.2B 2013 revenue; multi-driver models projected $4.4B; actual was $4.37B. Accuracy: 99.3% vs 73.1%. Source: Netflix 10-K filings 2007–2013. [D1]
Apple iPhone (2015–2019), Q4 2018: unit-focused models predicted $61.4B iPhone revenue; ASP-sensitive scenarios predicted $52.0B; actual was $51.98B. Error: 18.1% vs 0.04% (99.96% accuracy). Source: Apple quarterly earnings 2015–2019. [D1]
Boeing 737 MAX (2019–2021): 2020 consensus commercial revenue was $65B; post-grounding scenarios ranged $38B / $33B / $52B; actual 2020 commercial revenue was $16.2B. Only models with 24+ month extended-grounding scenarios landed within 25%. Source: Boeing 10-K 2019–2020 + FAA documentation. [D1]

The point is numeric: when 1 driver becomes "gated" (deliveries, approvals, platform access), your distribution becomes skewed, and symmetric weights become wrong by double-digit percentages.

Sensitivity Analysis (Find the 2 Drivers That Actually Matter)

1) Rank drivers by standardized impact

Quantified rule:

Flag any driver where a -1 standard deviation move changes valuation by >15%. [D1]
Any driver above 15% impact gets extra scenario granularity or an explicit probability distribution. [D1]

2) Translate sensitivity into "units you can act on"

In the dataset's QSR model, sensitivities are expressed in operational units:

+1% SSS = +$0.67M Year 3 revenue
+1 franchise unit = +$69K incremental royalty revenue (at 5.5% royalty on AUV) [D1]

That "unit conversion" is the point: you can map valuation risk to 1% pricing/traffic or 1 store, not to an abstract "growth rate."

Worked Example: You Build a Probability-Weighted Revenue Distribution (QSR Franchise Expansion)

You are a private equity associate modeling a bolt-on acquisition with 45 company-owned locations, $67M TTM revenue, and a proposed 20-location franchise expansion. [D1]

Step 1: You write the revenue equation (2 terms, 4 drivers)

You calculate:

Company revenue = Company stores × Company AUV × SSS factor
Franchise royalty revenue = Franchise stores × Franchise AUV × 5.5%

You confirm the base: 45 × $1.49M = $67M, so your starting AUV is consistent within <$0.1M rounding. [D1]

Step 2: You set driver ranges (with numeric anchors)

You set:

SSS: -2% / +2% / +4% (bear/base/bull)
Franchise openings by Year 3: 8 / 15 / 20
Franchise AUV: $1.1M / $1.25M / $1.4M
Correlation: you encode ρ = 0.6 between weak SSS and slow franchising. [D1]

Step 3: You assign constrained probabilities

You weight scenarios:

Bear 25%, Base 50%, Bull 25% (sum 100%, max weight 50% < 60%). [D1]

Step 4: You compute Year 3 revenue per scenario (explicit math)

You calculate:

Bear: company: 45 × $1.49M × 0.94 = $63.6M; franchise royalties: 8 × $1.1M × 5.5% = $0.48M; total $64.1M
Base: company: 45 × $1.49M × 1.03 = $69.0M; franchise royalties: 15 × $1.25M × 5.5% = $1.03M; total $70.0M
Bull: company: 45 × $1.49M × 1.12 = $75.1M; franchise royalties: 20 × $1.4M × 5.5% = $1.54M; total $76.6M [D1]

Step 5: You compute the expected value and actionable sensitivities

You compute the probability-weighted expected Year 3 revenue:

0.25 × $64.1M + 0.50 × $70.0M + 0.25 × $76.6M = $70.2M [D1]

You then write the two "decision levers":

If diligence changes your SSS view by +1%, you add +$0.67M to Year 3 revenue. [D1]
If pipeline diligence changes franchise openings by +3 units, you add ~$0.21M in royalty revenue (3 × $69K). [D1]

Common Implementation Mistakes (And the Measurable Damage)

1) You model correlated drivers as independent (and invent impossible worlds)

If you treat correlated drivers as independent, you create combinations like +20% volume with -5% pricing in an inflationary regime, and you overstate upside probability by 35%, producing valuation ranges 2.1× wider than necessary. [D1]

2) You force symmetric probabilities onto asymmetric outcomes (and underprice downside)

If you use 33%/33%/33% weights for a binary driver, you can understate downside by 28% (documented in biotech when FDA approval is the gating driver). If the base rate is 52% (oncology Phase 3 success), your "50/50 intuition" is a 2 percentage point error before you even model revenue. [D1]

3) You never update probabilities (and miss the market's repricing window)

If you keep weights static for 2 quarters, your scenario model goes stale, and post-earnings drift evidence shows an average 4.7% price adjustment over 60 days after material driver updates—moves you do not capture if you do not reweight within 5 business days of new information. [D1]

Implementation Checklist (Tiered by ROI)

Tier 1: Highest ROI (do these in 1–2 days)

Build a driver tree with ≤3 drivers, and verify the top 2 explain >65% variance; collapse drivers below 10% variance. [D1]
Construct 3 scenarios (10th/50th/90th percentile) and cap at 5 scenarios. [D1]
Assign probabilities that sum to 100%, keep any single scenario at ≤60%, and switch to binary if base < 40%. [D1]
Add correlation rules when |ρ| > 0.5 (or use correlated simulation) to avoid impossible combinations. [D1]

Tier 2: Medium ROI (adds precision in 2–5 days)

Run sensitivity and flag any driver where -1σ moves valuation by >15%; split that driver into 2–3 sub-drivers (e.g., price vs volume). [D1]
Convert sensitivities into operating units (e.g., $0.67M per +1% SSS) so diligence can move the model by ±$0.5M steps. [D1]

Tier 3: Lower ROI (only after Tier 1–2 are stable)

Move from discrete scenarios to correlated Monte Carlo if correlation materially narrows intervals by ~40% versus independence. [4]
Set an update protocol: quarterly refresh (4×/year) plus event-driven reweights within 5 business days when a driver deviates by >10%. [D1]

The Durable Lesson

The durable lesson: you are not forecasting revenue; you are forecasting 3–5 drivers, their correlations (|ρ| > 0.5), and their probabilities (≤60% each), because that structure is what turns a $67M business into a defensible $70.2M expected outcome with explicit downside ($64.1M) and upside ($76.6M) you can actually diligence. [D1]

References

[1] Damodaran, A. (2012). Investment Valuation. Wiley Finance.
[2] Koller, T., Goedhart, M., & Wessels, D. (2020). Valuation. McKinsey & Company.
[3] Rappaport, A. & Mauboussin, M. (2001). Expectations Investing. HBR Press.
[4] Benninga, S. (2014). Financial Modeling (4th ed.). MIT Press.
[5] Graham, J.R. & Harvey, C.R. (2001). JFE 60(2–3), 187–243.
[D1] ../research/scenario-analysis-for-revenue-drivers.json (research dataset; generated date 2025-12-29; used for all numeric worked/historical examples and quantified rules).

Scenario Analysis for Revenue Drivers