Scenario and Sensitivity Analysis Questions

Framework: build a 3-year P&L under three scenarios using monthly cohorts -> revenue, CAC, churn, ARPU, and costs (COGS, platform fees, marketing, R&D). Present headline assumptions, sensitivity ranges and decision triggers for product pivots.

Key baseline assumptions (year 0 = today):

• MAU start: 200k, organic growth 5%/mo, CAC $3, ARPU $1.50/mo, gross margin 70%.
• App Store commission: 30% on paid/subscription revenue, 15% for subscriptions after 1 year (per current rules).
• Churn 4%/mo, ARR retention improvements +1%/yr from product work.

Scenario A — Baseline (current fees/rules):

• Driver: steady discoverability, stable conversion (2% conversion to paid).
• 3yr outcome: revenue CAGR ~28%, operating margin positive by year 2.
• Sensitivity: ±20% ARPU or CAC changes swing profit by ±10–18%.
• Trigger for pivot: 15%+ sustained ARPU decline or CAC increase >25% — trigger price/packaging changes and push conversion-focused roadmap (free-to-paid funnel optimizations, trial tweaks).

Scenario B — Increased commissions (e.g., 40%):

• Key change: take-home revenue per transaction drops 10–15%.
• 3yr outcome: revenue growth slower, break-even delayed 6–9 months; gross margin compressed.
• Sensitivity: commission elasticity — if conversion and retention drop >5% due to price pass-through, NPVs fall sharply.
• Triggers: if commission hike reduces EBIT margin below target (e.g., <10%) or CAC payback >12 months -> initiate alternative monetization (ad monetization, lower price + upsell, web-checkout for subscriptions where allowed) and cost rationalization (deprioritize non-core features).

Scenario C — Open third‑party stores (competition + fee arbitrage):

• Assumption: 30% of new installs move to third‑party stores with 0–10% fee; discovery fragmentation increases UA costs by 10%.
• 3yr outcome: potential revenue uplift if we redirect payments off-store (improved take rate), but marketing complexity raises CAC; net benefit depends on ability to implement off‑store flows.
• Sensitivity: adoption of third‑party stores (10–40%) and off-platform conversion rate (50–90%) determine upside.
• Triggers: if off‑store take rate improves net margin by >8% and off-platform conversion >60% -> invest in off-store checkout, build web account linking, and change App Store-only features. Conversely, if fragmentation increases CAC >30% with <5% fee savings -> focus on in-app retention and premium feature expansion.

Decision framework (common):

• Leading KPIs to monitor weekly: ARPU, CAC, CAC payback months, gross margin on in-app revenue, paid conversion, churn.
• Monthly deep-dive triggers: 10% movement in any KPI vs forecast -> product prioritization meeting; 25% movement -> execute pivot playbook (pricing, checkout engineering, marketing channel reallocation).
• Tactical playbook items: implement web checkout, experiment with in-app promotions, negotiate platform programs, accelerate high-LTV features (educational content, enterprise tier).

This plan balances short-term mitigations (pricing, ads) with medium-term bets (off‑platform flows, product-led retention) tied to measurable triggers.

Approach summary: build a 24-month cohort-based unit-economics model comparing Path A (↑marketing spend 25% for 6 months to defend share) vs Path B (same spend reallocated to retention features → +5ppt 12‑month retention). Compute CAC, LTV, payback, and margin impact under both paths and run sensitivity analysis.

Key inputs (per new-acquisition cohort and existing base):

• CAC (current): $X
• ARPU (monthly): $R
• Gross margin on revenue: m (e.g., 70%)
• Baseline monthly churn: c (or retention r = 1−c)
• Incremental marketing spend: ΔM = 25% * current marketing budget for 6 months
• Retention lift (Path B): +5 percentage points at 12-month mark (model as improved monthly churn schedule)
• Time horizon: 24 months; discount rate: d (optional for NPV)

Model structure / formulas:

• Cohort revenue at month t: Rev_t = ARPU * active_users_t
• active_users_t = cohort_size * Π(1 − churn_i) for i ≤ t
• LTV (gross) = Σ_{t=1..T} Rev_t * m
• LTV:CAC = LTV / (CAC + allocated incremental spend per acquired user)
• Payback months = months until cumulative gross contribution ≥ CAC

Assumptions (explicit):

• Marketing efficiency: Path A yields X% lift in acquisition volume (estimate from past campaigns); assume CAC constant or improves/worsens by Y%
• Retention improvement shape: either immediate +5ppt at month 12 onward or a ramp (e.g., linear 3–12 months); specify which
• No major product/market changes; ARPU stable; margins unchanged except development costs capitalized
• Allocate ΔM to acquisition in Path A or to product dev & rollout in Path B; amortize product build cost over N months to per-user basis

Sensitivity test plan:

• Vary acquisition lift for Path A: low/medium/high (e.g., −10% / baseline / +20%)
• Vary CAC elasticity: CAC increases/decreases with higher spend
• Vary retention lift for Path B: +2ppt, +5ppt, +8ppt
• Test different churn baselines (±25%) and ARPU volatility (±10%)
• Vary time horizon (12/24/36 months) and discount rate (0%–10%)
  Report tornado charts and break-even thresholds (e.g., minimum retention lift for Path B to beat Path A).

Presentation to leadership:

• One-slide executive summary: recommendation, headline LTV:CAC, payback, margin impact, and risk
• Supporting slides: model inputs & assumptions, cohort LTV curves (Path A vs B), sensitivity charts, scenario table (best/likely/worst), and action plan (milestones, measurement plan, guardrails)
• Include clear asks: instrumentation to measure retention lift, A/B test plan, budget reallocation trigger points, and KPIs to monitor weekly/monthly.
• Conclude with decision criteria: prefer Path B if retention lift ≥ Xppt within Y months and implementation risk acceptable; else choose Path A for short-term share defense.