Skullgirls Mobile — LTV Analysis

Generated April 7, 2026 · Pipeline v2.1

Payback Period by Cohort

Days until cumulative paid revenue equals paid media spend. Based on paid channels only (media cost basis).

View:
Confirmed (reached within data) Extrapolated beyond M12 Projected (immature cohort)

* Paid Only: paid install revenue vs paid media spend. Bars with lower opacity = geometric extrapolation beyond M12 using observed M6→M12 growth rate.

Payback Period Detail Table

Cohort Payback (yrs) Payback (days) Crossed at M12 ROAS Status

LTV Curves by Channel

Cost-weighted ROAS progression. Computed as total channel revenue / total channel cost at each window.

ChannelD7D14D30D60D90D120M4M6M12

* Organic ROAS is N/A (no cost). See Revenue Per Install table below for organic benchmark.

* TikTok LTV data is from Aug 2024 – Feb 2025 campaigns (prior wave). Recent TikTok (launched Wk 5 2026) is running higher D7 (~34-56%) but has no long-window LTV data yet.

* "Ironsource (legacy)" is Q3 2024 Ironsource data ($49K total spend) — not representative of current Applovin campaigns which launched Wk 10 2026.

Multipliers vs D7 Baseline

ChannelD14/D7D30/D7D60/D7D90/D7D120/D7M4/D7M6/D7M12/D7

Retention Analysis

Install-weighted retention rates. Includes organic as benchmark.

ChannelD1D7D14D30D60D90D120M4M6

Retention Deltas

M4 bump = D90→M4 change (positive = users returning). Paid vs Organic gap at D30.

ChannelD90→M4 (pp)M4→M6 (pp)Paid vs Organic D30 (pp)

Organic Benchmark

Revenue per install ($) by window. Organic shown as baseline; gap shows how paid compares.

Revenue Per Install

ChannelD7D14D30D60D90D120M4M6M12

Gap vs Organic (%)

Green = within -10% of organic, Yellow = -10% to -30%, Red = below -30%

ChannelD7D14D30D60D90D120M4M6M12Trend

Target Calibration

Historical multipliers and back-calculated D7 floors for 60-day payback at 140% (incl. platform fees).

ChannelHist D7D30/D7D60/D7D90/D7 D7 for 70% D30D7 for 80% D30D7 for 140% D90 Recent D7Proj D90Status

Recent D7 actuals: Google ~35%, Facebook ~24%, TikTok ~56% (from last 30 days of campaign data). Applovin: too early (launched Wk 10 2026).

Last 30 Days vs Prior 30 Days

Campaign performance from weekly data. Last 4 weeks vs prior 4 weeks.

By Channel

ChannelSpendSpend ΔInstallseCPIeCPI ΔD7 ROASD7 ΔD30 ROAS

By Region

RegionPaid SpendPaid InstallsOrganic InstallsSpend Δ%Last D7 ROASPrior D7 ROASD7 Δ

Android vs iOS

OSPaid SpendPaid InstallsOrganicD7 ROASPrior SpendPrior PaidPrior OrgPrior D7Spend Δ%

Projected LTV (pLTV)

Cascading Ridge regression projections for immature cohorts (Sep 2025 – Mar 2026). Backtested on Jan–Aug 2025 holdout. Q1 2026 sourced from campaign data.

Actual (mature) Projected

Monthly pLTV — All Channels

Sep 2025 – Mar 2026 cohorts. Italic = projected beyond maturity window.

MonthInstallsD7D14D30D60D90D120M4M6M12

Paid Channels — Monthly pLTV

Paid only (Google, Facebook, TikTok). Toggle between projected gross revenue and revenue per install.

Paid Monthly × Channel

MonthChannelInstallsCostD7D90M12D90 ROASM12 ROAS

pLTV by Channel

ChannelInstallsCostD7D30D60D90D120M6M12D90 ROASM12 ROAS

pLTV by Region

Sep–Dec 2025 LTV data + Q1 2026 campaign data. D7 is actual; longer windows are projected from LTV cohorts only.

RegionInstallsCostD7D90M6M12

pLTV by Platform — Paid

PlatformInstallsCostD7D90M6M12D90 ROASM12 ROAS

pLTV by Platform — Organic

PlatformInstallsD7D90M6M12

Multiplier Model Backtest

Step-up multiplier accuracy vs 20 fully mature SGM cohorts. MAPE = mean absolute % error. Replaces D0 Ridge model (was 31-110% MAPE).

Experimental: predict LTV from day-zero revenue only. Use for early budget signals — confirm with D7 model after 8 days. D0 aggregate error: D90 ≈ ±9%, M12 ≈ ±7%.

D0 Backtest (Jan–Aug 2025)

TargetAgg ActualAgg PredictedAgg ErrorMAEN

D0 Early Signal — Paid Monthly

What the D0 model would have predicted from day-zero data alone. Compare against D7 model above.

MonthInstallsAd SpendD0 (actual)D7 (pred)D90 (pred)M12 (pred)D90 ROASM12 ROAS

D0 vs D7 Model Comparison — Paid Monthly

Side-by-side: D0 early signal vs D7 production model. Divergence > 15% is a flag.

MonthD90 (D0 model)D90 (D7 model)ΔM12 (D0 model)M12 (D7 model)Δ

Model Health Monitor

Self-correcting system: tracks prediction accuracy over time, replaces predictions with actuals as cohorts mature, and flags when retraining is needed.

Retraining Status

D7 Model — Drift Metrics

WindowBiasMAPESamplesTrendStatus

D0 Model — Drift Metrics

WindowBiasMAPESamplesTrendStatus

Prediction vs Actual — Live Tracker

Shows D7-model predictions alongside actuals as cohorts mature. Green = verified, gray = awaiting maturity.

CohortD7D30D60D90M6M12

Model Backtest Accuracy

Trained on pre-2025 cohorts (channel×month aggregates), tested on Jan–Aug 2025. Aggregate error = (predicted − actual) / actual.

WindowMAE ($/inst)Agg ActualAgg PredictedAgg ErrorN

Revenue Forecast — 2026

Two-layer model: DAU × ARPDAU for total monthly revenue (incl. elder players) + new cohort LTV. Webstore-adjusted. UA scaling 12%/qtr, 2 content drops.

Scenario Summary

Monthly Revenue — Actual & Forecast

Jan 24 – Mar 26 = Adjust actuals (webstore-adjusted). Apr – Dec 26 = base case forecast. Elder % = revenue from users acquired >30 days ago.

MonthDAUARPDAUTotal RevenueNew CohortElder RevElder %InstallsUA SpendNote

Total Monthly Revenue — Actual & Forecast

Revenue Composition: Elder vs New Cohort

DAU & ARPDAU Trajectory

Scenario Comparison — Cumulative Revenue (Apr–Dec 2026)

Key Assumptions

ParameterDownsideBaseUpside

Forecast Backtest — Walk-Forward Validation

For each of the last 6 months (Oct 2025 – Mar 2026), the model was trained only on data available before that month. This tests true out-of-sample forecasting accuracy. Naive baseline = "next month = last month."

Monthly Walk-Forward Results

MonthActual RevPredicted RevError % Actual DAUPred DAUDAU Err % Actual ARPDAUPred ARPDAUARPDAU Err % Direction

Revenue Backtest: Predicted vs Actual