A failed indie launch typically wastes $5,000–$50,000 in marketing spend. We give you calibrated revenue ranges — not point estimates — so you can plan budget under uncertainty. Five real comp launches per forecast, honest causal estimates of marketing-lever effects with confidence intervals, and a Total Lift Attribution tool that recovers the campaign wishlists Steam’s UTM dashboard misses (~75% under-reported).
Free forecast + comp set on every game. Full launch report — calibrated cone with explainer, marketing-lever causal estimates, and Total Lift Attribution access — is a $299 one-time purchase per launch.
14-day full refund if you haven’t uploaded Steamworks data yet. 50% refundable thereafter. Terms →
One launch, one purchase, no subscription. Everything below is delivered through your authenticated dashboard the moment payment clears.
P10 / P50 / P90 lifetime revenue estimates with empirically-validated coverage. The cone gets wider when your game is unlike anything in our 77K-app calibration corpus — and we say so explicitly. No false precision.
Independent revenue estimate from your wishlist count using the industry-standard Boxleiter formula, with a divergence flag if our cone disagrees with the rule of thumb. Two methods, one decision.
Honest causal effect estimates with confidence intervals for things like festival appearances, demo releases, and trailer revisions. Plan budget under uncertainty, not under guru-claimed point estimates.
5+ real launches similar to yours by genre + tag overlap, with their actual revenue figures and how their wishlist trajectory mapped to outcome. The cone is grounded in specific games you can name.
Upload your Steamworks “Wishlists by day” CSV and we recover the campaign wishlists Steam’s UTM dashboard misses (~75% under-reported). True per-campaign cost-per-wishlist, not the half-truth you get from utm_source alone.
As wishlists grow, re-run the forecast. The cone narrows, the Boxleiter check updates, the attribution recomputes. You’re not buying a one-time report — you’re buying a tool you keep using through your launch window.
For each historical launch, our Boxleiter forecast vs the actual outcome. The variance is the point.
The retrospective demos load from our database. While they populate, you can run a forecast on any released game right now.
Forecast Hades →Because launches are events, not ongoing operations. You buy the report once for one launch, run the forecast as many times as you want through that launch window, and that’s it. No recurring billing surprise. No "did I forget to cancel?" anxiety.
The cone gets wider, and we tell you so. Calibrated coverage means the published interval contains the true outcome with frequency at-least 1−α over a reference distribution — on average, not for every individual game. We surface comp-set similarity so you can judge the cone’s applicability to your specific situation. False precision is the enemy.
Boxleiter is a single-number heuristic (revenue ≈ wishlists × ~$5). It’s a starting point, not a budget plan. We deliver a calibrated cone with a P10 floor and P90 ceiling, plus a Boxleiter cross-check for sanity, plus comp-set evidence so you can name 5 actual launches similar to yours. Three converging methods beat one rule of thumb. Methodology →
Steam’s UTM dashboard only counts wishlists where the user clicked your campaign link AND completed the wishlist add in the same session. Real users discover via your campaign, leave, come back later. Steam attributes those to "direct/none." Total Lift Attribution uses your “Wishlists by day” CSV against a 14-day median baseline to recover the lift Steam misses — ~75% under-reported in our internal benchmarks. True per-campaign cost-per-wishlist instead of the half-truth.
14-day full refund if you haven’t uploaded any Steamworks data yet. 50% refundable thereafter. Email [email protected] from the same address you bought with; refund processes within 14 days. Full terms here.
Built solo by Greg C., a senior software engineer with production ML experience in predictive analytics for sports — a domain where calibrated prediction under fat-tailed uncertainty separates working strategies from losing ones. The same calibration discipline applies directly to indie launch revenue, where wishlist-to-sales conversion has the same heavy-tailed structure. Reach me at [email protected].
Bi-weekly notes on calibration validation runs, new features, and real launches we’re testing against. No spam.