Steam Revenue Forecaster
Forecast your indie launch. Stress-test any publisher offer on your desk.
Optional · real data
Calibrate the forecast from real data
Wishlist trajectory · Demo & Next Fest · auto-apply signals back to the forecaster inputs
Wishlist trajectory Applies to forecast Where you are today and where you're trending. Most universal signal — almost every Steam page has this data.
Demo & Next Fest performance Applies to forecast If you've already run a demo or Next Fest, plug the numbers in here.
Community & reach signals Applies to forecast Pre-launch indicators of audience growth and visibility. Stronger reach correlates directly with launch buzz.
Playtest quality signals Applies to forecast If you've run a closed playtest or beta, the cohort tells you how the product holds up under real-player use. Strongest predictor of refund rate.
Paid acquisition signals Applies to forecast If you're running paid ads (Meta, TikTok, YouTube, Reddit, etc.), the cost-per-wishlist and paid share tell you whether the spend is working — and whether your wishlist count is "real" or "bought".
Prior game comp Cross-check only If your studio shipped a game on Steam before, its performance is your single strongest comp. The model uses the prior $/wishlist ratio as an independent sanity check on the main forecast.
Post-launch diagnostic Cross-check only If you've already launched, plug in actual performance to see whether you're tracking above or below the forecast — and what year-1 implies from real numbers.
Suggested forecast calibration
Based on the grades above, here's how the forecaster should be set. One click applies all of these to the inputs panel below.
What to do next
Calibration Report
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Calibration grades
Suggested forecast inputs
Top recommendations
Got an offer on your desk?
We've negotiated and operated against dozens of indie publishing deals across DACH and Central Europe. Bring your term sheet — first conversation is free.
Your game
Advanced assumptions
Forecast
52-week revenue curve
Same Y1 total as your base case above — but spread week-by-week instead of monthly, with toggles for the Steam sales you'll participate in and any DLC drops on your roadmap. Lets you stress-test launch timing and ongoing engagement spend in a way the monthly view smooths over.
Calibrated estimate. Weekly distribution is grounded in GameDiscoverCo launch-window data and HowToMarketAGame week-1 share benchmarks. Sale uplift multipliers are typical-indie ranges; AA/AAA titles see weaker relative bumps, free-weekend or 75%-off promotions can see stronger ones. SteamDB tracks concurrent players but not per-game revenue, so revenue-shape calibration draws on Gamalytic aggregates. Treat the curve as directional, not predictive.
What moves the needle
Not every input matters equally. This chart ranks the eight drivers of your Year-1 revenue by how much they swing the number — so you know what to focus on first, both in production and in your deal.
The drivers · what they are, how to move them
Tap any driver to reveal what it represents and how to influence it.
Wishlists at launch
Post-launch wishlists
Pre-launch buzz
Review score
Price
Refund rate
Organic / non-wishlist
Seasonal discounts
Launch discount
Regional pricing
Demo + Next Fest
Compare publisher offers
Add any offers on your desk. The math runs against your base case so you can see exactly how each structure compares.
Self-published
Year-1 net to studio · side by side
How the math works
Wishlist conversion by sub-genre. Conversion varies dramatically by sub-genre, not by broad parent. GameDiscoverCo's 2024–2025 study puts median Week-1 conversion at 0.15× of wishlists (15%), dropping to 0.10× for games priced above $10; the variance is roughly 10× from median in either direction. At the top, online co-op breakouts (PEAK 29×, Mage Arena 8.7×, R.E.P.O. 7.5×) and viral horror clear an order of magnitude above the median. At the bottom, traditional visual novels, 2D platformers, and match-3 sit in single digits. Chris Zukowski's 2024 success-rate analysis puts Open World Survival Craft at 24.5% and Farming Sims at 20.8% — the top two indie genres by probability of crossing 1,000 reviews — while horror leads by absolute volume of hits. The forecaster carries a separate Week-1 and Year-1 benchmark for each of 42 sub-genres across 12 parent families: pick the closest fit for your main, optionally blend in a secondary at 70/30 for hybrids. The expected review tier then applies a multiplier: Overwhelmingly Positive = 1.4×, Very Positive = 1.15×, Mostly Positive = 1.0×, Mixed = 0.55×, Negative = 0.25×. Review score gates discoverability at the algorithm level, so its effect compounds across the year.
Pre-launch buzz. Buzz is the multiplier you've earned before the algorithm sees your launch — press attention, streamer reach, festival placements, viral demo moments. It compounds with both wishlist conversion and organic discovery (people who heard about you but didn't wishlist). Multipliers: None = 1.0×, Some = 1.15×, Building = 1.4×, Strong = 1.8×, Phenomenon = 2.5×. The top tier is the once-per-year breakout — Hades, Vampire Survivors, Balatro — and is rare for a reason: part craft, part timing, part luck.
Post-launch wishlists. Wishlists that arrive after launch — driven by reviews, content creator coverage, sale event visibility, and Steam's discoverability ripples — convert at roughly 70% of pre-launch wishlists. A healthy launch typically adds 50–200% of the launch-day count over the rest of Year 1, sometimes much more for sleeper hits.
Launch context. A playable demo at launch lifts conversion by roughly 8%. Next Fest adds another smaller bump (~4%) and shifts more revenue into the launch month through concentrated visibility. Both are independent of the wishlist count: they affect how those wishlists convert, not how many you have.
Organic, non-wishlist sales. Wishlists are not the whole funnel. Steam discoverability, store browse, and sale events drive additional sales not attributable to wishlists. We add this as a percentage of wishlist sales (20% default, range typically 5–50% depending on visibility). Buzz lifts this further at runtime.
Effective ARPU. List price × (1 − regional haircut) × (1 − blended discount). Steam's regional pricing pulls blended revenue per copy down by 15–25% vs straight USD math. For the year-1 number, the blended discount is a weighted average of your launch discount (applied to the launch month, which carries ~38% of Year-1 revenue) and your seasonal blend (applied to the remaining ~62%). This is why both discounts matter — and why a launch discount that's too aggressive can quietly chew into the year.
Regional pricing in detail. Steam recommends prices per region based on local purchasing power. A $19.99 game might be ~$14 in Brazil, ~$12 in Türkiye, ~$10 in India, ~$8 in some SEA markets. You can override the recommendations, but doing so usually costs more in lost conversion than it recovers in price. The default "haircut" assumes you follow Steam's recommendations — globally-appealing games sit at 20–30% haircut, US/EU-focused titles at 5–15%.
Refunds. Steam's 2-hour refund policy yields 5–10% refund rates for most indies, higher for short games. Refunds are deducted from gross copies before revenue is computed.
Steam's cut. 30% on all revenue up to $10M, 25% to $50M, 20% above. Only the 30% tier is relevant for nearly all indies, so we apply a flat 30%.
Sensitivity bands. The downside scenario assumes wishlists at 60% of input, review tier one notch lower, and buzz one notch lower. Upside assumes wishlists at 140% and review and buzz tiers one notch higher. Real launches cluster wider than this — treat the bands as a planning range, not a confidence interval.
Tornado chart. Each driver is moved through a defensible range (wishlists ±40%, price ±$5, review and buzz tiers ±1, refund/organic/seasonal/regional ±5–10pp, launch discount ±5pp, launch context toggled) while every other input is held at base. Sorted by total swing. Anything sitting at the top is what to focus on first — both in production and in deal negotiation.
Time distribution. Year-1 revenue concentrates heavily in launch month (~38% cumulative) with major lifts at the first Steam-wide sale events (typically months 3 and 6). Year-2 adds roughly 35% on top of year-1 total in a long tail.
52-week trajectory. The weekly view splits Year-1 into 52 buckets calibrated so each block matches the monthly model exactly: M1-M8 span 4 weeks each (W1-32, where ~84% of Y1 revenue lives), M9-M12 span 5 weeks each (W33-52, reflecting the real 4.33 weeks/month). Within each block the curve front-loads — Week 1 alone captures ~20% of Year-1 revenue. Sale events add incremental revenue as a percentage of total Y1 (Lunar +1.5%, Spring +2%, Summer +6% over 2 weeks, Autumn +2.5%, Winter +6% over 2 weeks) — calibrated from GameDiscoverCo sale-event tracking and HowToMarketAGame case studies, then scaled by remaining wishlist tail (a Summer Sale 4 weeks after launch hits at ~98% effectiveness; the same sale 40 weeks in only ~55%). DLC drops follow the same additive model: ~4.5% of Y1 spread across 4 weeks (45% / 28% / 17% / 10% of the budget), scaled by tail effectiveness. Stacking multiple events on the same week is additive. The shift to percent-of-Y1 (rather than multiplier-on-baseline) avoids the trap where early-game sale uplifts would over-inflate because baseline weeks 1-4 carry most of the year's revenue.
Deal mechanics. Revenue share splits gross post-Steam revenue. Recoup-first sends 100% to the publisher until a defined amount is recouped, then splits. MG against royalty pays the developer an upfront minimum guarantee, then the publisher recoups it from royalties before paying further; the developer keeps the MG regardless. Marketing fee takes a fixed fee off the top before rev share kicks in.
Sources & data · where the numbers come from
The model draws on public industry sources rather than any proprietary dataset. Wherever a constant is a calibrated estimate, it's flagged below.
- Sub-genre conversion rates (41 sub-genres, 12 parent families)
- The Week-1 and Year-1 conversion benchmarks for each sub-genre are calibrated from three 2024–2026 data sources: (1) GameDiscoverCo's October 2025 wishlist-conversion study (Simon Carless), which fixes the overall median at 0.15× Week-1, 0.10× for games above $10, with co-op viral outliers ranging up to 29× — read at newsletter.gamediscover.co; (2) Chris Zukowski's annual indie success-rate breakdowns at howtomarketagame.com, which rank Open World Survival Craft (24.5%), Farming (20.8%), Horror, Idle/Incremental, and Job Sims at the top, and Match-3, 2D Platformer, and traditional Visual Novel at the bottom; (3) GameDiscoverCo's June 2025 single-tag taxonomy assigning one main + one sub-genre to every Steam game grossing >$1M, used to validate sub-genre prevalence (Action Roguelike 104 games, Psychological Horror 44, JRPG 121). Cross-checked against VG Insights and Gamalytic public estimates. Sub-genre baselines published in the JS source under
GENRE_PRESETS. - Review tier multipliers (0.25× → 1.4×)
- Approximations of how Steam's discoverability algorithm gates impressions and conversion at each review threshold, calibrated against public sales data for same-genre, same-wishlist-count games at different review tiers. The "Mostly Positive is the algorithmic inflection point" pattern is widely confirmed in developer-shared data and GDC postmortems.
- Pre-launch buzz multipliers (1.0× → 2.5×)
- Calibrated estimate. Derived from comparing the observed launch performance of breakout titles (Hades, Vampire Survivors, Balatro, Lethal Company, Stray) against comparable non-hyped games in the same genre and wishlist bands. The "Phenom" tier is intentionally rare — it's a once-per-year outcome, not a planning baseline.
- Steam's revenue cut (30%)
- Direct from Valve's published revenue share tier table. The 25% and 20% tiers (above $10M and $50M lifetime) are not modelled because they're irrelevant for almost all indies.
- Refund rate (5–10% range, 7% default)
- From Valve's public statements that Steam's average refund rate sits around 7%, with short games skewing higher. Steam's 2-hour refund policy is documented at store.steampowered.com/steam_refunds.
- Regional pricing haircut (15–25% blended)
- Derived from Steam's own recommended regional pricing matrix. A $19.99 USD game's recommended regional prices net out to roughly $14 BR, $12 TR, $10 IN, $8 in some SEA markets — yielding a 15–25% blended haircut across all regions for globally-distributed titles. US/EU-focused titles see 5–15%.
- Demo conversion lift (+8%) and Next Fest lift (+4%)
- Calibrated estimate. Aggregate of Steam Next Fest performance data shared by Valve and individual developer postmortems (Chris Zukowski has published several detailed Next Fest case studies). Both effects compound with each other and with wishlist count.
- Post-launch wishlist conversion ratio (0.7)
- Calibrated estimate. Based on developer-shared data showing post-launch wishlist-adders convert at roughly 60–80% of the rate of pre-launch wishlist holders — the latter typically had stronger purchase intent at sign-up.
- Launch-month revenue share (38%) & year-1 distribution
- Approximation from published Steam release decay curves — most clean indie launches see ~35–45% of year-1 revenue in launch month, with material lifts at the first major Steam-wide sale (typically month 3 or 6 depending on launch date).
- Year-2 add-on (+35%)
- Calibrated estimate from long-tail analyses of indie titles aged 12–24 months on Steam. Healthy launches with continued visibility (sale event participation, content updates) often exceed this; cold-launched titles fall well short.
- Organic / non-wishlist sales boost (5–50% range, 20% default)
- Calibrated estimate. Wishlists never account for 100% of sales — Steam discovery surfaces, sale event browse traffic, and recommendation system referrals add a meaningful fraction. The buzz multiplier lifts this further at runtime to reflect that hyped games get disproportionate organic discovery.
- Community growth bands (Calibration · 5/15/35% per month)
- Calibrated estimate. Derived from Discord and newsletter growth rates reported by indie devs in GDC talks and Game Discoverability Now case studies. The rate matters more than absolute size — a 1,000-member community growing 25%/month outperforms a 10,000-member stagnant one for launch velocity.
- Reach grade bands (Calibration · trailer / creator / press)
- Calibrated estimate. Trailer view bands (30k / 150k / 500k cumulative across YT + Steam + social over 90 days) derived from typical indie performance data. Creator video bands favour count over total views since a single 100k-view stream often outperforms three 30k-view videos for wishlist impact. Combined reach grade weights: creator coverage 45% (highest leverage), press/festival 30%, trailer reach 25%.
- Playtest reception & completion → review tier & refund rate
- Calibrated estimate. Playtest cohorts skew kinder than launch audiences (testers self-selected and felt invested), so playtest % positive is treated as a moderately optimistic predictor of launch review tier. Playtest completion rate is the strongest pre-launch predictor of refund rate — below 30% completion typically translates to 12–20% refunds at launch; above 70% completion typically yields 3–5% refunds. Cohort sizes below 20 are flagged as small-sample / directional only.
- Calibration → forecaster mapping
- Each signal source maps to specific forecaster inputs:
- Buzz tier — highest signal across trajectory velocity, demo conv+uplift, community growth, and combined reach
- Review tier — average of demo review score and playtest reception (when both present)
- Wishlists at launch — trajectory projection preferred; falls back to demo wlAfter
- Post-launch wishlists — max of (trajectory monthly velocity × 3–12 months by tier) and (demo event delta × 0.5–4× by uplift tier)
- Refund rate — derived from playtest completion tier (15 / 10 / 7 / 5 %)
What this model is not: a prediction. It is a planning model calibrated against public benchmarks to give you a defensible range, identify the drivers that matter most, and stress-test offers against a self-published baseline. Any specific game can deviate 30–50% from the model's bands due to factors no model can capture: press cycle, viral moments, genre saturation, competitor launches, your specific marketing execution.
These are planning estimates. Real launches vary on press, virality, genre saturation, timing, and luck. A good consultant won't pretend otherwise.