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Back a Steam project only after testing five evidence layers together: demand and audience growth, monetization, discovery efficiency, product quality, and production execution. A large wishlist total is not enough. Request time-series Steamworks data, revenue and refund detail, cohort engagement, review trends, and a studio-verified runway model, then stress-test the investment against low, base, and high launch cases.
Start with an evidence pack, not a wishlist headline
Ask the studio for a dated export or read-only dashboard covering the project from its first public store activity to the latest week. The pack should distinguish management-reported figures from data pulled directly from Steamworks and identify every cohort, region, platform, price point, and event that shaped the result.
Use the same definitions throughout your model. “Wishlist” might mean the balance at a particular date, additions during a campaign, or wishlists that remain at launch after deletions. “Sales” might mean units, gross bookings, net receipts, or a publisher’s share. A decision is only as sound as those definitions.
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1. Demand and audience growth
Request the complete wishlist time series
Steamworks’ Sales & Activation Reports Portal provides wishlist additions, purchases, deletions, and regional reporting by country. Request daily data where possible, not only the current balance. Plot additions and deletions against every major event: the coming-soon page going live, a demo release, a festival, creator coverage, a paid campaign, a price change, or a trailer.
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- Slope: Is the audience growing steadily, or is almost all growth concentrated in one spike?
- Source: Separate organic Steam discovery, paid traffic, creators, festivals, press, and the studio’s own community.
- Timing: Record the date of each addition wave and whether those users converted at launch or remained dormant.
- Geography: Compare the country mix with the project’s supported languages, price plan, payment availability, and customer-support capacity.
- Persistence: Track deletions and the remaining balance after each campaign rather than assuming every addition is durable demand.
A coming-soon page, demo, festival appearance, creator video, or advertising campaign can produce very different kinds of wishlist. Treat the event and its downstream behavior as part of the metric.
“Because everyone wishlists for different reasons, there’s no formula to accurately predict sales from wishlists.”
That Steamworks warning means a wishlist count is an input to a scenario, not a promise of units sold. It also means two projects with the same balance can have radically different launch prospects.
Check audience quality by region and source
Ask for a source-level table showing impressions or visits, wishlist additions, cost, and eventual purchases. Calculate cost per wishlist and, when campaign attribution exists, cost per sale. A cheap wishlist from a broad campaign may be less valuable than a smaller, highly engaged audience in a target region. Do not infer quality from geography alone; reconcile regional demand with language support, price localization, and observed conversion.
2. Sales, price, and monetization
Get revenue by day and week
Request gross and net sales by day and week, units, list price, discount depth, refunds, regional price mix, and any platform, publisher, or revenue-share deductions. For a project with downloadable content or in-game purchases, separate base-game revenue from each additional source. State whether every figure is before or after refunds, taxes, payment fees, and contractual splits.
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Compare launch-week net revenue with the remaining production budget and content obligations. A strong first week can still leave an unfunded live-support plan, localization pass, console work, or promised post-launch content. Conversely, a modest launch may be adequate if the team has low burn and a realistic long-tail plan.
“One of the most reliable and accurate ways we’ve figured out to measure player interest is revenue.”
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Revenue is a stronger validation signal than store-page traffic by itself. Steam’s visibility guidance says traffic landing on a store page is not itself a visibility factor, and conversion rate is not a visibility factor. Traffic remains important for marketing economics, so use it to measure acquisition efficiency rather than to claim Steam will rank the game higher.
Model price and discount risk
Build separate cases for the planned launch price, launch discount, later discount schedule, and regional pricing. Show how many net units are required to fund remaining work at each price. Include refunds as a reserve rather than treating every transaction as permanent revenue. If a publisher or platform receives a split, model the project’s cash receipt, not the store’s gross sales figure.
3. Use wishlists as a range of scenarios
Do not insert one conversion percentage into a forecast and call it a prediction. Build low, base, and high cases with explicit assumptions for launch-wishlist balance, launch timing, price, review performance, marketing reach, and post-launch support.
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| Reference statistic | What it measures | How to use it |
|---|---|---|
| 10.5% median | Week 1 wishlist-to-sales conversion in a GameDiscoverCo 2024 sample | Use as a sample reference for a range, never as a guaranteed rate |
| 0.22x median | Launch-wishlist-balance-to-Month-1-sales ratio for a selected GameDiscoverCo 2025 subset of high-multiplier games with more than 50,000 launch wishlists | Keep the cohort definition intact; do not apply it to every genre or project |
| 292% median increase | Converting wishlists made during Next Fest versus the preceding two-week period, based on Valve data reported in a 2021-era analysis | Test whether the project achieved downstream sales or only a temporary event spike |
These figures use different samples, periods, and definitions. A project’s conversion can vary with genre, audience source, Early Access status, launch window, price, review score, demo quality, and marketing reach. Stress-test the model if launch slips, reviews are weaker than planned, paid reach is cut, or the game requires a large multiplayer population on day one.
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4. Discovery, demos, and events
Measure a demo beyond downloads
For every demo or festival appearance, request demo-page visits, starts, meaningful-session or completion rate, feedback themes, wishlists added, followers, creator coverage, peak concurrent users where available, and sales after the event. A large download count with short sessions, poor completion, no wishlist lift, or no later sales is weak validation.
For Steam Next Fest, record the event dates and compare the two weeks before, the event period, and the weeks after. The reported 292% median uplift concerns converting wishlists made during Next Fest relative to the preceding two weeks; it does not mean every download converts or that every genre receives the same lift.
Separate algorithmic discovery from purchased reach
Ask Steamworks traffic reports to classify organic Steam discovery, paid campaigns, creators, festivals, press, and owned-community links. Steam says algorithmic visibility is personalized and that wishlists are mostly not a ranking factor, with documented contexts such as Popular Upcoming. Therefore, a spike in impressions may reflect a campaign or event rather than a durable change in store ranking.
5. Reviews and product quality
Track level, volume, and direction
Request total review count, positive percentage, recent seven-day and 30-day trends, language and regional mix, refund comments, recurring complaints, and the relationship between each major update and sentiment. Read the complaint themes, not just the percentage. Repeated crashes, missing accessibility features, poor matchmaking, or unclear progression can create support costs and force deeper discounting even when the headline score looks acceptable.
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Steam’s visibility guidance says a game at Mixed or above (40% or higher) is generally not affected by review score for algorithmic visibility. Dipping below 40% into Mostly Negative reduces the likelihood of featuring. Treat 40% as a material downside threshold, not as a quality target. Review volume and trend still matter for product-market fit, support burden, refund risk, and the ability to sell at the planned price.
Connect reviews to operating risk
Ask which complaints are known, which are fixed, and which remain on the critical path. Match the answer to the patch schedule, QA capacity, and cash runway. A studio that cannot identify owners and dates for recurring launch complaints has a different risk profile from one with a funded corrective plan.
6. Engagement, retention, and refunds
Public Steam pages do not provide a complete retention picture. Request studio analytics for playtime distribution, repeat sessions, day-one, day-seven, and day-30 retention where the business model supports those measures, multiplayer concurrency, churn, and refund rates.
- Define each cohort’s start date and whether it includes demos, Early Access, free weekends, or paid users only.
- State platform coverage and whether the metric is calculated per account, purchaser, or active player.
- Provide raw exports or a read-only dashboard so retention can be reconciled with sales, reviews, and refunds.
- Separate launch-event activity from normal behavior after the event ends.
High playtime is not automatically healthy if it comes from a small group while most purchasers refund. Conversely, a short premium game may have good economics without day-30 retention. Interpret engagement against the project’s business model and content promise.
7. Production execution and financial runway
Request the metrics Steam cannot show
Steam reports do not reveal development budget, staffing cost, milestone variance, monthly burn, cash runway, remaining content scope, or financing commitments. Obtain these directly from management:
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- Milestone plan with acceptance criteria, owners, dependencies, and variance against the original schedule
- Current employees, contractors, rates, notice periods, and commitments that extend beyond launch
- Monthly cash burn and a cash forecast under base and downside cases
- Committed and uncommitted financing, payment dates, and conditions attached to each commitment
- Remaining art, engineering, QA, localization, certification, accessibility, and release-management work
- Critical-path dependencies, including external technology, co-development, licensing, or multiplayer services
- A downside plan if launch revenue misses the base case
Label every item as verified, management-reported, or unverified. A polished dashboard is not verification unless the underlying exports, contracts, payroll records, or bank evidence support it.
Build the investor model
Calculate months to content-complete, months of runway, burn under base and downside cases, launch marketing budget, post-launch support cost, revenue-share and publisher terms, refund reserve, and the minimum net sales volume required to break even. One simple break-even check is: fixed remaining costs divided by net revenue per unit after discounts, refunds, and contractual splits. Use a separate calculation for recurring in-game-purchase revenue when applicable.
Set a decision gate for each financing tranche. For example, require a playable build that meets written acceptance criteria, a stable burn rate, a funded QA plan, and a launch forecast that survives the low case before releasing the next tranche. The exact gate should match the project’s scope and risk; the discipline is more important than a universal percentage.
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When comparing two or more investments, normalize the variables before ranking them. Put these fields side by side:
| Axis | Questions to align |
|---|---|
| Market and model | Same genre and subgenre? Premium or free-to-play? Comparable price and discount policy? |
| Release state | Early Access, demo, or 1.0 launch? Same launch-window maturity? |
| Audience | Comparable target regions, languages, wishlist sources, and event exposure? |
| Product dependency | Single-player or multiplayer? Does success require a critical mass of concurrent users? |
| Execution | Similar team track record, headcount, remaining scope, and runway? |
| Validation | Comparable review quality, review volume, refund behavior, demo completion, and retention? |
| Marketing | Similar paid spend, creator reach, owned-community size, and attribution quality? |
VG Insights and GameDiscoverCo can provide comparative market views and benchmarks, but check their methodology, cohort dates, sample selection, and definitions before using a number in an investment memo. Do not compare a selected high-multiplier cohort with an all-project median.
A practical diligence sequence
- Verify the data source. Obtain Steamworks exports for wishlists, purchases, deletions, regions, traffic, and sales; preserve the export dates.
- Reconstruct the timeline. Mark store-page launch, demo, festival, creator, paid, pricing, and update events on the same chart as audience and sales changes.
- Reconcile demand with money. Match wishlist and traffic spikes to net sales, refunds, price, discount, and regional mix.
- Test the product. Review demo or current build, read recent and recurring complaints, and inspect completion, retention, concurrency, and support data.
- Audit execution. Tie remaining scope to milestones, staffing, burn, dependencies, and cash on hand.
- Run scenarios. Model low, base, and high outcomes with delayed launch, weaker reviews, lower reach, and higher support cost.
- Set investment conditions. Define evidence-based milestones, reporting cadence, tranche gates, and the downside response before signing.
What a credible investment case looks like
A credible case does not rely on one impressive metric. It shows where demand came from, how much of it converted to net revenue, whether players stay and recommend the product, and whether the team can finish and support it without an emergency financing round. It also states what is unknown, who can verify it, and which assumption would most damage the return if it proves wrong.
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