What Is a Steam Wishlist Worth? (Dollar Value, 2026)

Last updated: 2026-06-30 · Reading time: 7 min

Short answer: a Steam wishlist is worth roughly $2-$8 of gross lifetime revenue for typical indie pricing, centering near the well-known ~$5-per-wishlist rule of thumb. Net of Steam’s 30% cut, that’s about $1.40-$5.60 to you. But the value varies enormously — by conversion rate, price, region, and especially wishlist source — so $5 is a sanity check, not a budget plan.

The decomposition (the part worth citing)

A wishlist’s dollar value isn’t a constant — it’s the product of four levers. Write it out and the ~$5 average stops being magic:

Gross $/wishlist ≈ lifetime conversion × price × region factor × (1 − launch discount)

Then net-of-Steam is ~70% of gross, because Steam takes a 30% cut on virtually all indie revenue. (The cut drops to 25% above $10M lifetime and 20% above $50M — thresholds essentially no indie reaches, so assume 30%.)

A worked example, end to end

Take a $20 strategy game with a 10% launch-week discount, a lifetime conversion in the middle of the indie band, and a region mix that realizes ~85% of headline price:

Gross = 0.30 × $20.00 × 0.85 × 0.90 ≈ $4.59 per wishlist
Net   = $4.59 × 0.70 ≈ $3.21 per wishlist

That lands right where the Boxleiter $5 heuristic predicts on the gross line — which is exactly why the rule of thumb survives. But notice how sensitive it is: swing conversion to the 20% floor and gross drops to ~$3.06; push it to 40% and gross rises to ~$6.12. The single biggest driver of how much a wishlist is worth is the conversion rate behind it — and that’s the lever that moves most by source. Each input is a range, so the honest output is a band, not a point.

Why wishlists are not all worth the same

1. Source (the largest single factor)

Organic Steam-discovery wishlists convert roughly 2-4x better than paid-campaign wishlists. Someone the algorithm surfaced is already in buying mode; someone clicking through from a TikTok ad is mid-scroll. So an organic wishlist can be worth several times a paid one in realized dollars — even though both show up as “a wishlist” in your Steamworks count. The ~$5 average blends both together, which is why ad-heavy launches under-perform the heuristic. The full breakdown is in the wishlist conversion-rate guide.

2. Genre and price

Higher-priced, higher-engagement genres (strategy, sim, RPG at $25+) push both the conversion and the price terms up, so each wishlist is worth more in absolute dollars. Crowded low-price segments (action/platformer, $10-15, competing with free-to-play) compress both. This is the same effect that makes the Boxleiter multiplier range from ~$4 to ~$8 by genre rather than sitting at a flat $5.

3. Region mix

Steam regional pricing means a US/EU wishlist realizes close to headline price, while a CIS/SEA wishlist realizes far less per unit. A wishlist base weighted heavily toward low-price regions delivers fewer revenue dollars per conversion than the headline count implies — the region factor in the formula above is doing real work.

4. Trajectory shape

A wishlist count that grew linearly across the dev cycle converts better — and is therefore worth more — than one that spiked around a demo or a Next Fest. Spike-driven wishlists carry more “wait for sale” deferrers; linear-growth wishlists skew toward genuinely interested buyers. Two games with identical wishlist counts can have materially different per-wishlist value purely from the shape of how they got there.

The practical takeaway: budget against the floor, not the average

Here’s where the dollar-value number gets dangerous. When you’re deciding ad spend, it’s tempting to set your target cost-per-wishlist just under the average value — pay $4 to acquire a wishlist “worth $5” and book the spread. Don’t. Two reasons:

1. Paid wishlists are below-average by construction. The ~$5 figure is a blend of high-converting organic and low-converting paid. The wishlist your ad buys converts at the paid rate, not the blended one — so budgeting at the average double-counts optimism.
2. The average is right ~half the time and wrong the other half. Budget against the P10 (downside) value of your estimate, not the mean. If conversion disappoints, the P10 is the figure at which you’re still solvent; the average is the figure that bankrupts you when the launch lands on the wrong side of the distribution.

This is the same discipline behind a calibrated forecast: plan against the floor, communicate the median, treat the ceiling as upside.

How this ties to the Boxleiter method

The “a wishlist is worth ~$5” figure is the Boxleiter method, just stated per-wishlist instead of as a total (revenue ≈ wishlists × $5). The decomposition above is what’s hiding inside that single multiplier — conversion, price, region, and discount — which is exactly why the multiplier varies by genre and breaks down at the tails. Boxleiter also has a review-multiplier cousin (estimating sales from review counts) that runs into the same averaging problem. For the broader question of what a launch actually grosses, see how much do Steam games make.

How to estimate your own wishlist’s dollar value

You can run the formula above for your specific game in about five minutes:

1. Pick a conversion band for your genre. Lifetime is typically 20-40% (launch-week 10-25%). Strategy/sim/roguelike high; visual novel/F2P-adjacent low.
2. Set your effective price. Planned store price × (1 − launch discount).
3. Apply a region factor. ~1.0 for a US/EU-heavy base, ~0.7-0.9 for a CIS/SEA-weighted one.
4. Multiply for gross, then take 70% for net. Carry the low and high ends of each input through to get a band.
5. Haircut paid wishlists and budget against the P10. Discount paid-campaign conversion to roughly a third of organic, then size spend against the downside.

For a real per-game number rather than a hand-cranked band, run a calibrated forecast that integrates your genre comp set, price, and wishlist trajectory directly — instead of multiplying a wishlist count by a flat dollar figure.

Frequently asked questions

Built by Greg C. — senior software engineer with production ML experience in calibrated prediction. Steam Launch Forecaster trains a CQR-calibrated model on a 77K-app Steam corpus. See the methodology →