WillitWishlist

Methodology

Useful, honest, and clearly labelled.

WillitWishlist helps developers make better marketing decisions without pretending that private Steam metrics are publicly knowable.

Public data

Steam metadata, public store-page information, visible review aggregates, public reviews where allowed, tags, prices, release dates, supported features, and publicly visible player signals from approved sources.

Estimated data

Wishlist potential, sales ranges, revenue, retention proxies, and competitive positioning are estimates. They should be used for planning, not as guaranteed facts.

Proxy data

True retention and wishlist conversion are usually private. The product uses proxies such as review themes, playtime-at-review, achievement completion, demo availability, current-versus-peak players, and complaint patterns.

Similarity matching

Comparable games are matched by tags, genre, subgenre, price, release window, review score, review count, player signals, feature keywords, multiplayer status, visual style, and positioning.

Report generation

The report summarises review themes, repeated praise, complaint patterns, store-page copy, and similar-game lessons. It should label the signals used and avoid pretending estimates are exact.

Confidence levels

Low confidence means limited input. Medium means description, tags, audience, trailer, price, or competitors were supplied. High means the developer provided store assets, Steam page, wishlist data, demo data, analytics, or current player/review data.