How it works:
(Forecasting Methodology)

Given a Steam ID, we first filter all Steam games based on relevant tags, genres, and recency to identify a comparable set of titles.

We then group these comparable titles into competitor clusters using metrics such as Steam Score, Concurrent User Count (CCU), and Average Play Time, as well as trends in follower growth, review activity, and wishlist growth over time.

Using these competitor benchmarks, we build a predictive model to estimate click-to-wishlist conversion rates. This model uses a weighted index derived from the latest industry benchmarks.

Finally, by integrating these conversion rates with our internal GATEWAY ad campaign performance data, we calculate the estimated number of wishlists that can be achieved within a given budget.

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