How to count wishlists for game on Steam based on number of its followers
It is hard to guess how many wishlists a game has on Steam since this metric is only available to game developers. But there is a way to estimate the number by applying a certain multiplier to the follower count.
Simon Carless shared the current “follower to wishlist” multipliers for unreleased games on Steam (as of June 2023) in the GameDiscoverCo newsletter. The data is based on an anonymous survey of more than 125 game developers.
Here are the key takeaways:
- Wishlists are currently between 7x and 20x the followers (an average is 12.37x and a median is 12x), in other words, you need to multiply the number of Steam followers by 12 to estimate the number of wishlists;
- The median and average multipliers have increased by 25% and 28%, respectively, in the two years since GameDiscoverCo’s last survey;
- The reason for such an increase might be that game marketers are now focusing more on adding wishlists than followers, or that today’s Steam users follow games less often;
- “We’d speculate that games with a lot of wishlists are sometimes more ‘mainstream’, so might attract a mass of slightly more casual players, who are less likely to follow titles,” Carless noted;
- Average multipliers also differ depending on game tags: they are significantly lower for deep/complex PC titles (4X strategies — 7.5x, turn-based — 9x, survival — 9.5x) than for narrative-driven games (story-rich — 13.2x, relaxing — 15.7x, puzzle — 15.9x);
“Lower multipliers & higher sales are expected if you’re in a deeper PC game genre, where players expect GaaS-style news updates & multiple layers of gameplay mechanics,” Carless concluded, adding that “you should provide those updates.”
More data and insights can be found in the full newsletter.
And it is better to remember that median/average multipliers can’t provide 100% correct results for every unreleased game on Steam because there are always exceptions. The same goes for the Steam reviews/sales ratio, which is great for estimations but still far from perfect.
