Bài này ở Dataconomy nói khá rõ về những lần tiến hoá trong việc phân tích dữ liệu cho game. Cá nhân mình thì cho rằng mình đang ở giữa mức 1 và 2, có hơi hướng ở phần 3 khi có kết hợp công nghệ xử lý dữ liệu như Hadoop, Hive… 🙁 – khá buồn. Chuyện buồn này có thể xảy ra do mình đứng ở marketing-side của phần game-người-ta, không đi được cùng developer nhiều như các bạn làm marketing cho studio. Bạn đọc thêm để biết rõ nhé.

Over the last few years, there has been a significant shift in the games industry’s use of analytics. This evolution can be tracked across three distinct phases:

Analytics 1.0 – This was focused solely on game performance; dashboard reporting of what had happened in the game, but without providing the clarity that would enable developers to know where any issues may lie, or how to solve them.

Analytics 2.0 – This phase was about changing the game at the design level. Developers could see where the problems were, but could only implement broad-brush and one-size-fits-all changes to the game.

Analytics 3.0 – The most current approach. Deep Data – the combination of a large number of data points, incredibly fast database technology and multiple data sources – enables the gaming experience to be personalized for individual players within segments, based on their engagement and playing style.Dataconomy

In a recent deltaDNA survey of in-game advertising, 50% of games with 100k+ DAU (daily active users) said they provided different experiences to different non-paying players.Dataconomy

Leave a Reply