Online Game Data Visualization: Key Metrics, Methods, and Tools

According to the 2021 China Game Industry Report, the domestic game user base reached 666 million, generating massive amounts of data that make it difficult to identify which game features are effective, which factors keep players engaged, or which levels are most challenging.

Data visualization enhances cognition by converting data into interactive graphics, offering new perspectives for analysis.

The article introduces basic online game data visualization methods, the key metrics they highlight, and recommends web‑based visualization tools.

1. In‑game vs. out‑of‑game visualization – In‑game visualizations include health bars, mana bars, skill‑tree diagrams, and area charts for economy differences. Out‑of‑game visualizations target industry professionals, such as dynamic bar charts showing game popularity trends, and aim to keep players engaged.

2. Core metric visualization – The most important metrics are activity (DAU, MAU, average session length), retention/loss (daily/weekly retention rates, churn analysis), and payment (ARPU, ARPPU, ARPDAU, first‑purchase time). Line charts are used for trends, heatmaps for retention, Sankey diagrams for churn flows, and pie or bubble charts for payment distribution.

3. Scenario‑specific visualizations – Custom visualizations include heatmaps for map activity, relationship graphs for social networks, and timeline stacked bar charts for player behavior sequences.

4. Web visualization tools – Developers with programming skills can use JavaScript libraries such as ECharts to create interactive charts. Non‑programmers can use BI products like FineBI or Tableau, which provide drag‑and‑drop interfaces for data source selection and chart generation.

Overall, game developers primarily use line, bar, and pie charts for activity, retention, and payment metrics, while heatmaps, Sankey diagrams, and relationship graphs are common for more specialized analyses.