Use n8n to track Steam player feedback

Aswin Behera built a competitor-intelligence agent while working on his indie game, serverBound. He wanted to understand why comparable Steam releases attracted attention and what players praised or disliked without repeatedly reading reviews by hand. The workflow gathered popular releases and enriched them with player counts and review data.

An AI analysis step summarized likely discussion topics, emerging complaints and reasons a game resonated. Each competitor received a Notion page containing an application identifier, release date, player count, review score, popularity rank and the analysis. Behera showed an example page and linked the Notion workspace.

The build exposed practical limits. Some sources blocked requests, Notion's text-block size required splitting large outputs, and differently shaped Steam responses needed careful merging. Gemini sometimes treated release dates as future events, so Behera added context processing. He reports receiving daily competitive insights after these changes. The result is a maintained research workspace with documented data and model problems, rather than a guarantee that every interpretation of player sentiment is accurate.

Prompt
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Source: dev.to · Aswin Behera