Gamer Persona Framework — User Behavior Clustering
A Python-assisted research workflow that transformed gameplay evidence, transcript signals, and clip descriptions into eight gamer persona clusters for marketing campaign planning and user segmentation.
- Role
- AI & Data Research
- Company
- Eklipse.gg
- Input
- Transcripts + Clips
- Status
- Completed
Background
Marketing teams often need a clear way to understand different gamer audiences, but raw stream data is messy. Behavior signals are scattered across clip titles, descriptions, voice transcripts, game context, and emotional reactions.
This project turned those scattered signals into a structured persona framework. The work started from requirement gathering with Python, then moved into text extraction, signal mapping, prompt-assisted clustering, and final persona validation.
- Streamer behavior signals were unstructured and hard to compare.
- Campaign planning needed more useful gamer audience types.
- Manual persona grouping could become subjective without evidence rules.
- Gather and structure persona requirements using Python.
- Map transcript and clip signals into behavioral markers.
- Deliver campaign-ready gamer persona clusters.
Research Pipeline
The workflow moved from raw evidence collection to a usable persona taxonomy, with each step designed to reduce noise and preserve explainable cluster logic.
Used Python to gather and organize requirements around what persona outputs should support: user segmentation, campaign messaging, behavioral markers, and confidence notes.
Structured clip metadata, transcript snippets, descriptions, emotional signals, game context, and topic indicators into a comparable evidence format.
Mapped repeatable signals such as screaming dominance, analytical commentary, objective play, comedy framing, lore curiosity, rage patterns, tactical tension, and Just Chatting storytelling.
Designed prompts to group users by recurring behavior patterns, separate primary and secondary personas, and keep cluster explanations tied to observable evidence.
Reviewed overlap and outliers, documented confidence levels, and finalized persona definitions with source notes that make the taxonomy easier to maintain.
Persona Results
The final framework contains eight distinct gamer persona clusters, each grounded in clip behavior, transcript style, topic composition, and emotional signals.
Generates clips through vocal reactions to jump scares, chases, and ambushes.
Explains build theory, game systems, and strategy decisions while playing.
Creates highlights through objective play, team awareness, and clutch moments.
Turns glitches, absurd gameplay, and unexpected moments into comedy content.
Engages with story reveals, world discovery, lore, and survival tension.
Uses competitive frustration, opponent callouts, and escalation as content.
Produces clips from firefights, threat detection, and high-stakes survival.
Centers content on storytelling, IRL moments, and community interaction.