Inspiration
About the Project — Star Wars Fandom Atlas (Lite) Inspiration
We wanted a fast, credible way to turn Star Wars fan preferences into actionable creative and marketing choices—the kind a team can A/B test immediately. The dataset’s structure (favorite hero, villain, film, soundtrack, spaceship, planet, robot) is perfect for prototyping co-occurrence analytics and turning “what fans like” into promo focal points.
Star Wars Fandom Atlas Lite_Yan…
What We Built
A compact preference atlas with:
Top-N rankings (e.g., Obi-Wan Kenobi among heroes; Episode V among films; R2-D2 among robots).
A film × hero co-occurrence heatmap to surface high-leverage pairs for campaigns.
Concrete recommendations: anchor creatives on top film–hero pairs; use villains for buzz and robots for conversion; bundle popular spaceships/robots for merch.
Star Wars Fandom Atlas Lite_Yan…
A lite slide deck (8 pages) with clear visuals and presenter notes ready for a 7-minute pitch.
Star Wars Fandom Atlas Lite_Yan…
How We Built It
Data prep: CSV ingest, de-duplication, null handling, light normalization (e.g., name variants).
Measures: category frequencies; co-occurrence matrices across preference fields; simple lift for pair salience.
Visualization: Top-N bar charts; film × hero heatmap; dual-panel layouts for rapid reading.
Reproducibility: scripts to regenerate charts and slides; explicit notes where AI scaffolding helped.
What We Learned
Anchors matter: Top heroes and films are reliable attention anchors for promos.
Role separation works: Villains drive buzz, robots drive affinity & conversion; pairing world-building (spaceships/planets) with top characters improves memorability.
Simple stats go far: Co-occurrence + lift gives clear, defensible picks for A/B tests.
Star Wars Fandom Atlas Lite_Yan…
Challenges
Synthetic realism: Insights emphasize workflow (not market claims); future work will validate on real platforms.
Category imbalance & long tails: Careful chart design for readability; focus on Top-N and representative pairs.
Entity consistency: Minor normalization to keep names consistent across fields.
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