Data-Driven Game-dev Business: Building a Profitable Portfolio
summary
Builds a genre-ranking pipeline that aggregates sales, Metascore and user score, marketing proxies (YouTube views/likes/comments), IP/franchise labels and platform/region splits across 60 subgenres, normalizes metrics (CAGR, average Net Sales Revenue, HHI concentration, release share) and weights them into a single Segment Attractiveness Index. Presents per-genre feature importances, Metascore-based revenue forecasts and a launch-calendar K‑factor, pilots the model to pick a next AA/AAA title, and keeps the raw purchased data closed.
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chapters (15)
- 0:00Welcome
- 1:07Creative to portfolio strategy
- 2:51Business analysis in gaming
- 3:54Market trends
- 5:39Choosing profitable genres
- 7:44Segment Attractiveness Index
- 9:09Data foundation for analysis
- 11:36Aggregating genre data
- 13:01Calculating attractiveness index
- 14:24Presenting data financially
- 16:52Genre profiles for stakeholders
- 18:19Next steps after selection
- 19:43Key takeaways
- 20:46Analyst’s role in value
- 21:09Q&A
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