Data-Based Decision Making Throughout Your Game's Lifecycle
summary
Explains how to turn public signals into concrete projections: uses a rule of thumb of 40–70× reviews → estimated units sold, tracks review/day ratios, mines platform top-seller lists by doing a simple weekly top 50 scraping to spot discount or DLC lifts, and compares press-reported sales (example: 4 million units over ~625 days). Describes running remote concept tests (example: usertesting.com character survey with 30 players), A/B capsule-art changes, and correlating discount windows with daily unit spikes.
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chapters (21)
- 0:00Intro
- 0:26Data-Based Decision Making throughout Your Game's Lifecycle
- 2:52I'm Alexis Miller and I love data
- 6:01Prototype/Concept Phase
- 9:02There are public sales numbers if you look for them
- 10:41Start with public ranking charts like top sellers
- 11:47Then turn that data into super useful insights
- 15:49Analyze common themes from both positive and negative reviews, from successes and flops
- 17:54There's never too much information about competitors
- 19:30Test early concepts with players with surveys or interviews
- 20:26Simple online survey gave great direction to character choice
- 22:09Production/Development Phase
- 23:20You don't need your own research department to access player personas and market data
- 25:02But if you have more time, you can mine existing reports for greater Insights
- 29:28Test store assets and marketing material
- 33:00Here's some basic steps and tips for creating projections
- 34:17Seasonality and discounts are extremely important context for setting sales expectations
- 35:13Post-Launch Phase
- 35:39If everything is a priority, then nothing is a priority
- 36:36Pay attention to discounts!
- 37:55Cultivate a data-friendly culture within your team
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