Couverture de Inside Minecraft's Experimentation Strategy Part 2: Experimentation Adoption, and AI in the Product Lifecycle

Inside Minecraft's Experimentation Strategy Part 2: Experimentation Adoption, and AI in the Product Lifecycle

Inside Minecraft's Experimentation Strategy Part 2: Experimentation Adoption, and AI in the Product Lifecycle

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Setting the scene for this one and diving right in. Tim talks about successful experimentation being cultivated, not designed or manufactured and why building adoption for experimentation (or any organisational change) comes down to a simple question: are you on the soapbox or are you driving the bus?

Tim breaks down why correcting people (“you’re doing it wrong”) creates defensiveness, why the “soapbox” approach is the shortcut, and how the “bus ride” approach builds sustainable adoption. He shares an example from Minecraft, walking through a product lifecycle approach, observation, theory, test, bringing teams along for research, insights, solutioning tied to OKRs, prioritisation, experimentation, analysis, and implementation.

They then shift into AI and experimentation: where Tim sees real value today (speed from large datasets to insights), where he’s sceptical (AI-generated high-fidelity designs and experiment variations), and where opportunities may be strongest (analytics, projections, predictive modelling, multi-armed bandits, variance reduction, and qualifying/disqualifying users).

They close with Tim sharing why he opened time slots for people to chat about experimentation, why he was encouraged by his manager, and why LinkedIn is the best way to connect.

What’s covered (from the conversation):

  • “Soapbox vs bus ride” and sustainable adoption

  • Defensiveness, reactance, and avoiding “shaming” in change efforts

  • Minecraft example: bringing teams through the product lifecycle with experimentation

  • Separating observation → theory → test

  • Tying solutioning to OKRs and prioritising experiments

  • AI in discovery: summarising large qualitative (and quantitative) datasets quickly

  • Spot-checking + context pitfalls (e.g., “creeper,” profanity interpreted as negative)

  • Prompt templates vs AI agents with guardrails

  • Scepticism on AI for full-fidelity designs and experiment variations

  • AI/ML in analytics: sample size, test duration, forecasting, bandits, variance reduction

  • Community learning + LinkedIn as the main contact channel

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