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cloud.google.com·6h ago
Data Science
Towards Data Science·Sep 4·5 min read
Optimal Traffic Allocation Under Heterogeneous Variant Cost
TL;DR
Cost-optimal traffic allocation can significantly reduce experimental budgets by balancing the precision gained from additional subjects against their costs. This approach can save organizations 5% to 15% of their budget, translating to substantial financial savings.
✦ Why It Matters
Evaluate your current traffic allocation strategy to identify potential budget savings today.
Key Takeaways
- Use the square root formula for optimal sample ratios.
- Consider cost differences when designing experiments.
- Evaluate trade-offs between speed and budget in experiments.
- Document secondary questions to ensure adequate power in treatment groups.
How It Works
The optimal sample ratio is determined using a square root formula based on the inverse of the marginal costs of treatment and control groups. This approach accounts for diminishing returns on precision as sample sizes increase, allowing for a more cost-effective allocation of subjects.