The rapid development of the Internet industry drives the demand for rapid iteration of products. As a core tool for data-driven decision-making, A/B testing faces the challenges of high concurrency performance bottlenecks, traffic cost control and reliability of statistical methods. This study adopts the idea of dynamic strategy distribution to construct a modular architecture, decoupling the information production cache consumption module. It combines the traffic layered reuse model and the double-sided sliding window algorithm to achieve dynamic traffic management, and integrates sequential testing and fixed level testing to construct an anti data intrusion traffic management model. The system achieves a thousand level QPS processing capability and millisecond level response, supports experimental flow expansion, bucket deletion, and traffic push operations, shortens the cycle and reduces sample consumption through experimental self iteration, and ensures statistical reliability. Research has found that dynamic policy distribution significantly improves system lightweighting and scalability, while data privacy models achieve traffic and time cost savings while ensuring data reliability. The integration of technology stacks forms a fully automated closed-loop from experimental configuration to result analysis. This study provides a systematic solution for optimizing multi-channel conversion paths, promoting the efficient application of A/B testing in complex business scenarios.

Xiangping Yu. Exploration of Multi-Channel Conversion Path Optimization Methods Based on A/B Testing. Machine Learning Theory and Practice (2026), Vol. 6, Issue 1: 30-37. https://doi.org/10.38007/ML.2026.060104.

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