Out of a zillion options, customers want to find the one item perfect for them. The recommendations team makes that happen by leveraging advanced machine learning techniques and our immense datasets to provide ever improving recommendations. A key step in this process is experimentation, i.e. the design, launch and analysis of A/B tests to understand which algorithm provides the best customer experience. This blog post describes a simulation tool our Analytics team has built and used prior to every test launch. This tool has improved our test success rate and testing velocity dramatically, and led to new insights/improvements of our recommendations.
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