The requirement that at any given time, all customers see the same prices for the same products necessitates innovation in the design of A/B experiments.
The prices of products in the Amazon Store reflect a range of factors, such as demand, seasonality, and general economic trends. Pricing policies typically involve formulas that take such factors into account; newer pricing policies usually rely on machine learning models.
With the Amazon Pricing Labs, we can conduct a range of online A/B experiments to evaluate new pricing policies. Because we practice nondiscriminatory pricing — all visitors to the Amazon Store at the same time see the same prices for all products — we need to apply experimental treatments to product prices over time, rather than testing different price points simultaneously on different customers. This complicates the experimental design.
In a paper we published in the Journal of Business Economics in March and presented at the American Economics Association’s annual conference in January (AEA), we described some of the experiments we can conduct to prevent spillovers, improve precision, and control for demand trends and differences in treatment groups when evaluating new pricing policies.
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Really good research! Noticed this myself, since a lot of hunting gear/firearm accessory prices corelate with hunting seasons(usually it’s rising right before seasons and then slowly drops during, same trend with guns itself like https://gritrsports.com/guns/ but since those aren’t on Amazon itself, it doesn’t matter as much).