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This paper applies posterior sampling to a seller that must learn both its ad-click response and its market strength from censored data, where zero bids reveal nothing and purchases only follow clicks. The reported gains come from a simulated online florist, so real-world transfer is untested. The most useful takeaways for ML readers are the failure modes: point-estimate planners can stop bidding and never learn that bidding pays, and customers who wait for a discount can be misread as lost sales, which biases the learner's view of its market.
Learning to bid and bundle: a posterior-sampling agent for online retail
arXiv:2611.00165v1 Announce Type: new Abstract: An online retailer launching a perishable product bids for sponsored-search clicks, discounts a bundle with an always-stocked staple and displays its remaining stock, which raises willingness to pay. It knows neither how clicks respond to its bid nor how strong and dispersed its market is, and learns both over repeated seasons from data its own decisions censor: a zero bid reveals nothing about paid clicks, and choices follow only clicks. We design a posterior-sampling agent for it. Each period it draws a market from an exact posterior and posts the bid and discount of that market's exactly solved season plan, so every action is one a fully informed seller could take. In a simulated online florist it has the lowest regret averaged over markets. In weak markets it gives up 12% to 23% of one season's revenue over 500 seasons, four to seven times less than drawing once per season. A planner acting on point estimates can stop bidding and never learn that bidding pays. The agent escapes after about three seasons on average, against fifty with one draw per season. Where displayed scarcity keeps willingness to pay above the posted price, the discount needs no learning. Customers waiting for the discount to return to its recent level look like lost sales, so frequent waiting makes the agent underrate its market. One discount per season removes waiting at a cost of 5.9% of clairvoyant revenue in a weak market and none in a strong one.
Abstract:An online retailer launching a perishable product bids for sponsored-search clicks, discounts a bundle with an always-stocked staple and displays its remaining stock, which raises willingness to pay. It knows neither how clicks respond to its bid nor how strong and dispersed its market is, and learns both over repeated seasons from data its own decisions censor: a zero bid reveals nothing about paid clicks, and choices follow only clicks. We design a posterior-sampling agent for it. Each period it draws a market from an exact posterior and posts the bid and discount of that market's exactly solved season plan, so every action is one a fully informed seller could take. In a simulated online florist it has the lowest regret averaged over markets. In weak markets it gives up 12% to 23% of one season's revenue over 500 seasons, four to seven times less than drawing once per season. A planner acting on point estimates can stop bidding and never learn that bidding pays. The agent escapes after about three seasons on average, against fifty with one draw per season. Where displayed scarcity keeps willingness to pay above the posted price, the discount needs no learning. Customers waiting for the discount to return to its recent level look like lost sales, so frequent waiting makes the agent underrate its market. One discount per season removes waiting at a cost of 5.9% of clairvoyant revenue in a weak market and none in a strong one.
37 pages, 5 figures; online supplement included as an ancillary file. Under review at Expert Systems with Applications
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2611.00165 [cs.AI]
(or arXiv:2611.00165v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2611.00165 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
Supplementary-material.pdf
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