Signaling Targeting Cost Through List Price

Peiwen Yu () and Jiahua Zhang ()
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Peiwen Yu: School of Economics and Business Administration, Chongqing University, Chongqing 400044, China
Jiahua Zhang: Institute of Supply Chain Analytics, Dongbei University of Finance and Economics, Dalian 116025, China

Management Science, 2025, vol. 71, issue 3, 2733-2750

Abstract: Data analytics enable firms to offer personalized prices to targeted consumers but at a cost. We study a competitive personalized pricing game where the entrant is uncertain about the incumbent’s targeting cost. We demonstrate that implementing personalized pricing through a “list price-discount” scheme allows the incumbent to signal its targeting cost via the list price. This signaling mechanism is effective because the list price serves as a price ceiling, which limits the incumbent’s ability to extract consumer surplus through personalized discounts. The high-cost incumbent can strategically set its list price below the full-information level to separate itself from the low-cost incumbent. Interestingly, the high-cost incumbent prefers separating over pooling only when there is a moderate variation in the incumbents’ targeting costs. Personalized pricing can affect firms differently, benefiting the incumbent but hurting the entrant. Asymmetric information about targeting costs weakens the high-cost incumbent’s incentive to offer personalized discounts, resulting in lower total targeting costs and potentially increasing social surplus. These findings shed light on government regulations and transparency policies regarding personalized pricing.

Keywords: personalized pricing; targeting cost; list price; signaling; competition (search for similar items in EconPapers)
Date: 2025
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