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pro vyhledávání: '"Duncan, Simester"'
We propose and test a new adaptive conjoint analysis method that draws on recent polyhedral “interior-point” developments in mathematical programming. The method is designed to offer accurate estimates after relatively few questions in problems i
Externí odkaz:
http://hdl.handle.net/1721.1/3800
Autor:
Diego Aparicio, Duncan Simester
Publikováno v:
Marketing Science. 41:1057-1073
Price frictions reduce the success of new products and impact retailers’ product assortments.
Publikováno v:
The Journal of Finance. 77:3191-3247
The short and long-run impact of empowering customers in corporate social responsibility initiatives
Publikováno v:
Journal of Economic Behavior & Organization. 192:616-637
Rather than just informing customers about their corporate social responsibility initiatives, many for-profit firms have sought to engage their customers in these activities. Previous research assessing the impact of these programs has focused on sho
Publikováno v:
Marketing Science. 39:1033-1038
This editorial introduces the special issue on marketing science and field experiments. We compare the characteristics of the papers that were submitted and accepted for the special issue and provide several recommendations for researchers. In genera
Publikováno v:
SSRN Electronic Journal.
Publikováno v:
SSRN Electronic Journal.
Publikováno v:
SSRN
Champion versus challenger field experiments are widely used to compare the performance of different targeting policies. These experiments randomly assign customers to receive marketing actions recommended by either the existing (champion) policy or
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::852391f1d4026416e3a0a3f326bfcd74
https://hdl.handle.net/1721.1/135950
https://hdl.handle.net/1721.1/135950
Publikováno v:
SSRN Electronic Journal.
Many firms want to target their customers with a sequence of marketing actions, rather than just a single action. We interpret sequential targeting problems as a Markov Decision Process (MDP), which can be solved using a range of Reinforcement Learni