TY  - INPR
AV  - public
Y1  - 2022/08/30/
EP  - 25
TI  - Optimal Price Targeting
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
UR  - https://doi.org/10.1287/mksc.2022.1387
PB  - INFORMS
SN  - 0732-2399
N2  - The paper compares the profitability of personalized pricing policies that are generated from different models of demand and using different data inputs.
ID  - discovery10156641
A1  - Smith, Adam N
A1  - Seiler, Stephan
A1  - Aggarwal, Ishant
KW  - Social Sciences
KW  -  Business
KW  -  Business & Economics
KW  -  targeting
KW  -  personalization
KW  -  heterogeneity
KW  -  choice models
KW  -  machine learning
KW  -  PROMOTIONS
KW  -  BRAND
KW  -  STRATEGIES
KW  -  VARIABLES
KW  -  SELECTION
KW  -  ONLINE
KW  -  SMOTE
KW  -  MODEL
JF  - Marketing Science
ER  -