Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data

Benjamin Letham, Wei Sun, Anshul Sheopuri
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(1):217-225, 2014.

Abstract

Bundle discounts are used by retailers in many industries. Optimal bundle pricing requires learning the joint distribution of consumer valuations for the items in the bundle, that is, how much they are willing to pay for each of the items. We suppose that a retailer has sales transaction data, and the corresponding consumer valuations are latent variables. We develop a statistically consistent and computationally tractable inference procedure for fitting a copula model over correlated valuations, using only sales transaction data for the individual items. Simulations and data experiments demonstrate consistency, scalability, and the importance of incorporating correlations in the joint distribution.

Cite this Paper

BibTeX
@InProceedings{pmlr-v32-letham14, title = {Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data}, author = {Letham, Benjamin and Sun, Wei and Sheopuri, Anshul}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {217--225}, year = {2014}, editor = {Xing, Eric P. and Jebara, Tony}, volume = {32}, number = {1}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/letham14.pdf}, url = {https://proceedings.mlr.press/v32/letham14.html}, abstract = {Bundle discounts are used by retailers in many industries. Optimal bundle pricing requires learning the joint distribution of consumer valuations for the items in the bundle, that is, how much they are willing to pay for each of the items. We suppose that a retailer has sales transaction data, and the corresponding consumer valuations are latent variables. We develop a statistically consistent and computationally tractable inference procedure for fitting a copula model over correlated valuations, using only sales transaction data for the individual items. Simulations and data experiments demonstrate consistency, scalability, and the importance of incorporating correlations in the joint distribution.} }
Endnote
%0 Conference Paper %T Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data %A Benjamin Letham %A Wei Sun %A Anshul Sheopuri %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-letham14 %I PMLR %P 217--225 %U https://proceedings.mlr.press/v32/letham14.html %V 32 %N 1 %X Bundle discounts are used by retailers in many industries. Optimal bundle pricing requires learning the joint distribution of consumer valuations for the items in the bundle, that is, how much they are willing to pay for each of the items. We suppose that a retailer has sales transaction data, and the corresponding consumer valuations are latent variables. We develop a statistically consistent and computationally tractable inference procedure for fitting a copula model over correlated valuations, using only sales transaction data for the individual items. Simulations and data experiments demonstrate consistency, scalability, and the importance of incorporating correlations in the joint distribution.
RIS
TY - CPAPER TI - Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data AU - Benjamin Letham AU - Wei Sun AU - Anshul Sheopuri BT - Proceedings of the 31st International Conference on Machine Learning DA - 2014/01/27 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-letham14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 32 IS - 1 SP - 217 EP - 225 L1 - http://proceedings.mlr.press/v32/letham14.pdf UR - https://proceedings.mlr.press/v32/letham14.html AB - Bundle discounts are used by retailers in many industries. Optimal bundle pricing requires learning the joint distribution of consumer valuations for the items in the bundle, that is, how much they are willing to pay for each of the items. We suppose that a retailer has sales transaction data, and the corresponding consumer valuations are latent variables. We develop a statistically consistent and computationally tractable inference procedure for fitting a copula model over correlated valuations, using only sales transaction data for the individual items. Simulations and data experiments demonstrate consistency, scalability, and the importance of incorporating correlations in the joint distribution. ER -
APA
Letham, B., Sun, W. & Sheopuri, A.. (2014). Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data. Proceedings of the 31st International Conference on Machine Learning, in Proceedings of Machine Learning Research 32(1):217-225 Available from https://proceedings.mlr.press/v32/letham14.html.

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