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Nonparametric Pricing Analytics with Customer Covariates [article]

Ningyuan Chen, Guillermo Gallego
2020 arXiv   pre-print
We propose a nonparametric pricing policy to simultaneously learn the preference of customers based on the covariates and maximize the expected revenue over a finite horizon.  ...  Personalized pricing analytics is becoming an essential tool in retailing.  ...  Online Appendix for Nonparametric Pricing Analytics with Customer Covariates A.  ... 
arXiv:1805.01136v3 fatcat:yga3fpe555hqfhepcqagbne57q

Tricked by Truncation: Spurious Duration Dependence and Social Contagion in Hazard Models

Christophe Van den Bulte, Raghuram Iyengar
2011 Marketing science (Providence, R.I.)  
Not accounting for right truncation can also lead to suboptimal pricing decisions and to erroneous assessments of variations in customer lifetime value.  ...  Truncation also tends to deflate the effect of time-invariant covariates.  ...  We consider three substantive issues: new product pricing without contagion, new product advertising with contagion, and customer lifetime value.  ... 
doi:10.1287/mksc.1100.0615 fatcat:yprefjxm5je7nkclh25tkyibwq

A survey of non-exchangeable priors for Bayesian nonparametric models [article]

Nicholas J. Foti, Sinead Williamson
2012 arXiv   pre-print
Such models are appropriate priors when exchangeability assumptions do not hold, and instead we want our model to vary fluidly with some set of covariates.  ...  in some covariate space.  ...  The volatility of the price of a financial instrument is a measure of the instrument's price variation. High volatility implies large changes in price and vice versa.  ... 
arXiv:1211.4798v1 fatcat:asigyslavjaafiqpqov7rx2pyi

Customer Acquisition Promotions and Customer Asset Value

Michael Lewis
2006 Journal of Marketing Research  
It begins with an analytical framework that describes the role of uncertainty in customer acquisition.  ...  The article is structured as follows: I begin with an analytical description of how consumer uncertainty may influence customer trial and subsequent behavior.  ... 
doi:10.1509/jmkr.43.2.195 fatcat:b2hmqfcapvclbhqaru4rg7mnuu

A Bayesian mixed logit–probit model for multinomial choice

Martin Burda, Matthew Harding, Jerry Hausman
2008 Journal of Econometrics  
We estimate the model using a Bayesian Markov Chain Monte Carlo technique with a multivariate Dirichlet Process (DP) prior on the coefficients with nonparametrically estimated density.  ...  We estimate the nonparametric density of two key variables of interest: the price of a basket of goods based on scanner data, and driving distance to the supermarket based on their respective locations  ...  with covariance matrix Σ.  ... 
doi:10.1016/j.jeconom.2008.09.029 fatcat:svprghyfcba3rmtl563hfbw3om

Challenges and opportunities in high-dimensional choice data analyses

Prasad Naik, Michel Wedel, Lynd Bacon, Anand Bodapati, Eric Bradlow, Wagner Kamakura, Jeffrey Kreulen, Peter Lenk, David M. Madigan, Alan Montgomery
2008 Marketing letters  
Similar datasets emerge in retailing with potential use of RFIDs, online auctions (e.g., eBay), social networking sites (e.g., mySpace), product reviews (e.g., ePinion), customer relationship marketing  ...  Factor analytic approach Consider a retailer with data on sales and pricing for each of 30 brands across 300 customers for 36 months who wants to understand the (cross) effects of price on brand sales,  ...  With a two-factor solution, the model would require 1,800 estimates for the covariance of the price coefficients, still large but more manageable than a full-covariance model.  ... 
doi:10.1007/s11002-008-9036-3 fatcat:tna2cfftxrf7tihtc5f3y6oepu

Modeling and Detection of Future Cyber-Enabled DSM Data Attacks

Kostas Hatalis, Chengbo Zhao, Parv Venkitasubramaniam, Larry Snyder, Shalinee Kishore, Rick S. Blum
2020 Energies  
We also find that nonparametric detection outperforms parametric for smaller user pools, and random point attacks are the hardest to detect with any method.  ...  The first studies a supervised learning approach, with various classification models, and the second studies the performance of parametric and nonparametric change point detectors.  ...  customers sensitivity to price changes.  ... 
doi:10.3390/en13174331 fatcat:gpdxkj3eorfidaprvf76vadxnm

Data Analytics in Operations Management: A Review [article]

Velibor V. Mišić, Georgia Perakis
2019 arXiv   pre-print
Spurred by the growing availability of data and recent advances in machine learning and optimization methodologies, there has been an increasing application of data analytics to problems in operations  ...  In this paper, we review recent applications of data analytics to operations management, in three major areas -- supply chain management, revenue management and healthcare operations -- and highlight some  ...  covariates and the price, and the firm has access to historical transaction records, where each record contains the covariates of the customer, the price at which the product was offered, and a binary  ... 
arXiv:1905.00556v1 fatcat:fdjr43du7nacjdxfhw2cyzaovi

Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets [article]

Martin Huber, Jonas Meier, Hannes Wallimann
2021 arXiv   pre-print
Considering a survey-based sample of buyers of supersaver tickets, we investigate which customer- or trip-related characteristics (including the discount rate) predict buying behavior, namely: booking  ...  increases weakly monotonically in the discount rate, we identify the discount rate's effect among 'always buyers', who would have traveled even without a discount, based on our survey that asks about customer  ...  More concisely, our study provides two use cases of machine learning for business analytics in the railway industry: (i) Predicting buying behavior among supersaver customers, namely whether customers  ... 
arXiv:2105.01426v3 fatcat:pzjc5axltjg2te2l7nvuirwaga


Galyna Chornous, Ganna Ursulenko
2013 Ekonomika  
methods Statistical models (integrated Laplace approximation) Market risk Data on financial instrument rate of return with covariance matrix VaR estimation in analytic form Parametric and nonparametric  ...  These tasks involve customer analytics implementation (client database segmentation, probability of loan repayment calculation, customer life cycle definition and profitability, etc.), transfer prices  ... 
doi:10.15388/ekon.2013.0.1131 fatcat:x2bfmvu7j5gutcitsfc5t36f6a

A nonparametric Bayesian analysis of heterogeneous treatment effects in digital experimentation [article]

Matt Taddy, Matt Gardner, Liyun Chen, David Draper
2015 arXiv   pre-print
This article presents a fast and scalable Bayesian nonparametric analysis of such heterogeneous treatment effects and their measurement in relation to observable covariates.  ...  For linear projections, our inference strategy leads to results that are mostly in agreement with those from the frequentist literature.  ...  , a long tail, and variance that is correlated with available covariates.  ... 
arXiv:1412.8563v4 fatcat:f63jqxjhr5h7znxka6iww77ck4

Modeling nonlinearities with mixtures-of-experts of time series models

Alexandre X. Carvalho, Martin A. Tanner
2006 International Journal of Mathematics and Mathematical Sciences  
well as external covariates.  ...  We discuss a class of nonlinear models based on mixtures-of-experts of regressions of exponential family time series models, where the covariates include functions of lags of the dependent variable as  ...  Basically, we expect that the competitor will attract the more price sensitive customers, so the remaining tuna buyers will be less-price sensitive.  ... 
doi:10.1155/ijmms/2006/19423 fatcat:oila7polhff2tp3inz7jp7kdoe

Differential Privacy in Personalized Pricing with Nonparametric Demand Models [article]

Xi Chen, Sentao Miao, Yining Wang
2021 arXiv   pre-print
To address the privacy issue, this paper studies a dynamic personalized pricing problem with unknown nonparametric demand models under data privacy protection.  ...  any algorithm with LDP guarantee.  ...  Algorithm 1 utilizes two main ideas to carry out nonparametric personalized pricing with demand learning.  ... 
arXiv:2109.04615v1 fatcat:vrywlsf565hixjrtehvyyeyh3m

Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection [article]

Wenhao Li, Ningyuan Chen, L. Jeff Hong
2020 arXiv   pre-print
We consider a contextual online learning (multi-armed bandit) problem with high-dimensional covariate 𝐱 and decision 𝐲.  ...  To take advantage of the sparsity structure of the covariate, we propose a variable selection algorithm called BV-LASSO, which incorporates novel ideas such as binning and voting to apply LASSO to nonparametric  ...  Algorithmic chaining and the role of partial feedback in online nonparametric learning. Working paper, 2017. N. Chen and G. Gallego. Nonparametric pricing analytics with customer covariates.  ... 
arXiv:2009.08265v1 fatcat:fmzgpyc74fdlnmkws6jmjjd2ym

Machine learning methods in finance

Irina Karachun, Lyubov Vinnichek, Andrey Tuskov, S. Roshchupkin
2021 SHS Web of Conferences  
These areas overlap most with econometrics, predictive modelling, and optimal control in finance.  ...  The new data representation allows for a new form of financial econometrics with a focus on topological network structures, rather than just the covariance of historical price time series.  ...  high explanatory power Prediction, often with limited explanatory power Table 2 . 2 Comparison of parametric and nonparametric models Specification Parametric models Nonparametric models  ... 
doi:10.1051/shsconf/202111005012 fatcat:fll3dsvghnfddab2mtkj7nuz4q
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