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Nonparametric Pricing Analytics with Customer Covariates
[article]
2020
arXiv
pre-print
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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
2011
Marketing science (Providence, R.I.)
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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]
2012
arXiv
pre-print
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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
2006
Journal of Marketing Research
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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
2008
Journal of Econometrics
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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
2008
Marketing letters
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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
2020
Energies
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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]
2019
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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
RISK MANAGEMENT IN BANKS: NEW APPROACHES TO RISK ASSESSMENT AND INFORMATION SUPPORT
2013
Ekonomika
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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]
2015
arXiv
pre-print
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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
2006
International Journal of Mathematics and Mathematical Sciences
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2021
SHS Web of Conferences
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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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