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Regular version of the site
Of all publications in the section: 5
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Article
Antipov E. A., Pokryshevskaya E. B. Journal of Revenue and Pricing Management. 2017. Vol. 16. No. 3. P. 295-307.

In this study we develop a model for early box office receipts forecasting that, in addition to traditionally used regressors, uses several inputs that have never been used before, but appeared to be very useful predictors according to our variable importance analysis. New predictors account for the power of actors and directors, as well as for the intensity of competition at the time of movie release. Instead of Motion Picture of Association of America (MPAA) ratings commonly used in movie success prediction, textual information about the reasons for giving a movie its MPAA rating was formalized using word frequency and principal components analyses. The expert system is based on the Random forest algorithm, which outperformed a stepwise regression and a multilayer perceptron neural network. A regression tree-based diagnostic approach allowed us to detect the heterogeneity of model accuracy across segments of data and assess the applicability of the model to different movie types.

Added: Mar 15, 2018
Article
Antipov E. A., Pokryshevskaya E. Journal of Revenue and Pricing Management. 2017. Vol. 16. No. 3. P. 295-307.

In this study we develop a model for early box office receipts forecasting that, in addition to traditionally used regressors, uses several inputs that have never been used before, but appeared to be very useful predictors according to our variable importance analysis. New predictors account for the power of actors and directors, as well as for the intensity of competition at the time of movie release. Instead of Motion Picture of Association of America (MPAA) ratings commonly used in movie success prediction, textual information about the reasons for giving a movie its MPAA rating was formalized using word frequency and principal components analyses. The expert system is based on the Random forest algorithm, which outperformed a stepwise regression and a multilayer perceptron neural network. A regression tree-based diagnostic approach allowed us to detect the heterogeneity of model accuracy across segments of data and assess the applicability of the model to different movie types.

Added: May 26, 2017
Article
Ozhegova A., Ozhegov E. M. Journal of Revenue and Pricing Management. 2018. Vol. 17. No. 3. P. 131-145.

Studying the heterogeneity of consumers allows to price the product differently for consumer segments or groups of a product. In this paper we estimate a model of aggregate demand for Perm Opera and Ballet Theatre focusing on the heterogeneity in price effect on demand for tickets on different performances and seats. We estimate parameters of demand function using censored quantile regression that accounts for the limited capacity of the theatre house. We reveal the price effect variation across different types of theatrical productions and seats with lower elastic demand on ballets and for seats of higher quality.

Added: Jul 18, 2017
Article
Antipov E. A., Pokryshevskaya E. B. Journal of Revenue and Pricing Management. 2020. No. 19. P. 355-364.

Forecasting demand and understanding sales drivers are one of the most important tasks in retail analytics. However, traditionally, linear models and/or models with a small number of predictors have been predominantly used in sales modeling. Taking into account that real-world demand is naturally determined by complex substitution and complementation patterns among a large number of interrelated SKUs, nonlinear effects of prices, promotions, seasonality, as well as many other factors, their lagged values, and interactions, a realistic model has to be able to account for all that. We propose a conceptual model for sales modeling based on standard POS data available to any retailer and generate almost 500 potentially useful predictors of a focal SKU’s sales accordingly. In our comparison of three classes of models, Gradient Boosting Machines outperformed Random Forests and Elastic nets. By using interpretable machine learning methods, we came up with actionable insights related to the importance of various groups of predictors from the conceptual model, as well as demonstrated how helpful it can be for marketing managers to decompose predictions into the effects of individual regressors by using an approximation of Shapley values for feature attribution.

Added: Oct 31, 2020
Article
Lapina M., Fridman G. Journal of Revenue and Pricing Management. 2016. Vol. 15. No. 1. P. 37-51.

The forecast of passenger demand in Revenue Management is usually based on historical booking data that reflects the number of sales rather than true demand which is constrained by booking limits. That is why the process of demand forecasting under such circumstances is called unconstraining. The goal of every unconstraining approach is to get empirical or theoretical estimation of true demand. The application of the maximum likelihood method to unconstraining problems in Revenue Management is advocated in the paper based on the construction of the distribution function for the censored demand depending on availability of the censoring information. Numerical results are presented of comparative analysis of existing unconstraining methods and the method used in the paper. It is demonstrated that maximum likelihood method proves to be more efficient in case of high percentage of censoring. Another important advantage of the method connected to the fact that it enables one to process the situation of censoring information incompleteness when some elements of the observed sample data are known to be censored or not and for the others this information is not available. Mathematical computer environment Wolfram Mathematica has been used for obtaining all the numerical results presented in the paper.

Added: Oct 24, 2013