Investigating the Role of Customer Churn in the Optimal Allocation of Offensive and Defensive Advertising: the Case of the Competitive Growing Market

Machowska, Dominika

doi:10.18559/ebr.2018.2.1

Cited by 5 publications

(3 citation statements)

References 47 publications

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“…Due to the strong seasonality that is typical of entertainment services, all models contain monthly, weekly and intra-daily seasonal dummy variables. In addition, to account for the average performance from the first (opening) week we included unobserved movie reputation, consumer loyalty, prelaunch marketing instruments and movie features not captured by other variables (e.g., Machowska, 2018). A number of papers suggest that a movie's opening performance is a good predictor of the financial success of the movie (e.g., Gmerek, 2005;Sharda and Delen, 2006).…”

Section: Methodsmentioning

confidence: 99%

Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland

Baranowski

Korczak

Zając

2020

Business Systems Research Journal

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Background: Cinema programmes are set in advance (usually with a weekly frequency), which motivates us to investigate the short-term forecasting of attendance. In the literature on the cinema industry, the issue of attendance forecasting has gained less research attention compared to modelling the aggregate performance of movies. Furthermore, unlike most existing studies, we use data on attendance at the individual show level (179,103 shows) rather than aggregate box office sales.Objectives: In the paper, we evaluate short-term forecasting models of cinema attendance. The main purpose of the study is to find the factors that are useful in forecasting cinema attendance at the individual show level (i.e., the number of tickets sold for a particular movie, time and cinema).Methods/Approach: We apply several linear regression models, estimated for each recursive sample, to produce one-week ahead forecasts of the attendance. We then rank the models based on the out-of-sample fit.Results: The results show that the best performing models are those that include cinema- and region-specific variables, in addition to movie parameters (e.g., genre, age classification) or title popularity.Conclusions: Regression models using a wide set of variables (cinema- and region-specific variables, movie features, title popularity) may be successfully applied for predicting individual cinema shows attendance in Poland.

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Section: Methodsmentioning

confidence: 99%

Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland

Baranowski

Korczak

Zając

2020

Business Systems Research Journal

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“…Using Hamilton-Jacobi-Bellman equations in an infinite time horizon, the model was solved in a closed-loop way, and a closed-form solution was provided. In another study, Machowska [12] added the potential market share rate to the dynamic advertising model's state equation. In this model, market share is equal to the total market share minus the competitor's market share plus the rate of potential market share.…”

Section: Literature Reviewmentioning

confidence: 99%

Offensive, Defensive, and Generic Advertising Strategies in a Dynamic Oligopolistic Market

Jafari

Omran

Jahani

2021

Mathematical Problems in Engineering

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In today’s highly competitive business environment, advertisement plays an influential role in attracting customers and increasing market share. Companies adopt different advertising strategies in a competitive market, such as offensive, defensive, and generic, to keep and increase their market share. Researchers have generally modeled this problem using a dynamic differential game. All previous research studies have focused on finding these strategies in a duopoly market. Also, to simultaneously determine the optimal equilibrium strategy for these three strategies, the model is designed as a symmetric game due to the ease of solving. In contrast with the previous researches, the purpose of this paper is to present and solve an asymmetric game model to determine the optimal offensive, defensive, and generic advertising strategies in an oligopoly market. The proposed model’s objective is to obtain the maximum equilibrium profit for each company at any moment regarding the market share of each company and those of competitors. A numerical solution method based on the Pontryagin’s maximum principle is developed to solve the model. Then, the proposed model is solved for a triopoly market. Also, the sensitivity of the results to changes in model parameters has been investigated. The obtained results denote that in markets with more than two players under the asymmetric game, the proposed model can prescribe the optimal type of offensive, defensive, and generic advertising strategies.

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“…Longer the bank holds its significant customer, more prominent benefits and income the bank can make. The forecasting of customer attrition rate is a critical factor Banking Sector to hold its productive clients and to sustain in the aggressive market [2]. According to the Banking statistics, the customer attrition rate in the Banking sector is raised from 20% to 30% which is a very big concern for the Banking organization.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Deep Feed Forward Neural Network Model for the Customer Attrition Analysis in Banking Sector

Hegde¹,

Mundada²

2019

IJISA

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In the present era with the development of the innovation and the globalization, attrition of customer is considered as the vital metric which decides the incomes and gainfulness of the association. It is relevant for all the business spaces regardless of the measure of the business notwithstanding including the new companies. As per the business organization, about 65% of income comes from the customer's client. The objective of the customer attrition analysis is to anticipate the client who is probably going to exit from the present business association. The attrition analysis also termed as churn analysis. The point of this paper is to assemble a precise prescient model using the Enhanced Deep Feed Forward Neural Network Model to predict the customer whittling down in the Banking Domain. The result obtained through the proposed model is compared with various classes of machine learning algorithms Logistic regression, Decision tree, Gaussian Naï ve Bayes Algorithm, and Artificial Neural Network. The outcome demonstrates that the proposed Enhanced Deep Feed Forward Neural Network Model performs best in accuracy compared with the existing machine learning model in predicting the customer attrition rate with the Banking Sector.

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Investigating the Role of Customer Churn in the Optimal Allocation of Offensive and Defensive Advertising: the Case of the Competitive Growing Market

Cited by 5 publications

References 47 publications

Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland

Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland

Offensive, Defensive, and Generic Advertising Strategies in a Dynamic Oligopolistic Market

Enhanced Deep Feed Forward Neural Network Model for the Customer Attrition Analysis in Banking Sector

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