Effects of revenue management on perceived value, customer satisfaction, and customer loyalty

Matsuoka, Kohsuke

doi:10.1016/j.jbusres.2022.04.052

Cited by 37 publications

(30 citation statements)

References 91 publications

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“…The RMSE (Root Mean Square Error) is a method for determining the accuracy of a predictive target value model [32]. It is the estimate of the average difference between the actual value and the estimated value, which is calculated as shown in (1). Where n is the number of estimates, yj represents the current estimate, and ŷj represents the average value of the estimates.…”

Section: Discussionmentioning

confidence: 99%

“…They are more loyal to brands that make them feel pampered and privileged. However, the loyalty of customers is critical to the prosperity and success of a business [1]. Thus, companies are increasingly trying to anticipate customer needs and generate personalized product or service offers, which makes it possible to initiate, convert and build a relationship with customers.…”

Section: Introductionmentioning

confidence: 99%

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A New Marketing Recommendation System Using a Hybrid Approach to Generate Smart Offers

Mensouri

Azmani

2022

Applied Computer Systems

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In order to increase sales, companies try their best to develop relevant offers that anticipate customer needs. One way to achieve this is by leveraging artificial intelligence algorithms that process data collected based on customer transactions, extract insights and patterns from them, and then present them in a user-friendly way to human or artificial intelligence decision makers. This study is based on a hybrid approach, it starts with an online marketplace dataset that contains many customers’ purchases and ends up with global personalized offers based on three different datasets. The first one, generated by a recommendation system, identifies for each customer a list of products they are most likely to buy. The second is generated with an Apriori algorithm. Apriori is used as an associate rule mining technique to identify and map frequent patterns based on support, confidence, and lift factors, and also to pull important rules between products. The third and last one describes, for each customer, their purchase probability in the next few weeks, based on the BG/NBD model and the average of transactions using the Gamma-Gamma model, as well as the satisfaction based on the CLV and RFMTS models. By combining all three datasets, specific and targeted promotion strategies can be developed. Thus, the company is able to anticipate customer needs and generate the most appropriate offers for them while respecting their budget, with minimum operational costs and a high probability of purchase transformation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Marketing Recommendation System Using a Hybrid Approach to Generate Smart Offers

Mensouri

Azmani

2022

Applied Computer Systems

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“…The negative comments will affect existing customer loyalty and reduce the likelihood of repeat customers. In addition, within the framework of decreasing customer satisfaction, business sales and profitability will also decrease (Matsuoka, 2022).…”

Section: Declining Customer Satisfactionmentioning

confidence: 99%

Quiet Quitting: Causes, Consequences And Suggestions

Yıldız¹

2023

smryj

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The 21st century is pushing businesses like never before. After the Covid-19 pandemic, a new danger has emerged for businesses that have started to recover and return to regular working hours. This study aimed to define quiet quitting, determine the potential risks for businesses, and reveal the precautions to be taken against it. Although quiet quitting is a new concept in the literature, holistic studies on the potential risks for businesses and the precautions to be taken against quiet quitting have yet to be found. Therefore, to contribute to the literature, the concept of quiet quitting has been qualitatively examined and defined. In addition, the factors that reveal the concept of quiet quitting are stated. Subsequently, the potential risks that may be encountered due to quiet quitting businesses are mentioned. Finally, some suggestions have been presented to the business to prevent quiet quitting. The study is anticipated to contribute to the literature for future research.

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“…Revenue Management is widely understood to mean the management of pricing and external factors to maximize the profitability of an asset. Matsuoka (2022) states that Revenue Management aims to maximize financial performance by setting different prices for the same offerings. Cha et al (2017) understood pricing to be a complex task that requires in-depth understanding.…”

Section: Literature Reviewmentioning

confidence: 99%

Factors affecting short-term rental first price: A revenue management model

2022

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The aim of this paper is to conduct a revenue management study, generating a theoretical model that establishes the relationship between the factors of a Short-Term Rental apartment offered on the Airbnb marketplace or similar and its optimal rental price set when the property is first put on the market, considering not only the characteristics defined in the platform listing but also the sociodemographic characteristics of the area in which the apartment is located. The research process was structured in six phases as case study for the technology transfer model. First, research planning was conducted to estimate the time, cost, and suitability of the research topic. Second, the study design was determined to establish a technology transfer model focusing on the theory of mixed revenue management. Third, data collection about the city of Madrid was extracted from two technological databases, namely SeeTransparent based mainly on Airbnb (28 internal characteristics of the apartment) and Deskmind Research (9 sociodemographic variables of the area in which the apartment is located). Fourth, the data were prepared to create a new descriptive variable of the apartments based on geolocation. Fifth, the analysis of this study was applied to explore the correlation between the price charged per night, the 28 internal characteristics of the apartments, and the 9 sociodemographic variables of their surrounding areas. Sixth, with this integrated database, the information was transformed into multivariate inferential statistics through Exploratory Factor Analysis and Multiple Linear Regression, creating a technology transfer model (big data algorithm) that allows revenue managers to set the price of an apartment based on known information, prior to having a history of market reactions. This research process and model consider some of the factors affecting the psychological behavior of tourism consumers. Practical implications of the findings indicate that the size/capacity of the apartments used for Short-Term rentals largely determines the initial rental price set (72%). The equipment offered by the apartments has a moderate impact (18%), and the sociodemographic characteristics of the surrounding area have a minor influence (11%).

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Effects of revenue management on perceived value, customer satisfaction, and customer loyalty

Cited by 37 publications

References 91 publications

A New Marketing Recommendation System Using a Hybrid Approach to Generate Smart Offers

A New Marketing Recommendation System Using a Hybrid Approach to Generate Smart Offers

Quiet Quitting: Causes, Consequences And Suggestions

Factors affecting short-term rental first price: A revenue management model

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