Dynamic pricing: Definition, implications for managers, and future research directions

Kopalle, Praveen K.; Pauwels, Koen; Akella, Laxminarayana Yashaswy; Gangwar, Manish

doi:10.1016/j.jretai.2023.11.003

Cited by 17 publications

(1 citation statement)

References 74 publications

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“…With the increasing complexity of demand patterns, the industry is turning to advanced technologies, such as neural networks, to optimize pricing strategies (Puller and Taylor, 2012). Today, companies are moving towards using technology for dynamic pricing to increase their revenues (Kopalle et al, 2023). Dynamic pricing can affect companies' revenues and productivity (Sheng et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Designing an algorithm for predicting plane ticket prices using feedforward neural network modeling

Mojoodi,

Jalalian,

Kumail

2024

JHTI

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PurposeThis research aims to determine the ideal fare for various aircraft itineraries by modeling prices using a neural network method. Dynamic pricing has been studied from the airline’s point of view, with a focus on demand forecasting and price differentiation. Early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.Design/methodology/approachA feedforward neural network was employed in the current study. Two hidden layers, consisting of 18 and 12 neurons, were incorporated to enhance the network’s capabilities. The activation function employed for these layers was tanh. Additionally, it was considered that the output layer’s functions were linear. The neural network inputs considered in this study were flight path, month of flight, flight date (week/day), flight time, aircraft type (Boeing, Airbus, other), and flight class (economy, business). The neural network output, on the other hand, was the ticket price. The dataset comprises 16,585 records, specifically flight data for Iranian airlines for 2022.FindingsThe findings indicate that the model achieved a high level of accuracy in approximating the actual data. Additionally, it demonstrated the ability to predict the optimal ticket price for various flight routes with minimal error.Practical implicationsBased on the significant alignment observed between the actual data and the tested data utilizing the algorithmic model, airlines can proactively anticipate ticket prices across all routes, optimizing the revenue generated by each flight. The neural network algorithm utilized in this study offers a valuable opportunity for companies to enhance their decision-making processes. By leveraging the algorithm’s features, companies can analyze past data effectively and predict future prices. This enables them to make informed and timely decisions based on reliable information.Originality/valueThe present study represents a pioneering research endeavor that investigates using a neural network algorithm to predict the most suitable pricing for various flight routes. This study aims to provide valuable insights into dynamic pricing for marketing researchers and practitioners.

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Section: Introductionmentioning

confidence: 99%

Designing an algorithm for predicting plane ticket prices using feedforward neural network modeling

Mojoodi,

Jalalian,

Kumail

2024

JHTI

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Beyond Convenience: Exploring the Hidden Impact of Algorithmic Pricing on Consumer Trust and Loyalty in Chinese Online Travel Agencies

Wang,

Yu,

Chen

et al. 2024

Journal of Tourism Research

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The emergence of online travel agencies (OTAs) provides travelers with “one‐stop” convenience, but also faces issues of price discrimination. This mixed‐methods study began with semi‐structured interviews of 30 participants to explore initial perceptions, followed by a survey of 307 Chinese consumers. The study examined attitudes toward OTA overreliance on algorithmic pricing and unclear information, and their impact on trust and loyalty. Findings reveal that during peak seasons, regular customers without value‐added membership services paid higher room rates than new customers. The results indicate that once trust is established through clear information quality and reliable service, consumers are less wary of pricing quotes. The conclusion summarizes the findings and suggests further research to promote transparent and fair pricing practices, enhancing consumer trust and loyalty.

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AI and Marketing

Patsiotis,

Kapareliotis,

Tousai

2024

Reference Module in Social Sciences

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Dynamic pricing: Definition, implications for managers, and future research directions

Cited by 17 publications

References 74 publications

Designing an algorithm for predicting plane ticket prices using feedforward neural network modeling

Designing an algorithm for predicting plane ticket prices using feedforward neural network modeling

Beyond Convenience: Exploring the Hidden Impact of Algorithmic Pricing on Consumer Trust and Loyalty in Chinese Online Travel Agencies

AI and Marketing

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