Adaptive Pricing E-Commerce Strategy Improvement Presentation

Customers Personalization

• Customers’ personalization platform based on online activity.

“Browsing data, booking data, flying data” (Lu et al. 446).

• Big data platform for storage and analyzing.
• Identification algorithm to determine the customers’ interests.

The personalizing system optimizes the search (Adajiand Vassileva, 107).

• An optimized search contributes to positive experience.
• Positive customers’ experience means customers’ loyalty.

New Distribution Capability (NDS) Standardization

• The personalization requires transparent information transmission.
• The NDS standard optimizes data transfer.

“Communications between airlines and travel agents” (“The NDC program”).

• The NDS standardization integrates into airline conglomerate.
• It allows tracking of competitors’ offers.
• Tracking allows effectively adapting its own prices.
• Favorable adaptive prices and offers attract customers.

Adaptive or Dynamic Pricing

• Real-time data contributes to a dynamic pricing.

Dynamic pricing is crucial for airline e-commerce (Fiig et al. 381).

• It balances customer capabilities and airline needs.
• Dynamic pricing enables sectorial offers, prices.
• Sectorial pricing divided by different customers’ incomes.
• It provides affordable services to social strata.
• It means the potential customers’ greater reach.

Artificial Intelligence (AI) and Neural Networks Integration

• Dynamic pricing data requires fast analysis.
• AI and neural networks can provide it.

Neural networks positively affect customer experience (Prasad et al. 1).

• They also track minor market patterns.
• It allows predicting market and pricing changes.
• It contributes to greater financial benefits.

Continuous Localization

• Standardization and personalization mean a growing audience.
• Etihad Airlines translated into most languages.
• However, some language adaptations contain errors.

The presence of e-commerce localization errors (Wu 2).

• Neural networks analyze and correct the text.
• Proper translation makes the website more attractive.

Customer’ Information from Social Networks

• AI and neural networks analyze customers’ profiles.
• It provides information on customer preferences.
• It means a successful prediction of offers.

Neural networks successfully predict social phenomena (Liu et al. 1).

• It means a more friendly range of services.
• Customers often prefer a friendly interface.

Works Cited

Adaji, Ifeoma, and Julita Vassileva. “Evaluating Personalization and Persuasion in E-Commerce.” PPT@PERSUASIVE, 2016, pp. 107-113.

Fiig, Thomas, Remy Le Guen, and Mathilde Gauchet. “Dynamic pricing of airline offers.” Journal of Revenue and Pricing Management, vol. 17, no. 6, 2018, pp. 381-393.

Liu, Qun, et al. “Social Relationship Prediction across Networks Using Tri-training BP Neural Networks.” Neurocomputing, 2020.

Lu, Chun, Wen-Hui Qiu, and Xue-Long Cheng. “Research on Aviation Big Data and E-commerce Applications.” International Conference on Electronic, Control, Automation and Mechanical Engineering (ECAME 2017), 2017, pp. 446-450.

“New Distribution Capability.” 2020. Web.

Prasad Majumder, Bodhisattwa, et al. “Deep Recurrent Neural Networks for Product Attribute Extraction in eCommerce.” Cornell University. 2019. Web.

Wu, Frances Man Hin. Airline e-commerce globalization competitiveness: localization error analysis of 23 American and European airline websites. Dissertation, University of Limerick, 2017.