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This study explores the influence of digital marketing strategies on brand loyalty among young airline passengers, specifically targeting Millennials and Gen Z individuals aged 18-35 years old. With the increasing reliance on digital channels such as social media, email marketing, and search engine marketing, it is crucial to understand how these strategies impact the customer-brand relationship, brand trust, and ultimately, brand loyalty. The research employs a quantitative approach, gathering data from 230 participants who are frequent users of digital platforms and have recent travel experiences with airlines. The findings reveal that social media marketing has the most significant impact on the customer-brand relationship, followed by email and search engine marketing. Additionally, the study confirms that brand trust acts as a critical mediator between digital marketing activities and brand loyalty. These results highlight the importance of tailored digital marketing strategies for airlines aiming to engage and retain young passengers in a highly competitive industry.

This study mainly aimed to investigate the impact of seasonal changes on media advertising revenues (AR) in R environment through a visual-print media company example. So, the study primarily aimed to examine whether visual, print and internet ARs were affected by seasonal change. The study also aimed to indicate the trends of visual/ print/ and internet ARs over time in the light of seasonality. Furthermore, it was also aimed to make AR estimates for the next year with the best performing methods covering ARIMA and ETS. Methodologically, R programming language and its libraries were used together with ARIMA and ETS time series models. The data set consisted of a monthly ARs series consisting of 180 rows covering the period between January/2007-December/2021 and having a sufficient number of observations for the application of the models. Data were analyzed with R and next year’s forecasts were done with ARIMA and ETS. The study revealed that all AR types were affected by seasonal changes in the specified period and included seasonality, i.e. specially in the summer months.  Additionally, it was observed that AR types have different trends despite containing seasonality. The study found that visual ARs denoted a slight downward trend including seasonality during the period. It was also indicated that print ARs reflected a very rapid and drastic downward trend covering seasonality. The study also revealed that internet ARs showed a very rapid and sharp upward trend compared to the other two AR types despite having seasonality. Furthermore, taking into account the calculated error metrics, it was found that the ARIMA model showed the best estimation performance in both i.e. visual and internet ARs. On the other hand, the ETS model indicated the best estimation performance in print ARs. Accordingly, estimations of the visual/print and internet ARs for the next year were made with the best performing methods. It was pointed out that estimated ARs included seasonality in the summer months and revealed similar results to the previous trends based on historical data.