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2020 | 19 | nr 2 | 7--35
Tytuł artykułu

The Use of Big Data in Tourism Sales Forecasting

Warianty tytułu
Analizy big data jako narzędzie prognozowania sprzedaży pakietów turystycznych
Języki publikacji
EN
Abstrakty
Wprowadzenie. Eksplozja nieustrukturyzowanych wielkich zbiorów danych (big data - BD), automatyzacji i uczenia maszynowego pozwala współczesnym przedsiębiorcom lepiej przewidywać zachowania poszczególnych podmiotów. W badaniach naukowych duże zbiory danych są szeroko wykorzystywane do badania zachowań i opinii konsumentów. Jednym z narzędzi umożliwiających prognozowanie wielkości sprzedaży jest model dyfuzji Bassa, którego uniwersalny charakter udowodniono w licznych zastosowaniach w prognozowaniu sprzedaży produktów w różnych segmentach rynku. W niniejszym artykule zaproponowano zastosowanie big data jako zmiennych egzogenicznych w modelu Bassa w celu zwiększenia dokładności prognozowania wolumenu sprzedaży pakietów turystycznych.
Cel badań. Celem badań jest ocena wpływu wykorzystania wielkich zbiorów danych na poprawę dokładności prognoz sprzedaży pakietów turystycznych. Uogólniony model Bassa został w tym celu rozszerzony o zbiory big data. Zmienne egzogeniczne obejmują: (1) treści generowane przez marketerów (en. marketer-generated content, MGC) i (2) treści generowane przez użytkowników (en. user-generated content, UGC), w tym wyszukiwania w sieci i posty zamieszczane na blogach i mikroblogach.
Metodologia. W artykule analizowane są dane obejmujące wiadomości online, posty na blogach i wolumen wyszukiwań w Internecie (ruch sieciowy) związane z informacjami dotyczącymi pakietów turystycznych polskich touroperatorów. Informacje te zostały zintegrowane z modelem Bassa jako część zmiennych egzogenicznych reprezentujących działania marketingowe touroperatorów. Założono, że wolumen informacji zamieszczanych online przez touroperatorów stanowi odzwierciedlenie treści generowanych przez marketerów (MGC), podczas gdy posty na blogach i ruch związany z wyszukiwaniem w sieci stanowią treści generowane przez użytkowników (UGC).
Wyniki. Analiza empiryczna wykazała, że włączenie dużych zbiorów danych do modelu Bassa zapewnia dokładniejsze prognozowanie wielkości sprzedaży pakietów turystycznych. Ponadto UGC (jako zmienna egzogeniczna) lepiej służy prognozowaniu wielkości sprzedaży niż MGC. UGC jest dość dobrym narzędziem wyjaśniającym poziom zainteresowania i zaangażowania potencjalnych turystów. Wykazano jednak, że skuteczność prognozowania jest różna w przypadku wpisów na blogach i liczby wyszukiwań w sieci. (abstrakt oryginalny)
EN
Background. The explosion of big data (BD), automation, and machine learning have allowed contemporary businesses to better understand and predict human behavior. In scientific research big data have been widely used to study consumer journey and opinions. One of the tools enabling forecasting of sales volume is the Bass diffusion model, which universal nature has been proven in many applications in forecasting the sale of products belonging to various market segments. This article considers the use of BD as exogenous variables in the Bass model to predict the sales of tourist packages.
Research aims. The purpose of the research is to assess the impact of using big data on improving the accuracy of forecasts for the sale of tourist packages. The Generalized Bass Model (GBM) has been thus expanded to include big data, which means that exogenous variables include: (1) marketer-generated content (MGC) and (2) user-generated content (UGC), including volume of web search and blog posts.
Methodology. This article analyzes online news, blog posts and web search traffic volume related to tourist packages, and then integrates the information into the Bass model, treating it as part of the exogenous variables representing the marketing efforts of tour operators. It has been assumed that the volume of tour operators' web news is a proxy for content generated by marketers (MGC), while the volume of blog posts and web search traffic constitute user-generated content (UGC).
Key findings. The empirical analysis found that by incorporating big data into the Bass model provides more accurate prediction of tourist packages' sales volume. In addition, UGC (as an exogenous variable) is better at predicting sales volume than MGC. UGC is a fairly good tool explaining the level of interest and involvement of potential tourists. However, it has been shown that forecasting efficiency is different for blog posts and web search traffic volumes. (original abstract)
Rocznik
Tom
19
Numer
Strony
7--35
Opis fizyczny
Twórcy
  • Warsaw School of Economics (SGH), Warsaw, Poland
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Typ dokumentu
Bibliografia
Identyfikator YADDA
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