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Using Google Trends to analyze interest in travel insurance

Szewczyk Mirosława, Zygmuntowicz Justyna

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

2024

z. 203

541--559

Purpose: The aim of the article is to use Google Trends data to examine the seasonal pattern in interest in travel insurance. Design/methodology/approach: This article examines the use of Google Trends to study the frequency of queries related to „ubezpieczenie turystyczne” [travel insurance] and „EKUZ” [European Health Insurance Card] in Poland. We applied the Holt-Winters method with multiplicative seasonality for our analysis. The statistical analysis was conducted using Statistica software. Findings: In the context of studying queries related to „ubezpieczenie turystyczne” and „EKUZ”, Google Trends offers insights into seasonal variations and emerging trends. Research limitations/implications: This research has several limitations, notably the reliance on search queries to measure interest levels, which may not accurately represent actual tourist behavior. Furthermore, the relationships and patterns identified were tested solely within a single country, thereby constraining the generalizability of the findings to other regions and cultural contexts. Practical implications: The data indicate seasonality, reflecting the public's heightened interest during peak travel periods. This seasonal pattern is crucial for policymakers and businesses in the travel and insurance sectors, enabling them to tailor their services and marketing strategies accordingly. Social implications: The extensive volume of searches conducted via Google generates trend data, which can be analyzed using Google Trends, a publicly accessible tool that compares the volume of internet search queries across different regions and time periods. Consequently, Google Trends can offer indirect estimates and has the potential to detect specific patterns earlier than traditional systems. Originality/value: This study contributes original insights by utilizing Google Trends data to analyze and understand seasonal patterns and interest level for travel insurance.