Psychographic And Behavioral Segmentation Of Food Delivery Application Customers To Increase Intention To Use

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis study presents a framework for segmenting Food Delivery Application (FDA) customers based on psychographic and behavioral variables as an alternative to existing segmentation. Customer segments are proposed by applying clustering methods to primary data from an electronic survey. Psychographic and behavioral constructs are formulated as hypotheses based on existing literature, and then evaluated as segmentation variables regarding their discriminatory power for customer segmentation. Detected relevant variables are used in the application of clustering techniques to find adequate boundaries within customer groupings for segmentation purposes. Characterization of customer segments is performed and enriched with implications of findings in FDA marketing strategies. This paper contributes to theory by providing new findings on segmentation that are relevant for an online context. In addition, it contributes to practice by detailing implications of customer segments in an online sales strategy, allowing marketing managers and FDA businesses to capitalize knowledge in their conversion funnel designs

Digital Solutions and Machine Learning Can Improve Niche Market Reach

Digital solutions in marketing can help reach niche markets. Marketers have the greatest opportunity ever to address segments whose needs have not yet been met. Online segmentation techniques allow to better know their characteristics. The aim of this article is to investigate the segmentation and targeting possibilities of the Google Ads system, which helps to explore consumer patterns more deeply. Digital marketing solutions help marketers reach niche markets to maximise the effectiveness of their activities. The goal of this social constructivist research was to find an answer to the question of whether the segmentation and targeting options of the Google Ads advertising system can sufficiently ensure this. To this end, we examined the presence of the “target market category” label in 37 individuals using a face-to-face survey method. The occurrence of the labels and the actual interests often overlapped

Patterns and Pathways: Applying Social Network Analysis to Understand User Behavior in the Tourism Industry Websites

The contemporary tourism landscape is undergoing rapid digitization, necessitating a nuanced comprehension of online user behavior to guide data-driven decision-making. This research bridges an existing gap by investigating the tourism website ecosystem through social network analysis. It focuses specifically on inter-website communication patterns based on user navigation. Data mining facilitates the identification of 162 core Iranian tourism websites, which are visualized as an interconnected network with websites as nodes and user transitions as weighted directed edges. By implementing community detection, eight key clusters are discerned, encompassing domains like ticket/tour bookings, accommodations, location services, and cuisine. Further analysis of inter-community relationships reveals website groupings frequently accessed together by users, highlighting complementary services sought during travel planning. The research derives invaluable insights into user preferences and information propagation within the tourism ecosystem. The methodology and findings contribute original perspectives to academia while offering pragmatic strategic recommendations to industry stakeholders like service providers, investors, and policymakers. This pioneering exploration of latent user behavior patterns advances comprehension of the evolving digital tourism landscape in Iran. It contributes pathways toward a sustainable future vision of the ecosystem, guiding stakeholders in targeted decision-making based on empirical evidence derived from social network analysis of websites and consumption patterns. The innovative methodology expands the toolkit for data-driven tourism research within academia