On a Certain Research Gap in Big Data Mining for Customer Insights

Mach‐Król, Maria; Hadasik, Bartłomiej

doi:10.3390/app11156993

Cited by 17 publications

(6 citation statements)

References 187 publications

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“…The advanced analysis of information provided by customers is a way to acquire, maintain, engage and satisfy their needs in an optimal way. It also allows creating transparency, segmentation of the population, replacement or support in the decision-making of individuals [ 79 ]. This is the importance of carrying out data analysis, since through comprehensive evaluation indicators, the analysis can be used to create an offer of personalized intelligent services for both tourists and tourism providers [ 80 ].…”

Section: Methodsmentioning

confidence: 99%

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Topics and destinations in comments on YouTube tourism videos during the Covid-19 pandemic

Carvache‐Franco

et al. 2023

PLoS ONE

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This study examines the comments posted on tourism-related YouTube videos during the Covid-19 pandemic to establish sustainable development strategies in destinations. Its objectives were: (i) to identify the topics of discussion, (ii) to establish the perceptions of tourism in a pandemic crisis, and (iii) to identify the destinations mentioned. The data was collected between January and May 2020. 39,225 comments were extracted in different languages and globally through the YouTube API. The data processing was carried out using the word association technique. The results show that the most discussed topics were: “people,” “country,” “tourist,” “place,” “tourism,” “see,” “visit,” “travel,” “covid-19,” “life,” and “live,” which are the focus of the comments made on the perceptions found and represent the attraction factors shown by the videos and the emotions perceived in the comments. The findings show that users’ perceptions are related to risks since the “Covid-19” pandemic is associated with the impact on tourism, people, destinations, and affected countries. The destinations in the comments were: India, Nepal, China, Kerala, France, Thailand, and Europe. The research has theoretical implications concerning tourists’ perceptions of destinations since new perceptions associated with destinations during the pandemic are shown. Such concerns involve tourist safety and work at the destinations. This research has practical implications since, during the pandemic, companies can develop prevention plans. Also, governments could implement sustainable development plans that contain measures so that tourists can make their trips during a pandemic.

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Section: Methodsmentioning

confidence: 99%

“…Carrying out an SMA includes sentiment analysis and is useful to access and understand the behavior of users, or the group of users more broadly defined as society, especially in uncertain conditions such as the COVID-19 pandemic. An SMA generates new leads through customer experience and operational efficiency [ 79 ].…”

Section: Methodsmentioning

confidence: 99%

Topics and destinations in comments on YouTube tourism videos during the Covid-19 pandemic

Carvache‐Franco

et al. 2023

PLoS ONE

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“…Notably, it excels in generating regression trees, which predict real numbers rather than classes, minimizing squared prediction errors. Some recent research, such as that by Mach-Krol and Hadasik (2021) [14] and Hassouna et al, (2016) [15], sought to compare data mining methods, specifically decision trees and logistic regression, in constructing a customer churn model. The study involved a real-world dataset from an English mobile operator, employing two balanced datasets, one for training, with 17 variables and 19,919 customers, and another for testing, with 17 variables and 15,519 customers.…”

Section: Literature Reviewmentioning

confidence: 99%

Unveiling IoT Customer Behaviour: Segmentation and Insights for Enhanced IoT-CRM Strategies: A Real Case Study

Eslami,

Razi,

Lonbani

et al. 2024

Sensors

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In today’s competitive landscape, achieving customer-centricity is paramount for the sustainable growth and success of organisations. This research is dedicated to understanding customer preferences in the context of the Internet of things (IoT) and employs a two-part modeling approach tailored to this digital era. In the first phase, we leverage the power of the self-organizing map (SOM) algorithm to segment IoT customers based on their connected device usage patterns. This segmentation approach reveals three distinct customer clusters, with the second cluster demonstrating the highest propensity for IoT device adoption and usage. In the second phase, we introduce a robust decision tree methodology designed to prioritize various factors influencing customer satisfaction in the IoT ecosystem. We employ the classification and regression tree (CART) technique to analyze 17 key questions that assess the significance of factors impacting IoT device purchase decisions. By aligning these factors with the identified IoT customer clusters, we gain profound insights into customer behaviour and preferences in the rapidly evolving world of connected devices. This comprehensive analysis delves into the factors contributing to customer retention in the IoT space, with a strong emphasis on crafting logical marketing strategies, enhancing customer satisfaction, and fostering customer loyalty in the digital realm. Our research methodology involves surveys and questionnaires distributed to 207 IoT users, categorizing them into three distinct IoT customer groups. Leveraging analytical statistical methods, regression analysis, and IoT-specific tools and software, this study rigorously evaluates the factors influencing IoT device purchases. Importantly, this approach not only effectively clusters the IoT customer relationship management (IoT-CRM) dataset but also provides valuable visualisations that are essential for understanding the complex dynamics of the IoT customer landscape. Our findings underscore the critical role of logical marketing strategies, customer satisfaction, and customer loyalty in enhancing customer retention in the IoT era. This research offers a significant contribution to businesses seeking to optimize their IoT-CRM strategies and capitalize on the opportunities presented by the IoT ecosystem.

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“…Mach-Król and Hadasik, in their study (2021), use two research methods-systematic search in bibliographic repositories and bibliographic verification of the obtained resultsto discuss a research gap in Big Data mining for customer insight. Their goal is to provide a theoretical ground to the discussion by organizing knowledge on the role of Big Data mining in understanding customers' behavior [20].…”

Section: Big Datamentioning

confidence: 99%

Big Data—Knowledge Discovery in Production Industry Data Storages—Implementation of Best Practices

2021

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CRISP-DM (cross-industry standard process for data mining) methodology was developed as an intuitive tool for data scientists, to help them with applying Big Data methods in the complex technological environment of Industry 4.0. The review of numerous recent papers and studies uncovered that most of papers focus either on the application of existing methods in case studies, summarizing existing knowledge, or developing new methods for a certain kind of problem. Although all of these types of research are productive and required, we identified a lack of complex best practices for a specific field. Therefore, our goal is to propose best practices for the data analysis in production industry. The foundation of our proposal is based on three main points: the CRISP-DM methodology as the theoretical framework, the literature overview as an expression of current needs and interests in the field of data analysis, and case studies of projects we were directly involved in as a source of real-world experience. The results are presented as lists of the most common problems for selected phases (‘Data Preparation’ and ‘Modelling’), proposal of possible solutions, and diagrams for these phases. These recommendations can help other data scientists avoid certain problems or choose the best way to approach them.

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On a Certain Research Gap in Big Data Mining for Customer Insights

Cited by 17 publications

References 187 publications

Topics and destinations in comments on YouTube tourism videos during the Covid-19 pandemic

Topics and destinations in comments on YouTube tourism videos during the Covid-19 pandemic

Unveiling IoT Customer Behaviour: Segmentation and Insights for Enhanced IoT-CRM Strategies: A Real Case Study

Big Data—Knowledge Discovery in Production Industry Data Storages—Implementation of Best Practices

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