Brand Positioning and Segmentation of Sneakers through Multi-Dimensional Customer Experience Analysis

2021 ◽  
Vol 13 (2) ◽  
pp. 335-345
Author(s):  
R. Senthilkumar ◽  
B. RubanRaja ◽  
Monisha
Keyword(s):  
Text Analysis ◽  
Customer Experience ◽  
Structured Data ◽  
Multivariate Techniques ◽  
Brand Positioning ◽  
Customer Reviews ◽  
Perceptual Mapping ◽  
Analysis Techniques ◽  
Hidden Patterns ◽  
And Cluster Analysis

A huge corpus of valuable information on customer experience is available as unstructured form in customer reviews on e-commerce websites. Multivariate data analysis techniques are effective in uncovering hidden patterns and segments in structured data. A major challenge is to convert the unstructured data into a structured form for applying multivariate techniques. In this article, we have provided a text analysis based approach coupled with multivariate techniques to uncover the sentiment of various features associated with different brands and to determine the brand positions and segments through perceptual mapping and cluster analysis.

scholarly journals Reading Between the Lines: Understanding Customer Experience With Disruptive Technology Through Online Reviews

2021 ◽  
pp. 183933492199948
Author(s):  
Jeandri Robertson ◽  
Caitlin Ferreira ◽  
Jeannette Paschen
Keyword(s):  
Text Analysis ◽  
Online Reviews ◽  
Customer Experience ◽  
Disruptive Technology ◽  
Word Choice ◽  
Emotional Tone ◽  
Customer Reviews ◽  
Brand Engagement ◽  
Online Customer Reviews ◽  
Computerized Text Analysis

A customer’s experience with a brand, as evidenced in online customer reviews, has attracted multidisciplinary scholarly attention. Customer experience plays an important role as an antecedent to brand engagement, brand adoption, and eventual brand loyalty. Thus, it is important for businesses to understand their customers’ experiences so that they can make changes as necessary. The COVID-19 pandemic has brought unprecedented changes to the business landscape, forcing businesses to move online, with many utilizing enterprise video conferencing (EVC) to maintain daily operations. To ensure efficient digitization, many turned to the online reviews of others’ experiences with EVC before engaging with it themselves. This research examined how the customer experience is portrayed through emotional tone and word choice in online reviews for the EVC platform Zoom. Using computerized text analysis, key differences were found in the emotional tone and word choice for low- and high-rated reviews. The complexity and emotionality expressed in reviews have implications on the usability of the review for others. The results from this study suggest that online customer reviews with a high rating express a higher level of expertise and confidence than low-rated reviews. Given the potential dissemination and impact, digital marketers may be well advised to first and foremost respond to online reviews that are high in emotional tone.

scholarly journals A Computerized Text and Cluster Analysis Approach to Psychotherapy Talk

2020 ◽  
Vol 9 (1) ◽  
pp. 4-25
Author(s):  
Dennis Tay
Keyword(s):  
Cluster Analysis ◽  
Agglomerative Hierarchical Clustering ◽  
Analytic Thinking ◽  
Future Directions ◽  
Analysis Techniques ◽  
Entry Points ◽  
And Cluster Analysis ◽  
Language Styles ◽  
Computer Text ◽  
Qualitative Exploration

This paper illustrates an analytical approach combining LIWC, a computer text-analytic application, with cluster analysis techniques to explore ‘language styles’ in psychotherapy across sessions in time. It categorizes session transcripts into distinct clusters or styles based on linguistic (di)similarity and relates them to sessional progression, thus providing entry points for further qualitative exploration. In the first step, transcripts of four illustrative therapist-client dyads were scored under ten LIWC variables including ‘analytic thinking’, ‘clout’, ‘authenticity’, ‘emotional tone’, and pronoun types. In the next step, agglomerative hierarchical clustering uncovered distinct session clusters that are differently distributed in each dyad. The relationships between these clusters and the chronological progression of sessions were then further discussed in context as contrastive exemplars. Applications, limitations and future directions are highlighted.

scholarly journals INDIGO DALAM NOVEL SUPERNOVA AKAR KARYA DEE LESTARI: KAJIAN PSIKOLOGI SASTRA

EDU-KATA ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 141-148
Author(s):  
Erna Rakhmawati
Keyword(s):  
Data Analysis ◽  
Text Analysis ◽  
Textual Analysis ◽  
Literary Works ◽  
Analysis Techniques ◽  
Descriptive Research ◽  
Qualitative Descriptive ◽  
The Subject

The background of this research is many indigo events in the community and many indigo literary works. Indigo research in the  “Indigo dalam Novel Supernova Akar Karya Dee Lestari:Tinjauan Psikologi Sastra,” first aims to find the description of the indigo abilities experienced by the characters, secodly to find the causes of indigo experienced by characters, and thirdly to find out the type of indigo experienced by characters in the Supernova Akar. The research includes qualitative descriptive research on data analysis techniques using textual analysis or text analysis. The subject of this study is a character in the Supernova Akar. The result of research that is narated by Dee lestari source are; 1)levitasi, 2) procegnition, 30 psicometri, 4) teleportasi, and 5) clayvoyance. Indigo causes experienced by characters in the Supernova Akar are; 1) a gift from God, 2) from offspring, and from training. The type of indigo experienced by characters in the Supernova Akar are; humanis and artis.

scholarly journals Typology and characterization of the pampean beekeeping systems

2020 ◽  
Vol 51 (2) ◽  
pp. 299-318
Author(s):  
Tomás Bragulat ◽  
Elena Angón ◽  
Alberto Giorgis ◽  
José Perea
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Rural Areas ◽  
Export Market ◽  
Multivariate Techniques ◽  
High Productivity ◽  
Selection Of Variables ◽  
Socially Relevant ◽  
And Cluster Analysis ◽  
Random Survey

Objective: Identify and characterize the beekeeping systems of La Pampa (Argentina) using multivariate techniques based on the main structural, productive and economic characteristics. Methodology: The data was collected through a random survey of 80 beekeepers. The classification and description of the apicultural systems was based on a multivariate sequence comprising three stages: review and selection of variables, factor analysis and cluster analysis. Results: Factor analysis revealed that the size of the farm and the productive and economic performance of beekeeping jointly explained 66% of the variability. Through cluster analysis, three types of beekeeping have been identified: (i) Subsistence beekeeping grouped 55% of the farms, mainly characterized by small sizes and low productive and economic yields. (ii) Industrial beekeeping concentrated 54% of production in 15% of farms, mainly characterized by large sizes and high productive and economic yields. (iii) Commercial beekeeping grouped 30% of the farms, mainly characterized by high productivity with intermediate sizes. Limitations: The study has been carried out on a few farms due to the difficulty of obtaining answers to all the variables included in the survey. Practical implications: Beekeeping in La Pampa is generally a highly heterogeneous complement of income or family subsistence, with low productivity and low input use. Subsistence beekeeping is a socially relevant system for its contribution to family employment and income in rural areas. Industrial beekeeping is oriented to the export market and has a more competitive scale. Commercial beekeeping is situated on an intermediate scale.

scholarly journals A Framework for the Forensic Investigation of Unstructured Email Relationship Data

2011 ◽  
Vol 3 (3) ◽  
pp. 1-18 ◽  
Author(s):  
John Haggerty ◽  
Alexander J. Karran ◽  
David J. Lamb ◽  
Mark Taylor
Keyword(s):  
Forensic Analysis ◽  
Structured Data ◽  
Unstructured Data ◽  
Forensic Investigation ◽  
Qualitative Information ◽  
Data Set ◽  
Analysis Techniques ◽  
Potential Sources ◽  
Network Identification ◽  
Digital Investigation

The continued reliance on email communications ensures that it remains a major source of evidence during a digital investigation. Emails comprise both structured and unstructured data. Structured data provides qualitative information to the forensics examiner and is typically viewed through existing tools. Unstructured data is more complex as it comprises information associated with social networks, such as relationships within the network, identification of key actors and power relations, and there are currently no standardised tools for its forensic analysis. This paper posits a framework for the forensic investigation of email data. In particular, it focuses on the triage and analysis of unstructured data to identify key actors and relationships within an email network. This paper demonstrates the applicability of the approach by applying relevant stages of the framework to the Enron email corpus. The paper illustrates the advantage of triaging this data to identify (and discount) actors and potential sources of further evidence. It then applies social network analysis techniques to key actors within the data set. This paper posits that visualisation of unstructured data can greatly aid the examiner in their analysis of evidence discovered during an investigation.

Comparison of neural network and cluster analysis techniques for the reliability of high-speed machinery

1995 ◽  
Author(s):  
Colin P. Matthews ◽  
J. Y. Clark ◽  
Paul M. Sharkey ◽  
K. Warwick
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
High Speed ◽  
Analysis Techniques ◽  
And Cluster Analysis

A Study and Comparison of Sentiment Analysis Techniques Using Demonetization

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Krishna Kumar Mohbey ◽  
Brijesh Bakariya ◽  
Vishakha Kalal
Keyword(s):  
Sentiment Analysis ◽  
Text Analysis ◽  
Analytical Approach ◽  
Document Type ◽  
Analysis Techniques

Sentiment analysis is an analytical approach that is used for text analysis. The aim of sentiment analysis is to determine the opinion and subjectivity of any opinion, review, or tweet. The aim of this chapter is to study and compare some of the techniques used to classify opinions using sentiment analysis. In this chapter, different techniques of sentiment analysis have been discussed with the case study of demonetization in India during 2016. Based on the sentiment analysis, people's opinion can be classified on different polarities such as positive, negative, or neutral. These techniques will be classified on different categories based on size of data, document type, and availability. In addition, this chapter also discusses various applications of sentiment analysis techniques in different domains.

A Survey on Sentiment Analysis Techniques for Twitter

2022 ◽  
pp. 57-90
Author(s):  
Surabhi Verma ◽  
Ankit Kumar Jain
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Analysis Techniques ◽  
Goods And Services ◽  
Text Document ◽  
The Subject ◽  
Over Time

People regularly use social media to express their opinions about a wide variety of topics, goods, and services which make it rich in text mining and sentiment analysis. Sentiment analysis is a form of text analysis determining polarity (positive, negative, or neutral) in text, document, paragraph, or clause. This chapter offers an overview of the subject by examining the proposed algorithms for sentiment analysis on Twitter and briefly explaining them. In addition, the authors also address fields related to monitoring sentiments over time, regional view of views, neutral tweet analysis, sarcasm detection, and various other tasks in this area that have drawn the researchers ' attention to this subject nearby. Within this chapter, all the services used are briefly summarized. The key contribution of this survey is the taxonomy based on the methods suggested and the debate on the theme's recent research developments and related fields.

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