Customer Reviews Research Articles

Determinants of Purchase Intention of Skincare Products, Brand Image as Moderation On The Tiktok Platform

With the rapid development of Internet technology, many companies are now using the Internet as a major platform in their marketing strategy of skincare products, mainly through social media. The research aims to explore the factors that influence the intention of buying skincare products on the TikTok platform by considering the role of brand image as a moderation variable. The research method uses descriptive causality with a quantitative approach, and uses purposive sampling techniques. Primary data obtained from 267 respondents active TikTok users who were interested in skincare products and collected through online questionnaires. Data analysis is done using Structural Equation Modelling-Partial Least Square (SEM-PLS). Research findings show that social media marketing activities, online customer reviews, influencer endorsement, brand image can improve purchase intention, while price consciousness does not affect the purchase intent of skincare products on Tiktok platform. Implications, skincare manufacturers and marketers should pay attention to branding strategies on TikTok's platform to boost purchasing intentions. Further research is suggested to explore other factors that can influence the intention of buying skincare products in the digital age.

Exploring the Relationship between Social Media Engagement, Customer Reviews, and Brand Perceptions: A Comprehensive Study in Retail Industry

This research investigates the intricate relationships between social media engagement, customer reviews, and brand perceptions in the retail industry. Through a qualitative analysis of customer-generated content and social media interactions, key themes and patterns emerged, shedding light on the factors influencing brand sentiment and consumer behavior. The findings highlight the impact of positive brand experiences, strategic social media engagement, user-generated content, transparency, and authenticity in shaping brand perceptions and fostering customer loyalty. The study underscores the importance of delivering exceptional customer experiences, engaging meaningfully on social media platforms, and prioritizing transparency and authenticity to enhance brand perception and build trust in the digital era.

Guided Diverse Concept Miner (GDCM): Uncovering Relevant Constructs for Managerial Insights from Text

The Guided Diverse Concept Miner (GDCM) is an innovative deep learning algorithm tailored for the extraction of managerially relevant concepts from textual data, emphasizing the autonomy in discovering insights without predefined labels or guidance. This tool stands out by embedding words, documents, and concepts within the same vector space, which simplifies the interpretation of unearthed concepts and ensures their alignment with managerial outcomes. Central to GDCM’s methodology is its capacity to focus on concepts that are highly correlated with user-specified managerial outcomes, termed guiding variables, thereby enhancing the relevance and application of extracted insights in decision-making processes. The algorithm’s design inherently promotes the diversity of the recovered concepts, ensuring a broad spectrum of insights. Through practical application in analyzing customer reviews related to online purchases, GDCM not only identified key concepts influencing conversion rates but also validated its findings against established theories and prior causal research. This validation underscores GDCM’s utility in generating actionable, diverse insights tailored to specific managerial contexts, marking a significant advancement in how businesses leverage textual data for strategic decisions.

Pengaruh Content Marketing dan Online Customer Review pada Media Instagram terhadap Keputusan Pembelian di Krav.ery

Krav.ery is a custom cake business that was founded in June 2020, during the Covid-19 pandemic. Krav.ery uses Instagram social media in marketing their products and services, by uploading interesting and consistent content. The increased use of Instagram as a marketing medium can also be seen from the creativity of businesses in producing interesting content that is relevant to the pandemic situation. Because of that, many do s of Krav.ery's products and services through the content they upload. This has made Krav.ery experience an increase in sales during the pandemic until now, because the Content Marketing and s found on Krav.ery have increased customer purchasing decisions.Theories used to support this research are content marketing theory, online customer review theory, and purchasing decision theory. In this study, content marketing and online customer review as independent variables and purchasing decisions as the dependent variable. The objects of this research are content marketing, online customer review, and purchasing decisions on purchasing decisions at Krav.ery. The data collection technique used in this study is a communication technique by distributing questionnaires electronically via Google Forms to 105 respondents. The sampling technique used is a non-probability sampling technique with a judgment sampling approach. The procedure used for model testing and data processing is using the SBM SPSS Statistic 20 software. The results obtained from the data analysis test in this study indicate that variables X1 (content marketing) and X2 (s) affect variable Y (purchasing decisions), namely that there is a positive influence between content marketing and online customer review on consumer purchasing decisions at Krav.ery This is shown by the results of research on visual communication variables influencing purchase intention variables. The conclusion of this study is to test 105 respondents who have purchased and consumed products from Krav.ery, stating that there is a positive influence of content marketing and online customer review on Krav.ery on purchasing decisions at Krav.ery

PENGALAMAN PELANGGAN MEMBELI TIKET KONSER COLDPLAY: MENAMBANG ULASAN ONLINE BERDASARKAN PEMODELAN TOPIK DAN ANALISIS SENTIMEN

This study aims to analyze customer experience in buying tickets for the Coldplay Concert in Indonesia using sentiment analysis and topic modelling. Data is collected from online customer reviews about concert ticket purchases via social media platforms such as Twitter. The stages of the research include data collection, data labelling, data pre-processing, topic modelling using Latent Dirichlet Allocation (LDA), sentiment analysis, and interpretation of the results. The results of the sentiment analysis show that most reviews are positive, with customers expressing satisfaction with the ticket-buying process, experience at the concert, Coldplay's performance, and customer service. Several primary topics frequently appearing in reviews have been identified through topic modelling, including ticket-buying, concert experience, ticket prices, customer service, concert performance, concert location, togetherness with fans, accessibility, concert facilities, and supporting events. The interpretation of each topic provides insight into customer preferences and expectations. Recommendations for concert organizers include improving customer service, ensuring performance quality, choosing a convenient concert location, and paying attention to accessibility and the atmosphere around the concert venue. This research provides an in-depth understanding of customer experience and can serve as a guide for concert organizers to improve customer experience in the future.

CONSUMER BEHAVIOURL ANALYSIS IN E COMMERCE AS MYNTRA USING DATA ANALYTICS

CONSUMER BEHAVIOURL ANALYSIS IN E COMMERCE AS MYNTRA USING DATA ANALYTICS

Dampak Marketplace Layout Design, Sales Promotion dan Customer Review Terhadap Impulse Buying

The Shopee marketplace continues to show its existence as the marketplace with the most visitors. The distinctive color combination and well-arranged navigation menu can provide a different feel when visiting the maketplace. Information about various products can be seen very clearly on the main homepage, and shopping promo offers are also clearly visible on the slider screen on the main marketplace screen. The marketplace layout is also supported by the existence of a customer review navigation feature as a window for customer reviews of products and services that have been received. Shopee seems to understand how urgent the role of product information and customer reviews is in minimizing risks and supporting unplanned purchasing decisions. Therefore, this research aims to determine the impact of marketplace layout design, sales promotions and customer reviews to increase impulse buying. Research was conducted among Y generation in Kendari City who shopped at the Shopee marketplace. The total sample is 160 respondents. A questionnaire with a Likert scale was used as a data collection tool. Data were analyzed by multiple linear regression using SPSS software. The results of data analysis show that marketplace layout design does not have a significant influence on impulse buying. Meanwhile, sales promotion and customer reviews have a significant positive influence on impulse buying. These findings provide information to marketplace managers and sellers about the urgency of the role of sales promotion as a short-term strategy in accelerating impulse buying. Apart from that, the existence of a customer review feature on the marketplace website can contribute to impulse buying. So it becomes urgent to provide stimulus, such as giving rewards or incentives to customers who have purchased to be able to provide reviews of the products and services they receive.

Evaluating brand equity in the hospitality industry: Insights from customer journeys and text mining

To achieve effective marketing, continual enhancement of product and service quality is pivotal. Assessing brand equity encompasses diverse methods, including questionnaires and interviews, unearthing invaluable customer insights. The choice of data collection methods, driven by researchers' objectives, may extend beyond direct customer interactions to employ techniques such as text mining, extracting insights from customer posts and reviews. A study at Phuket's Amari Hotel seamlessly integrated customer journey analysis with text mining, yielding a robust hotel equity model. Notably, the findings unveiled the substantial influence of factors like location and community value on hotel equity, often overlooked in previous studies. Remarkably, the hotel accommodation room emerged as the dominant touchpoint (81 %). These insights can fuel the transformation of marketing strategies and brand equity assessment. Moreover, this framework accommodates dynamic customer behavior while delivering cost efficiencies compared to alternative machine learning methods.

A Fullstack Web Application to Provide On-demand Household Services

Abstract: A Fullstack Web Application to Provide On-demand Household Services is a paper focused on designing and creating a full stack web application. The application aims to help users find nearby household service providers, such as electricians, plumbers, gardeners, carpenters etc. Users can easily browse the available services, book the appointments, track the location of service professionals, and share feedback about completed work. For service professionals, the application offers tools to manage appointments and track earnings. The application incorporates advanced technologies like Blockchain for secure storage of customer ratings, reviews, and work history generated by service professionals. Additionally, Machine Learning is utilized to develop chatbots for real-time user assistance. Few technological domains which are covered in this application are Full Stack development, Blockchain technology and Machine Learning. In summary, the goal of this application is to improve the skilled workforce connection system, making it efficient, secure, and engaging for all users

Understanding of The Important Role of Online Customer Reviews in Social Commerce on Customer Trust and Impulse Buying

This research aims to investigate the impact of online customer reviews on customer trust and impulse buying, by utilizing the Stimulus- Organism-Response (S-O-R) theoretical framework. Furthermore, it ex- amines the impact of customer trust as a mediating variable between online customer reviews and impulse buying. Data was obtained through the use of an online questionnaire which was filled out satisfactorily by a total of 150 participants. Participants in this research were individuals who had engaged in impulsive online purchasing behavior and demonstrated con- sumer trust. The methodology used in this research involves the utilization of Structural Equation Modeling with Partial Least Squares (SEM-PLS). The findings show that online customer reviews significantly influence con- sumer trust and impulse buying, while consumer trust significantly influ- ences impulse buying. Moreover, consumer trust partially mediated the relationship between online customer reviews and impulse buying. This research encourages online customer reviews for social commerce in Indo- nesia. When customer trust increases, consumers will make impulse pur- chases based on recommendations. Individuals who show a tendency to engage in impulsive online purchasing behavior are supported by the trust that the individual has. This research provides theoretical and practical implications that can provide valuable insight for further online consumer purchase research.

The Influence of Content Marketing, Online Customer Reviews, and Cash on Delivery on Consumer Purchasing Decisions at Tiktok Shop in Batam City

The purpose of this research is to find out how Tiktok Shop in Batam City, Indonesia uses content marketing, online customer review, and cash on delivery to influence customers' choices to buy The Originote items. The data for this research was gathered using a Google Form questionnaire. People that use the TikTok app and buy the original items will fill out the survey, which uses a Likert scale for measuring. 204 people participated in this research. The research used a causality design to examine the relationship between the variables that were chosen for analysis. With SPSS version 25, we were able to determine the coefficient of determination. Content marketing, online customer reviews, and cash on delivery impact purchase choices by 48.5%, according to the study and data analysis that was carried out.

Aspect-Level Sentiment Analysis of Customer Reviews Based on Neural Multi-task Learning

In the era of big data, major e-commerce platforms are facing the challenge of an exponential growth in the number of user comments. Effectively utilizing these comments has become an urgent issue. Traditional manual statistical methods are no longer able to meet the demands for accuracy and real-time analysis. The rise of artificial intelligence-based text mining techniques provides a new approach to address this problem. By building deep learning analysis models, it is possible to uncover user preferences and product characteristics, helping businesses proactively adjust sales strategies, improve product and service quality, and achieve precise marketing. Text sentiment analysis is one of the important research directions in the field of text mining. Its basic idea is to transform subjective texts with emotional color into structured data, and then use machine learning, deep learning, and other artificial intelligence technologies to extract emotional features and discover knowledge patterns. This article proposes a deep learning-based sentiment analysis model, which includes a shared sentiment prediction layer used to transfer emotional knowledge between different aspect categories and alleviate the problem of insufficient data. The model consists of two parts: the Aspect Category Detection (ACD) classifier based on attention mechanism and the Aspect Category Sentiment Analysis (ACSA) classifier. The ACD part generates word weights using attention mechanism, while the ACSA part predicts the sentiment of words and combines weights to determine the sentiment of aspect categories within sentences.

Online Review Analysis from a Customer Behavior Observation Perspective for Product Development

Observing customers is one of the methods to uncover their needs. By closely observing how customers use products, we can indirectly experience their interactions and gain a deep understanding of their feelings and preferences. Through this process, companies can design new products that have the potential to succeed on the market. However, traditional methods of customer observation are time-consuming and labor-intensive. In this study, we propose a method that leverages the analysis of online customer reviews as a substitute for direct customer observations. By correlating a customer journey map (CJM) with online reviews, this research establishes a verb-centric analysis that produces a CJM based on online review data. Various text analysis techniques were utilized in this process. When applying online retail site review data, our method of customer observation required one week. This proved to be more efficient in comparison with traditional customer observation methods, which typically need at least one month to complete. Additionally, we observed that the customer behavior-based VOC (voice of customer) identified during the CJM mapping process offers broad insights that are distinct from traditional product feature-centric review analyses. This behavior VOC can be effectively utilized for product improvement, new product development, and product marketing. To verify the usefulness of the behavior VOC, we asked product development experts to evaluate the quantitative analysis results of the same reviews. The experts evaluated the CJM as useful for product conceptualization and selecting technology priorities.

How Ratings Shape the Supply on Sharing Economy Platforms: A Longitudinal Cohort Study on Listing Survival on Airbnb

ABSTRACT Customer reviews on sharing economy platforms like Airbnb have been studied extensively. However, their effectiveness has recently been questioned. We investigate their role regarding listing churn on Airbnb using panel data. We find that listings with few or poor ratings will likely leave the platform. Correcting for the listings’ characteristics, we find that the best possible rating strongly predicts the listings’ success. We conclude that the best rating is a necessary but insufficient condition for success.

Evaluation of the Effectiveness of Neural Network Models for Analyzing Customer Review Sentiments on Marketplace

According to the 2019 report, Tokopedia is the most visited marketplace with 140,000,000 visitors per month, making it one of the most popular marketplaces in Indonesia. Customers have the opportunity to write reviews about the products they purchase at the end of the transaction process on Tokopedia. The aim of this research is to conduct sentiment analysis on product reviews on Tokopedia. Three neural networks that will be used for text classification are Bi-GRU, GRU, and LSTM. The data processing technique is divided into training and testing samples, split into 80%:20% using the holdout technique. The BI-GRU algorithm has an accuracy of 0.93% and precision of 0.96, better than the other two methods LSTM and GRU, which each have an accuracy of 0.92 and recall of 0.91.

The Influence Of Storytelling, Social Media Marketing, and Customer Reviews Of Coffee Roasters On The Purchase Decision Of Single-Origin Arabica Coffee Beans By Coffee Shops

The coffee industry, centered on Arabica and Robusta beans, relies heavily on coffee shops for delivering quality products, particularly single-origin Arabica coffee. Coffee roasters, operating in the B2B market, supply coffee shops, shaping product quality and customer satisfaction. This study examines the impact of marketing strategies—storytelling, social media, and customer reviews—on coffee shop owners' decisions to purchase Arabica beans. Utilizing a quantitative approach with 222 respondents and multiple linear regression analysis in SPSS, findings show a significant positive influence of these strategies on purchase decisions. Together, they explain 70.6% of the variance, with remaining factors beyond the study's scope. This underscores the crucial role of marketing strategies in shaping coffee purchasing decisions for coffee shops.

Decision analysis of PPP project's parties based on deep consumer participation.

Although PPP(Public-private partnership) mode has been applied for a long time in infrastructural project, the success rate is not very high. The sustainability of PPP projects is still influenced by many factors. In order to examine the evolutionary stable strategies (ESSs) of social capital, government, and paying consumers, a tripartite evolutionary game model is established in this work. In order to further promote consumer participation, it is necessary to make the assumption that customer oversight and review can have an impact on service prices. The results show: i)The strategy choice of consumer depends on the comparison between supervision cost of consumer and price coefficient for consumer to social capital. ii)Consumer supervision can promote the provision of high-quality services by social capital. iii)The difference between high-quality cost and low-quality cost, subsidy coefficient, price coefficient and supervision cost of consumer are critical factors influencing both evolutionary results and trajectories. This paper also puts forward policy implications for the three stakeholders to promote social capital's high-quality strategy so as to maintain the sustainability of PPP projects.

Helpful clicks: consumers’ perceived diagnosticity of online book reviews

This paper investigates online feedback systems and their perceived diagnosticity in the book industry, using a mixed methods approach, with data retrieved from Amazon. Several variables concur to give the overall perceived diagnosticity of customer reviews: score systems, written text and its themes, and reviews date. Even though reviews are positively biased, long negative reviews are perceived as the most diagnostic. The older the review, the higher the perceived diagnosticity, due to the combined effects of early bird bias and winner circle bias. By understanding the role that variables have on diagnosticity, the findings can be used by scholars, as well as by e-commerce and marketplace webmasters who seek ways to improve the feedback systems of the online platforms in or outside the book industry.

PENGARUH ONLINE CUSTOMER REVIEW, HARGA DAN ONLINE CUSTOMER RATTING TERHADAP KEPUTUSAN PEMBELIAN MELALUI TOKOPEDIA (Studi Pada Konsumen Toko Online Ventela Store Official di Kota Medan)

This study aims to analyze how the influence of online customer reviews, prices and online customer ratting on consumer purchasing decisions Ventela store in Medan city. The influence between online costumer reviews, prices and online costumer ratting will be analyzed partially and simultaneously on employee performance. The form of research used in this research is quantitative research with an associative approach. Sampling was done through non-probability sampling techniques and used 100 respondents as samples. The research findings show that: The results of this study indicate that Online customer review and Online customer review have a significant effect, both partially and simultaneously (together) on consumer purchasing decisions. However, price has no significant effect on consumer purchasing decisions. Thus, the results of this study make an important contribution to the management of Ventela stores in Medan city in improving marketing strategies and employee performance management. Ventela store management can utilize online customer reviews and ratings as one of the factors that can improve consumer purchasing decisions and employee performance.

A Study on Maybelline Products: A Review on Customer Satisfaction

This research aims to conduct a comprehensive study on Maybelline products, a renowned brand in the beauty and cosmetics industry, with a specific focus on understanding customer satisfaction. Maybelline, a subsidiary of L'Oréal, has established itself as a global leader in offering a diverse range of makeup and beauty products. Employing a mixed-methods research design, combining online reviews, surveys, and interviews, this study seeks to explore the perceptions, preferences, and overall satisfaction levels of consumers using Maybelline products. Key objectives include analyzing customer reviews on various platforms, assessing the factors influencing customer satisfaction, identifying popular and well-received products, and understanding customer expectations from the brand. The study also aims to uncover any challenges or areas for improvement as perceived by customers. The findings of this research are expected to contribute valuable insights to Maybelline and the broader beauty industry, aiding in the enhancement of product offerings, marketing strategies, and overall customer satisfaction.