Exploration of Spa Leisure Consumption Sentiment towards Different Holidays and Different Cities through Online Reviews: Implications for Customer Segmentation

With the improvements in per capita disposable income, and an increase in work-related pressure, demand for leisure consumption such as foot bath spas is constantly increasing. Analysis of leisure consumption sentiment is of great importance for the leisure service industry—to meet customer needs, improve service quality and improve customer relationship management. However, traditional sentiment analysis approaches only aimed to ascertain the overall sentiment of the customer, which is less effective for analyzing customer satisfaction on account of customer size, different customer locations, and different leisure holidays. Sentiment analysis via online reviews can assist different businesses, including foot bath spa services, to better inform the development of customer segmentation strategies and ensure optimal customer relationship management. Hence, the objective of this paper is to explore foot bath spa leisure consumption sentiment towards different holidays and different cities by applying data mining via online reviews, so as to help optimize customer segmentation. A novel general framework and related sentiment analysis methods were proposed and then conducted through a collection of datasets from customers’ textual reviews of foot bath spa merchants in three cities in China on the Meituan social media platform. Findings confirm that the proposed general framework and methods can be used to gain insights into the swing characteristics of sentiment towards different holidays and different cities, to better develop customer segmentation according to the city-holiday emoticon face patterns obtained through sentiment tendency analysis from online social media review data. The study results can help to develop better customer and marketing strategies, thereby creating sustainable competitive advantages, and can be extended to other fields to support sustainable development.

Performance evaluation of after-sales service partners in the power tools industry

After-sales service (ASS) is a product support activity provided to customers; after-sales service that focuses on quality can create a sustainable competitive advantage. This study aims to the ASS performance, analyze the causes of performance constraints that occur in ASS and recommendations for improving the performance of ASS conceptually. Service partner performance achievement can be assessed from four categories, i.e. very good, good, bad, and poor, based on the actual level of performance compared to performance targets. The sample of this research is 12 ASS partners in the electrical equipment industry in West Java and Jakarta. Factors causing failure were analyzed descriptively using the fault tree analysis (FTA) method, which involved experts in focus group discussions (FGD). Based on the evaluation, 25% of partners were rated good, 50% poor, and 25% very poor. Partner performance achievement is carried out on 22 performance indicators, of which 20 performance indicators have not achieved a good rating. These indicators relate to brand issues, warranty, training, parts control and stock management, pricing, and service systems, including service centers and service facilities. The proposed concept that becomes a recommendation for improving the performance of power tools ASS is improving the operating system and the selection of partners in the service center and service dealer categories with industrial customer segmentation.

The Influence Of Marketing Mix On Usage Decision Of Auto Clean Waterless Car Wash Services

Increasing the number of vehicles makes a great business opportunity for car wash services. AutoClean Waterless is a company engaged in car wash services that utilise innovation by using special fluids so that it saves water. The problem faced by AutoClean in Bogor City is AutoClean’s sales have not been stable because there is still a decrease in transaction and termination of partners. Sample in this research were 185 respondents. Data were analysed using descriptive analysis and SEM LISREL. The result of this research shows that consumer use behaviour is influenced by weather, priority factors in using car wash services are quality, price, ease for access, and customers more often come to outlets that are not in the mall and majority customers use silver packages. Consumer perception of AutoClean innovation shows that innovation is one of the most essential factors in usage decisions of the consumer. The marketing mix (7P) has a significant influence on usage decisions. Marketing strategies to increase sales in AutoClean Waterless can be applied by making AutoClean customer segmentation based on customer characteristics. Then the company can make priority targets following following predetermined segmentation. Next, AutoClean can make positioning by looking at the priority factors when customers choose car wash services, such as quality, price and ease of access

Customer Segmentation Using Machine Learning

Customer Segmentation is the process of division of customer base into several groups called as customer segments such that each customer segment consists of customers who have similar characteristics. Segmentation is based on the similarity in different ways that are relevant to marketing such as gender, age, interests, and miscellaneous spending habits.The customer segmentation has the importance as it includes, the ability to modify the programs of market so that it is suitable to each of the customer segment, support in business decisions; identification of products associated with each customer segment and to mange the demand and supply of that product; identifying and targeting the potential customer base, and predicting customer defection, providing directions in finding the solutions.

Customer Segmentation Using Machine Learning

Nowadays Customer segmentation became very popular method for dividing company’s customers for retaining customers and making profit out of them, in the following study customers of different of organizations are classified on the basis of their behavioral characteristics such as spending and income, by taking behavioral aspects into consideration makes these methods an efficient one as compares to others. For this classification a machine algorithm named as k-means clustering algorithm is used and based on the behavioral characteristic’s customers are classified. Formed clusters help the company to target individual customer and advertise the content to them through marketing campaign and social media sites which they are really interested in.

Prepaid debit cards and banking intention

PurposeThis study examines the relationship between prepaid debit card use and the intention to open a bank account within twelve months. The Transtheoretical Model (TTM) of Behavior Change helped to conceptualize one's stage in the process of changing from unbanked status if desired. The Theory of Planned Behavior (TPB) provided a framework to examine factors that influence banking intention. Prepaid debit card use is considered a social norm as it is a popular alternative to banking, and these accounts have increasingly mimicked bank account features in recent years.Design/methodology/approachThree in-depth focus group interviews with low-income respondents were first conducted in 2012, which revealed a prolific use of prepaid debit cards. Most participants had previous banking history, and despite negative experiences, some requested information about banking terms and “free” banking. These themes and previous studies informed a TPB-based biprobit model, which was estimated using data of an unbanked sample from 2013, 2015 and 2017 waves of the US Survey of Unbanked and Underbanked Households.FindingsThough there was banking interest in the focus groups, no significant empirical association was found between recent prepaid debit card use and banking intention. Going deeper with another sample, we found that current cardholders were equally likely to have become recently banked or to be long-term unbanked but less likely to be long-term banked. Also, factors such as a more recent relationship with banks, use of other alternative financial services for transactions and credit, smartphone ownership, and trust increase banking intention.Research limitations/implicationsThe main limitation of the study is the cross-section quantitative data. Future research may track banking status over time, particularly as financial technology (fintech) evolves with alternatives that may influence banks and customers to adapt.Practical implicationsTo compete with “leapfrog” fintech banking alternatives, bank managers should consider utilizing customer segmentation to target “at-risk” customers and former customers with products and terms tailored to meet their banking needs. Banks can also tailor digital products to capture markets in banking desserts through mobile phones.Originality/valueThis mixed-methods study is unique in that it builds on insights from earlier in-depth interviews with real unbanked groups to examine a trend in prepaid debit card use and the impact on banking interest.