Customer Attributes Research Articles

A Study on the Marketing Strategy Based on the Selection Attributes of Golf Practice Customers Using the Pursuit Benefits

A Study on the Marketing Strategy Based on the Selection Attributes of Golf Practice Customers Using the Pursuit Benefits

Human employees and service robots in the service encounter and the role of attribution of theory of mind

A frequently made assumption – supported in a large number of empirical studies – is that customer satisfaction stemming from a service encounter influences the customer's subsequent word-of-mouth activities. The present study re-examines this association with respect to both human service employees and service robots (which are expected to become more common in service encounters in the near future). First, it is assumed that the customer's attribution of theory of mind to a service agent is an important source of information for the formation of a satisfaction assessment. Indeed, it is assumed that the agent's theory of mind is a prerequisite for understanding the customer's needs. Second, in contrast to many existing studies, word-of-mouth is captured in terms of the valence of what customers actually say (as opposed to various forms of intentions to engage in word-of-mouth, which represent a dominant contemporary operationalization of word-of-mouth). A between-subjects experiment was conducted in which a service agent's identity (service robot vs. human) and service performance (poor vs. good) were the manipulated factors. The results show that both these factors influenced attribution of theory of mind to the agent, and that attribution of theory of mind enhanced customer satisfaction. The results also show that customer satisfaction affected word-of-mouth content in a valence-congruent way.

Assessing the effectiveness of OTT services, branded apps, and gamified loyalty giveaways on mobile customer churn in the telecom industry: A machine-learning approach

Telecom operators allocate a significant amount of resources to retain their customers as the organic growth in the number of customers is slowing down. Gamified loyalty programs, branded apps, and over-the-top (OTT) services emerged as ways to develop customer acquisition and retention strategies. Despite these strategies, some mobile customers still churn; therefore, churn prediction plays an essential role in the sustainable future of telecom businesses. Churn prediction is used both to detect customers with a high propensity to churn and to identify the reasons behind their churn behavior. This study examines several features affecting the churn behavior of mobile customers, including branded apps, gamified loyalty programs, and OTT services. In this study, the secondary data is provided by a telecom operator and contains the attributes of both churner and non-churner mobile customers. Logistic regression and random forest classifiers are compared in terms of their predictive power, and we used the latter as the machine learning algorithm in the churn prediction model. To understand the variable importance, mean decrease in impurity and permutation importance are performed. The key findings of this research reveal that while gamified loyalty giveaways and branded app strategies are effective, OTT service strategies show lower importance in predicting mobile customer churn behavior.

Customer attributions for LGBT-friendly branding hotels and skepticism

PurposeThis paper aims to examine the relationships between motives, skepticism and brands evaluations in the LGBT-friendly branding hotels context.Design/methodology/approachAn online survey is created and 278 LGBT participants are recruited through a consumer panels firm. The research model is tested through structural equation modeling.FindingsValues-, stakeholder- and strategic-driven motives are negatively related to skepticism, while egoistic-driven motive is positively related to skepticism. Also, sexual orientation openness moderates the relationship between stakeholder-driven motives and skepticism. Finally, skepticism is negatively related to brands evaluations.Research limitations/implicationsThe research findings are limited to the LGBT-friendly hotels. However, this research contributes to the CSR and LGBT consumer research by examining the relationships between the four motives and skepticism considering LGBT customers’ sexual orientation openness level.Practical implicationsHotels should devote greater efforts to communicating that their LGBT-friendly branding efforts are genuine by acknowledging both the social benefits and the business interests.Social implicationsAs more and more hotels promote themselves as LGBT-friendly brands, a key challenge is reducing skepticism by appropriately conveying their motives. This research sheds light on this critical issue.Originality/valueAlthough existent research on LGBT-friendliness has established its importance; there is a lack of understanding as to how customers perceive LGBT-friendly branding hotels. This research examines the four motives of hotels presenting themselves as being LGBT-friendly and their relationships to skepticism.

Customer Churn Prediction Using Machine Learning

Customer churn, the phenomenon where customers cease their relationship with a business, is a critical concern for e-commerce platforms striving for sustained growth and profitability. Predicting churn in advance can empower businesses to implement proactive retention strategies, thereby mitigating revenue loss and enhancing customer satisfaction. In this study, we propose a machine learning-based approach to predict customer churn in e-commerce settings. We begin by collecting extensive data encompassing various customer attributes, transactional history, browsing behavior, and engagement metrics. Leveraging this rich dataset, we employ state- of-the-art machine learning algorithms such as logistic regression, random forests, gradient boosting machines, and neural networks for predictive modeling. Feature engineering techniques are applied to extract meaningful patterns and insights from the raw data, enhancing the predictive performance of the models. By deploying the developed predictive model into production environments, businesses can proactively identify at-risk customers and tailor targeted retention strategies to mitigate churn. The results demonstrate the effectiveness of machine learning in accurately predicting customer churn in e-commerce, enabling businesses to proactively implement retention strategies and enhance customer engagement. Index Terms— Machine learning, E-commerce, Logistic re- gression, Decision tree, Random forest algorithm.

Multi-factor authentication scheme based on custom attributes

Multi-factor authentication scheme based on custom attributes

The signal detection expectation profiling method with a two-step rating for guiding product optimization

Understanding consumer requirements with respect to the sensory attributes of food and the sentimental consequences is critical for enhancing consumer satisfaction and achieving market success. A recent innovation, the signal detection expectation profiling method, introduced by Lee, Kim, van Hout, and Lee (2021), utilizes the two-step rating-based ‘double-faced applicability (DFA)’ test to create expectation profiles for product attributes, quantified by d′A output measures. This study aimed to demonstrate and test the usage and efficacy of the d′A expectation profiling method to provide insight for product development. The efficiency of this approach for using a two-step rating-based DFA was examined first and the information guided by the d′A expectation profiling output measures was compared to satisfaction drivers identified through partial least square (PLS) regression and landscape segmentation analysis (LSA), commonly used to link consumer satisfaction/liking and sensory perception. Consumer expectations and satisfaction/sensory evaluations of six different mayonnaise products formed the dataset. Overall, the d′A expectation profiles effectively identified the key attributes significantly impacting overall satisfaction, aligning with the results of the PLS regression and LSA. The advantage of d′A expectation profiles lies in their quantitative representation of the degree of expected sensory attributes, extending beyond the scope of actual evaluated products and offering actionable insights for product optimization. Furthermore, by incorporating custom attributes (descriptor pairs) based on the hedonic valence of target consumers, the d′A expectation profile showcases the potential for effectively addressing consumer-relevant attributes tailored to specific target consumer groups.

Marketing Communication Strategy in the Retail Sector: Examining Repurchase Intention

In the face of intensifying market competition, the significance of retail marketing strategies and tactics cannot be understated, as they are pivotal in enhancing customers’ satisfaction, fostering loyalty in customers, and ultimately elevating the likelihood of repurchase intentions. This research strives to discern the distinct attributes of customers and gauge their perceptions of the marketing strategies employed by Hypermarket Companies. Additionally, it examines the interplay between customer satisfaction, loyalty, and the consequent cultivation of repurchase intentions. Furthermore, the study explores the foundational retail marketing strategies that underpin the establishment of customer loyalty. The analytical approach encompasses Confirmatory Factor Analysis, SEM-PLS, and Biplot techniques. The findings underscore a customer profile primarily comprising males within the 18–29 age bracket, married, childless, holding undergraduate degrees, enjoying middle-income status, and engaged as private sector employees. Notably, the paramount driver shaping loyalty is the quality of retail service. It is intriguing to note that while the retail mix strategy within Bandung's hypermarkets is predicted to wield no direct influence on loyalty, marketing communication emerges as a potent determinant significantly impacting repurchase intentions. This study contributes to understanding customer behavior and loyalty in hypermarkets. It sheds light on the critical role of service quality in building loyalty. It highlights the importance of effective marketing communication for encouraging repurchase intentions and offers valuable insights for retail marketing strategies. Doi: 10.28991/ESJ-2024-08-01-08 Full Text: PDF

Comparison of Density-Based Spatial Clustering of Applications with Noise (DBSCAN), K-Means and X-Means Algorithms on Shopping Trends Data

This study extensively compares the efficacy of three clustering algorithms of DBSCAN, K-Means, and X-Means in analyzing shopping trend data, utilizing the Davies-Bouldin Index (DBI) for group validity assessment. The dataset, sourced from Kaggle.com, encompasses various customer attributes. Results indicate that the DBSCAN algorithm demonstrates superior cluster validity, outperforming K-Means and X-Means. Specifically, with an Eps value of 0.3 and MinPts value of 3, DBSCAN achieves an optimal DBI value of 0.1973. K-Means follows with a DBI value of 2.2958, and X-Means attains its best value (2.5663) with k=3. This research underscores the pivotal role of clustering algorithms in understanding shopping trends and customer preferences, offering valuable insights into their comparative performance.

Community-based management for low-digitalized communities using cross-cutting purchasing behavior

The need for community-based management to revitalize the economy of commercial areas by using consumer behavior analysis focusing on transactions has increased. Low-digitalized shopping communities, commercial communities that include retailing that have not introduced digital technologies, require community-based management using consumer behavior analysis. However, low-digitalized shopping communities cannot collect cross-cutting consumer behavior data using digital technologies such as point of sales (POS) systems. This difficulty obscures the novel management potential of applying such customer behavior analysis to community-based management. Our study aims to bridge the gap between low-digitalized shopping communities and community-based management using customer behavior analysis. To achieve this purpose, this study proposed a novel management approach using data collected using paper-based community currencies and its analysis method. Two field experiments were performed in low-digitalized shopping communities in Japan using two types of community currencies: from-to (FT) and customer attributes (CA). This study illustrated the possibility of community-based management in low-digitalized shopping communities and extending conventional retailing management methods using customer behavior analysis to community-based management.

Creating social value through operational supply chain transparency

AbstractWhether inclusive business (IB) supply chain transparency (SCT) can add value to a firm as an essential strategic resource is controversial. This study investigates how IB SCT affects willingness to pay a premium (WPP). Conditional process analysis was used to analyze survey data collected from 633 Chinese subjects in a scenario‐based role‐playing experiment. Analysis of the responses demonstrates that IB SCT indirectly enhances WPP through indirect reciprocity. In addition, the stronger the costly prosocial attributes of customers, the greater the indirect effect of IB SCT on WPP. The results indicate that IB SCT can add value to firms through consumer rewards, especially for costly prosocial consumers. These research constructs provide valuable insights for operators adopting IB to help them communicate and coordinate IB resources with consumers for IB sustainability.

Machine Learning Based Customer Churn Prediction Using Improved Feature Selection Techniques

In the contemporary, fiercely competitive business environment, the retention of customers holds utmost significance for the continued growth and profitability of companies. Customer churn, which entails the defection of customers to rival services, stands out as a critical and prevalent challenge in a variety of sectors, spanning from telecommunications to e-commerce. This study delves into the realm of customer churn prediction, presenting an innovative approach that leverages machine learning and advanced feature selection techniques. The research utilizes a comprehensive dataset encompassing customer attributes, historical behavior, and churn labels. Employing state-of-the-art machine learning algorithms, including decision trees, random forests, and support vector machines, a predictive model is developed to identify potential churners accurately. The distinctive aspect of this study lies in its feature selection methodology, which integrates recursive feature elimination (RFE) with feature importance ranking. This hybrid approach refines the feature set, eliminating irrelevant or redundant variables, thereby enhancing model efficiency and interpretability. Empirical results showcase the effectiveness of the enhanced feature selection approach in improving predictive accuracy and model performance.

Classifying Customer Attributes with Importance Performance Analysis and Fuzzy Kano

Analyzing what consumer needs remains every day’s challenge for every business. Every business entity requires continuous effort as consumers become more demanding and have more access to product/service offerings, leading to more competitive market dynamics and the necessity for more innovative ways of offering products/services. The research aims to recommend a set of customer attributes for the studied company and analyze the selected attributes using a combination of Importance Performance Analysis (IPA) and fuzzy Kano. The research is a case study of a company selling gift vouchers for individual and corporate consumers. The research combines literature study and affinity diagram workshop to identify the required consumer attributes, which are analyzed using the integration of IPA and fuzzy Kano. The results suggest that the studied company should concentrate on several attributes, such as A7-simple requirement during the purchasing process, A10-no administration fee during purchase, A14-cross promotion with various sister brands, and A15-no minimum purchase. The attributes fall under “concentrate here” in the IPA grid while at the same time, those are considered as “effective improving area” in the fuzzy Kano grid. The studied company is also recommended to keep their good work on the attribute of A5-expiry date longer than one year so that it remains their competitive attribute and does not fall into the other inferior quadrants.

코로나19 발생 후 외식산업 연구주제에 대한 연구

The purpose of this study is to find ways to develop the food service industry through analysis of research trends related to the food service industry after COVID-19. In this study, through detailed search on RISS, 133 papers related to 'food service' with English abstracts were analyzed using R package to analyze keyword frequency, word clouding, keyword network analysis, LDA topic modeling, and TF, TF.-IDF verification was performed. The analysis results are as follows. First, as a result of keyword frequency analysis and word clouding, it was found that keywords related to service, food, restaurant, intention, customer, consumer, satisfaction, quality etc. were frequently used. In addition, the appearance of delivery and covid related to Corona 19 was confirmed. Second, As a result of word network analysis, each word was related to food, customer, franchaise, company, brand, consumption, consumer, etc. centering on service, and it was found that business, job, hmr, relationship, convenience, performance, foodservice, etc. were studied separately. Third, as a result of TF and TF-IDF analysis, words related to COVID-19 such as stress, honbab, stability, coffee, complain, solo, hmr, conflict, retro, and eatingout did not appear in Hi-TF, but appeared in Hi-TF-IDF. From this, it can be seen that COVID-19 is having a great impact on the food service industry. Finally, as a result of LDA topic modeling, the appropriate number of topics was found to be 10, which is being studied as the subject of delivery, franchise, HMR, corporate strategy, employee, customer satisfaction, COVID-19, globalization, customer attributes, and store environment according to each characteristic. confirmed.

Estimating promotion effects in email marketing using a large-scale cross-classified Bayesian joint model for nested imbalanced data

We consider a large-scale, cross-classified nested (CRON) joint model for modeling customer responses to opening, clicking, and purchasing from promotion emails. Our logistic regression-based joint model contains crossing of promotions and customer effects and allows estimation of the heterogeneous effects of different promotion emails, after adjusting for customer preferences, attributes, and historical behaviors. Using data from an email marketing campaign of an apparel retailer, we exhibit the varying effects of promotions not only based on the contents of the email but also across the three different stages, viz. open, click, and purchase of the conversion funnel. We conduct Bayesian estimation of the parameters in the joint model by using a block Metropolis–Hastings algorithm that not only incorporates nested subsampling to tackle the severe imbalance between conversions and no conversions but also uses additive transformation-based modifications of random walk Metropolis to scale estimation for large numbers of customers. We extend our approach to a segmented cross-classified nested (SCRON) joint model that encompasses the possibility of varying promotion effects across different customer segments. The resultant high-dimensional model is estimated using spike-and-slab priors on the promotion and customer segment interactions. Our nested joint model accounts for the correlations in customer preferences across the conversion funnel. Based on the promotion estimates from the model, we demonstrate how marketers can use different priced, nonpriced, and combination of price and nonprice promotions to increase brand awareness or increase purchases. Comparing estimates from CRON and SCRON models, we display the benefits of targeted marketing by using email promotion lists which are separately optimized for the different customer segments.

Stopping the Spread: How Blame Attributions Drive Customer-to-Customer Misbehavior Contagion and What Frontline Employees Can Do to Curb It.

Service encounters nowadays are increasingly characterized by customer-to-customer (C2C) interactions where customers regularly become targets of other customers' misbehavior. Although previous research provides initial evidence of the contagiousness of such C2C misbehavior, it remains unclear whether, how, and why C2C misbehavior spreads when frontline employees (FLEs) are involved and what FLEs can do to curb it. Two online and one field experiment in the context of co-working and transportation services reveal that FLE-directed blame attributions drive the spread of C2C misbehavior while perpetrator-directed blame attributions reverse it. These blame attributions are greater the more severely customers judge other customers' misbehavior. Findings further rule out alternative contagion mechanisms (social norms and emotional contagion) and show that contagion spills over to C2C misbehavior unrelated to the initial transgression. By specifying how contagion unfolds and by explicating the central role blame attributions play in C2C misbehavior contagion, this research uncovers its social dynamics, thus extending existing theory on customer misbehavior and attribution theory in multi-actor settings. Managerially, this research provides FLEs with explicit guidance on what they should do (personalized FLE interventions delivered either in person or remotely) and avoid doing (disapproving looks, FLE service recovery) when faced with C2C misbehavior.

How attributions of coproduction motives shape customer relationships over time

Despite the proliferation of coproduction concepts in various B2C contexts, knowledge on how coproduction shapes customer relationships is still surprisingly limited, as prior studies find mixed results and are bound to a short-term perspective. The present study addresses these limitations by providing first insights into the underlying psychological processes that explain differences in the short- and long-term relationship consequences of positive and negative coproduction perceptions. Drawing from the multiple inference model, this research shows how customers’ ambivalent attributions of a firm’s coproduction motives (i.e., firm-serving and customer-serving) affect customer satisfaction, willingness to pay, and spending behavior over time. The results of a latent growth analysis based on a longitudinal field study (n1 = 12,662; six waves) show that coproduction can harm customer relationships in the long-run, as the detrimental effects of firm-serving motive attributions are temporally more persistent than the favorable but ephemeral effects of customer-serving motive attributions. An additional experiment (n2 = 931) and field study (n3 = 360) confirm the generalizability of the key findings and provide new managerial insights into how firm-specific characteristics of a coproduction concept (i.e., coproduction intensity, design freedom, monetary savings) influence customer attributions different coproduction motives and thereby shape customer relationships over time.

CRUSHING MACHINE DESIGN FOR WOOD WASTE UTILIZATION USING AXIOMATIC DESIGN METHOD.

The increased number of furniture orders led to a significant amount of material waste. Bahana Raya produces 5-7 m3 of multiplex and blockboard waste. Multiplex waste in the form of lumber and wood chips has been solely utilized as fuel for stoves being used for cooking for worker lunches, with the remainder simply being burned as waste. This study was carried out to provide solutions for dealing with existing waste in terms of multiplex and blockboard waste crushing machine. A tool or machine which can address all of these shortcomings were then developed. The axiomatic Design method can be utilized that can address these shortcomings workers’ needs and expectations based on field conditions. Completing the stages of determining Customer Attributes (CA) and making a hierarchy of Functional Requirements (FRs) and Design Parameters (DPs) can result in a product design that meets all of the workers' needs and expectations. The result of this study suggested the development of a multiplex and blockboard waste crushing machine, which offers a number of benefits over existing wood crusher machines, including the ability to produce finer sawdust in accordance with MDF raw material criteria (0,43 g/m3).

The Research on Dynamics in Phone Sales Marketing Campaign Based on Machine Learning

Phone market campaign is one of the selling methods that banks use to attract new term deposits. Identifying attributes of customers is essential to increase the successful rate of a phone marketing campaign. This paper uses a dataset from a Portuguese bank where various features and attributes of customers are summarized. Four methods are used to analyze this issue, firstly decision tree method, then random forest method, K nearest neighbor and lastly logistic regression. After evaluating and reviewing their confusion matrices and generated scores, the decision has been made to choose the model of random forest as it possesses the highest mean of all metrics of classification. In conclusion, the duration of contact, age, how many days have passed since last contact, the month of the contact, and the number of contacts on one customer are deemed as more important than other attributes under a random forest classifier. The findings of this paper implicates that the Portuguese bank needs to focus more of these attributes to obtain a sustainable development.

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

Consumption behavior prediction reveals customer attributes, personal preferences, and intrinsic laws. Organizations would benefit from knowing further about customer needs and business desires by monitoring client behavior to provide more precise recommendations and boost acquisition rates. The economics of the customer, buyer groupings, and product quality are only a few of the numerous variables that influence customer behavior. The key issue that has to be resolved at this time is how to filter out useful information from these vast amounts of data to forecast customer behavior. For customer consumption behavior prediction and analysis with an advanced quantitative research process, we proposed the multiobjective evolutionary algorithm, which significantly boosts the accuracy of consumption behavior predictions. The dataset is initially gathered based on consumer preferences and behaviors as the essential information for the entire prediction model. Min-max normalization is used as a component of the preprocessing of the data to get the elimination of redundant and superfluous data. The Word2vec model is utilized for feature extraction, and boosted ant colony optimization (BACO) is employed to choose the best features. Utilizing the suggested multiobjective evolutionary algorithm (MOEA), the predictions are made. The suggested system’s performance is assessed, and the metrics are contrasted with more established methods. The findings demonstrate that the suggested MOEA technique performs well than the traditional ML, XGB, AI, and HNB algorithm methods in terms of accuracy (95 percent), quality of prediction (97 percent), precision (99 percent), recall (93 percent), F 1 -score (98 percent), and prediction time (50 seconds). Hence, the outcomes show that the regression model is sustainable. The suggested consumption behavior prediction system has demonstrated its efficiency in boosting profitability.