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Aesthetic medicine is a rapidly evolving field integrating knowledge from various medical specialties to enhance physical appearance, boost self-esteem, and improve quality of life. This study investigates patient satisfaction with innovative aesthetic medicine services in Poland, focusing on determinants influencing the selection and effectiveness of treatments. Utilizing data from 745 patients across large clinics and in-depth interviews with 20 physicians, the research evaluates popular procedures, post-treatment outcomes, and consumer loyalty using the Net Promoter Score (NPS) technique. Results indicate high satisfaction levels, with botulinum toxin type A and hyaluronic acid injections being the most favored treatments. Patients highlighted factors such as professionalism, technological innovation, and clinic environment as critical to their positive experiences. The findings underscore the importance of aligning service offerings with consumer expectations and leveraging advanced technologies to drive growth in the aesthetic medicine sector. Future research aims to expand the analysis to international markets, further exploring satisfaction determinants across diverse cultural and economic contexts.

Net Promoter Score (NPS) has been widely adopted by managers as a measure of customer mindset and predictor of sales growth. Over time, practitioners have evolved the use of NPS from its original purpose as a transaction-based customer loyalty metric, towards a metric for tracking overall brand health which includes responses from non-customers. Despite enduring managerial popularity, academics remain skeptical of NPS, citing methodological issues and ongoing concerns with NPS measurement. This study re-visits the use of NPS as a predictor of sales growth by analyzing data from seven brands operating in the U.S. sportswear industry, measured over five years. Our results confirm—within the context of our study—that while the original premise of NPS is reasonable, the methodological concerns raised by academics are valid, and only the more recently developed brand health measure of NPS (using an all potential customer sample) is effective at predicting future sales growth.

The current paper presents a novel method for reducing a multiclass confusion matrix into a 2×2 version enabling the exploitation of the relevant performance metrics and methods such as the receiver operating characteristic and area under the curve for the assessment of different classification algorithms. The reduction method is based on class grouping and leads to a special type of matrix called the reduced confusion matrix. The developed method is then exploited for the assessment of state of the art machine learning algorithms applied on the net promoter score classification problem in the field of customer experience analytics indicating the value of the proposed method in real world classification problems.

Background Patient experience is a complex phenomenon that presents challenges for appropriate and effective measurement. With the lack of a standardized measurement approach, efforts have been made to simplify the evaluation and reporting of patient experience by using single‐item measures, such as the Net Promoter Score (NPS). Although NPS is widely used in many countries, there has been little research to validate its effectiveness and value in the healthcare setting. The aim of this study was to systematically evaluate the evidence that is available about the application of NPS in healthcare settings. Methods Studies were identified using words and synonyms that relate to NPS, which was applied to five electronic databases: Medline, CINAHL, Proquest, Business Journal Premium, and Scopus. Titles and s between January 2005 and September 2020 were screened for relevance, with the inclusion of quantitative and qualitative studies in the healthcare setting that evaluated the use of NPS to measure patient experience. Results Twelve studies met the inclusion criteria. Four studies identified benefits associated with using NPS, such as ease of use, high completion rates and being well‐understood by a range of patients. Three studies questioned the usefulness of the NPS recommendation question in healthcare settings, particularly when respondents are unable to select their service provider. The free‐text comments section, which provides additional detail and contextual cues, was viewed positively by patients and staff in 4 of 12 studies. According to these studies, NPS can be influenced by a wide range of variables, such as age, condition/disease, intervention and cultural variation; therefore, caution should be taken when using NPS for comparisons. Four studies concluded that NPS adds minimal value to healthcare improvement. Conclusion The literature suggests that many of the proposed benefits of using NPS are not supported by research. NPS may not be sufficient as a stand‐alone metric and may be better used in conjunction with a larger survey. NPS may be more suited for use in certain healthcare settings, for example, where patients have a choice of provider. Staff attitudes towards the use of NPS for patient surveying are mixed. More research is needed to validate the use of NPS as a primary metric of patient experience. Patient or Public Contribution Consumer representatives were provided with the research findings and their feedback was sought about the study. Consumers commented that they found the results to be useful and felt that this study highlighted important considerations when NPS data is used to evaluate patient experience.

The NPS index is used in the hotel industry to measure customer loyalty and, by extension, customer satisfaction. Many hotel companies set their annual budget based on this index and include it, together with annual economic results, for evaluation when deciding on a potential management bonus. For managers in some companies, achieving a high NPS becomes nearly as important as achieving strong economic results. The purpose of this research is to deepen the study of the NPS index by analysing the existing relationship that the model has with customer satisfaction, focusing on the following main areas of a hotel: reception, cleanliness and room comfort, and gastronomy. To do so, this study uses fuzzy set qualitative comparative analysis (fsQCA). New evidence of value is offered based on the analysis of a sample of six hotels (4 and 5*) located in the Balearic Islands, Spain (Mallorca, Minorca, and Ibiza). In total, 557 surveys were completed in August 2021 and 571 surveys were completed in August 2020, and therefore both sample groups were impacted by a Black Swan (BS) event, the COVID-19 pandemic, in two different stages of its trajectory. The results suggest that in the study sample, the key factor in achieving a high NPS was (1) gastronomy in 2021 (after more than one year of the COVID-19 pandemic), and (2) cleanliness and room comfort in 2020 (at the beginning of the COVID-19 pandemic). These results offer insights for hotel managers, as well as for academics who can develop new lines of research on the subject.

Problematic consumption of digital experiences poses rising risks to consumer well-being, yet early detection remains challenging. This article examines whether data routinely collected in digital products can reveal indicators of problematic use. Drawing on dual-process theory, research on compulsive consumption, and clinical diagnostic criteria, the study proposes that Net Promoter Score (NPS) inversion—defined as negative evaluation of a product concurrent with sustained or increased consumption—may signal dysregulated use. The prevalence of this pattern is assessed using data from a free-to-play mobile game with loot boxes, indicating that a sizable proportion of users exhibit NPS inversion. These users report disliking the game yet continue playing, possibly experiencing problems with self-control and compulsion. The study discusses implications of this finding for research and emphasizes applications of such data in in-market systems for early detection and prevention.

Purpose How can customer service be so bad in an era when companies collect endless data on customer interactions? The purpose of this paper is to contribute to the important challenge of elevating customer service delivery by providing guidelines for when and how to select optimal measures of customer service measurement using a new decision framework. Design/methodology/approach The paper uses a comprehensive, multi-dimensional review of extant literature related to customer service, journey mapping and performance measurement and applied a qualitative, taxonomic approach for model development. Findings A process model and customer journey mapping framework can facilitate the selection and application of appropriate and relevant customer service experience metrics to enhance customer service experience strategies, creation and delivery. Research limitations/implications The taxonomy of customer service metrics is limited to current publicly and commercially available metrics. The dynamic nature of the customer service environment necessitates continuous updates of the model and framework. Practical implications Selection of customer service performance measures should match relevant stages of the customer journey; use perception-based, operational and outcome-based metrics that track employee and customer behaviours; improve omni-channel measurement; and integrate data-sharing and benchmark measurement initiatives through collaboration with customer service communities. Originality/value A reimagined perspective is offered to the complex challenge of measuring and improving customer service, providing a new decision-making framework for customer service experience measurement and guidance for future research.

Purpose This study aims to develop a customer-centric model based on an online customer experience (OCE) construct relating to e-loyalty, e-trust and e-satisfaction, resulting in improved Net Promoter Score for Indian digital banks. Design/methodology/approach This study used an online survey method to gather data from a sample of 485 digital banking users, from which usable questionnaires were obtained. The obtained data were subjected to thorough analysis using partial least squares structural equation modelling to further investigate the research hypotheses. Findings The main factors determining digital banks’ OCE were perceived customer centrality, perceived value and perceived usability. Additionally, relevant constructs were evaluated using importance-performance map analysis. Research limitations/implications This study used convenience sampling for the urban population using digital banking services; therefore, the outcome may be generalized to a limited extent. To further strengthen digital banking, it would be valuable to imitate studies in other countries. Originality/value There is a lack of research on digital banking and OCE in India; thus, this study will help rectify this issue while providing valuable insights. This study differs from others in that it examines the connections between online customer satisfaction, loyalty, trust and the bottom line of financial institutions using these factors as dependent variables instead of traditional measures.

In the age of digital media and Artificial Intelligence, User-Generated Content (UGC) has emerged as a valuable yet underexplored source for understanding consumer perceptions of brands. While earlier research has focused mainly on UGC's role in engagement and satisfaction, limited attention has been given to how textual UGC can be systematically utilised to measure Customer- Based Brand Equity (CBBE). Addressing this gap, the present study employs sentiment analysis and customer satisfaction metrics to evaluate brand equity using consumer reviews of budget smartphones available on Amazon.in. Using advanced text analytics through Azure Machine Learning, this research investigates how consumer sentiments expressed in online reviews relate to key dimensions of brand equity- brand loyalty and brand advocacy, which in turn strengthen overall brand equity. However, this study also indicates that positive sentiment alone does not always translate into long-term retention, highlighting the importance of building deeper emotional connections in the budget smartphone category. This research contributes to the theoretical understanding of brand equity measurement in digital contexts by integrating UGC-derived sentiment with broader customer satisfaction and retention metrics. For practitioners, the findings offer actionable insights for brand managers in the smartphone industry, demonstrating how real-time consumer feedback can be leveraged to monitor customer satisfaction, reduce churn, and enhance consumer brand relationships.

This study proposes a predictive framework that integrates machine learning techniques with Transactional Net Promoter Score (tNPS) data to enhance sustainable Human Resource management. A synthetically generated dataset, simulating real-world employee feedback across divisions and departments, was used to classify employee performance and engagement levels. Six machine learning models such as XGBoost, TabNet, Random Forest, Support Vector Machines, K-Nearest Neighbors, and Neural Architecture Search were applied to predict high-performing and at-risk employees. XGBoost achieved the highest accuracy and robustness across key performance metrics, including precision, recall, and F1-score. The findings demonstrate the potential of combining real-time sentiment data with predictive analytics to support proactive HR strategies. By enabling early intervention, data-driven workforce planning, and continuous performance monitoring, the proposed framework contributes to long-term employee satisfaction, talent retention, and organizational resilience, aligning with sustainable development goals in human capital management.