Customer Management

2018 ◽  
pp. 3-40
Author(s):  
Sarah Critchley
Keyword(s):  
Customer Management

Analysis of the Importance Degree and Performance of Internet Service Providers in Makassar City

2019 ◽  
Vol 10 (2) ◽  
pp. 168-177
Author(s):  
Haerdiansyah Syahnur ◽  
Jafar Basalamah
Keyword(s):  
Customer Satisfaction ◽  
Gap Analysis ◽  
Service Providers ◽  
Customer Management ◽  
Quadrant Analysis ◽  
Internet Service Providers ◽  
Internet Service ◽  
Actual Performance ◽  
Satisfaction Index ◽  
Customer Satisfaction Index

This study aimed to analyze the customer experience seen from the level of actual performance and the level of importance of services provided by internet service providers PT. XYZ in Makassar City. Variables and attributes issued by TM Forum GB 912 consisting of Customer Management, Fulfillment, Assurance, and Billing, are used to analyze the performance provided by customer service in the field. The analysis technique will be carried out using the Importance Performance Analysis and Customer Satisfaction Index consisting of quadrant analysis and gap analysis used to investigate customer satisfaction and identify variables whose performance is deemed to need improvement. Data were collected using a platform-based questionnaire application from 100 respondents selected using random sampling techniques. The results showed that customers were satisfied with the performance and quality of services provided. The customer satisfaction index value obtained by CSI analysis shows a value of 82.006%. In conclusion, that the Fulfillment variable is a service variable that is considered the most important customer and requires improvement because its performance is still relatively low. While the variables considered good and need to be maintained are the Billing variable. Other service variables are sorted based on priority of improvement in a row, namely Fulfillment, Customer Management, and Assurance.

Development of an integrated performance measurement framework for lean organizations

2018 ◽  
Vol 29 (1) ◽  
pp. 41-84 ◽  
Author(s):  
Narpat Ram Sangwa ◽  
Kuldip Singh Sangwan
Keyword(s):  
Performance Measurement ◽  
Performance Indicators ◽  
Manufacturing Process ◽  
Key Performance Indicators ◽  
Supplier Management ◽  
The Body ◽  
Customer Management ◽  
Content Type ◽  
Measurement Framework ◽  
Integrated Performance

Purpose The purpose of this paper is to propose an integrated performance measurement framework to measure the effect of lean implementation throughout all functions of an organization. Design/methodology/approach The paper identifies the seven categories representing all organizational functions. These categories have been divided into 26 performance dimensions and key performance indicators (KPIs) for each performance dimension have been identified to measure lean performance. The interrelationship of each category with lean principles and/or lean wastes has been identified. KPIs are developed on the basis of identified criteria, frequency analysis of existing literature, and discussion with industry professionals. Finally, an integrated performance measurement framework is proposed. Findings The proposed framework evaluates the organization under seven categories – manufacturing process, new product development (NPD), human resource management, finance, administration, customer management, and supplier management. In total, 26 dimensions and 119 key performance indicators have been identified under the seven categories. Research limitations/implications The proposed framework is a conceptual framework and it is to be tested by empirical and cross-sectional studies. Originality/value The main novelty of the research is that the leanness of the organization has been measured throughout the supply chain of the organization in an integrated way. The various areas of measurement are manufacturing process, NPD, finance, administration, customer management, and supplier management. Further, the proposed KPIs are also categorized as qualitative or quantitative, strategic or operational, social or technical, financial or non-financial, leading or lagging, static or dynamic. This paper contributes to the body of knowledge in performance measurement.

scholarly journals The role of customer management capabilities in public–private partnerships

2016 ◽  
Vol 25 (5-6) ◽  
pp. 384-404 ◽  
Author(s):  
Ana Isabel Canhoto ◽  
Maureen Meadows ◽  
Kirstie Ball ◽  
Elizabeth Daniel ◽  
Sally Dibb ◽  
...  
Keyword(s):  
Customer Management ◽  
Public Private Partnerships ◽  
Management Capabilities

scholarly journals Design and Implementation of Customer Management System Using UML

2007 ◽  
Vol 7 (11) ◽  
pp. 59-68
Author(s):  
Won-Gyo Jung ◽  
Sang-Sung Park ◽  
Young-Geun Shin ◽  
Myoung-Hoon Kim ◽  
Dong-Sik Jang
Keyword(s):  
Management System ◽  
Customer Management ◽  
Design And Implementation ◽  
Customer Management System

scholarly journals SISTEM APLIKASI BERBASIS OPTIMASI METODE ELBOW UNTUK PENENTUAN CLUSTERING PELANGGAN

JOUTICA ◽  
2018 ◽  
Vol 3 (1) ◽  
pp. 117 ◽  
Author(s):  
Elly Muningsih ◽  
Sri Kiswati
Keyword(s):  
Data Center ◽  
Clustering Algorithm ◽  
Visual Basic ◽  
Customer Management ◽  
Number Of Clusters ◽  
Transaction Data ◽  
Development Method ◽  
Popular Method ◽  
Or Groups ◽  
Cluster 2

Customer is a very important asset for the company. Having customers who are loyal to the company is an absolute and important for the progress of the company. This study aims to help companies, especially in the online shop to create a better customer management by identifying and grouping customers into several clusters or groups to know the characteristics of their loyalty to the company. The method used in this research is K-Means method which is one of the best and most popular method in clustering algorithm. To overcome the weakness of the K-Means method in determining the number of clusters, we use the Elbow method where this method gets the comparison of the number of clusters added by calculating the SSE (Sum of Square Error) of each cluster value. This research starts from collecting the necessary data and will be processed. From total transaction data 478 then done cleaning of data and result 73 data. Then the data processed with RapidMiner software from Cluster 2 up to 10 to search the data center of each cluster. From the calculated SSE value found that the best number of clusters is 3. The end result of the research is a Visual Basic based application program that is expected to provide ease in grouping or clustering customers. Software development method using Waterfall method.

scholarly journals The Effect of Hairdresser’ Loneliness on Work Immersion and Depression

2021 ◽  
Vol 27 (5) ◽  
pp. 1086-1094
Author(s):  
Eun-Su An ◽  
Yong-Mi Jin
Keyword(s):  
Factor Analysis ◽  
Regression Analysis ◽  
Reliability Analysis ◽  
Frequency Analysis ◽  
Customer Management ◽  
Free Communication ◽  
Personal Time ◽  
Efficient Management ◽  
Rest Time ◽  
The Relationship

Research on the stress and loneliness felt by Hairdresser, their work immersion, and their effects on depression is as important as research on customer management and is essential for the efficient management of the beauty industry. Therefore, frequency analysis, factor analysis, reliability analysis, and regression analysis were conducted to find out the relationship between work immersion and depression of beauty workers, and a total of 295 copies were used.The results of the study are as follows.First, as loneliness increases, work immersion decreases. Second, as loneliness increases, depression increases. In order to reduce loneliness, personal time should be secured through free communication and troubleshooting through Sns and beauty communities, rest time, holidays and monthly leave guarantees, and if these improve, work immersion will increase and depression will decrease.It is expected that subsequent research will continue with various variables through age diversification and segmentation of majors in the future.

Detecting sarcasm in customer tweets: an NLP based approach

2017 ◽  
Vol 117 (6) ◽  
pp. 1109-1126 ◽  
Author(s):  
Shubhadeep Mukherjee ◽  
Pradip Kumar Bala
Keyword(s):  
Language Processing ◽  
Social Issues ◽  
Customer Management ◽  
Function Words ◽  
Content Type ◽  
Management Perspective ◽  
Supervised Learning Algorithms ◽  
Practical Implications ◽  
Incorrect Classification

Purpose The purpose of this paper is to study sarcasm in online text – specifically on twitter – to better understand customer opinions about social issues, products, services, etc. This can be immensely helpful in reducing incorrect classification of consumer sentiment toward issues, products and services. Design/methodology/approach In this study, 5,000 tweets were downloaded and analyzed. Relevant features were extracted and supervised learning algorithms were applied to identify the best differentiating features between a sarcastic and non-sarcastic sentence. Findings The results using two different classification algorithms, namely, Naïve Bayes and maximum entropy show that function words and content words together are most effective in identifying sarcasm in tweets. The most differentiating features between a sarcastic and a non-sarcastic tweet were identified. Practical implications Understanding the use of sarcasm in tweets let companies do better sentiment analysis and product recommendations for users. This could help businesses attract new customers and retain the old ones resulting in better customer management. Originality/value This paper uses novel features to identify sarcasm in online text which is one of the most challenging problems in natural language processing. To the authors’ knowledge, this is the first study on sarcasm detection from a customer management perspective.

Sign in / Sign up

Export Citation Format

Share Document