Korean Customer Service Associate Assist System Based on Machine Learning

2019 ◽  
Author(s):  
Nayoung Yun ◽  
Hakjun Lee ◽  
Jiwon Moon ◽  
Ki-Baek Lee
Keyword(s):  
Machine Learning ◽  
Customer Service

scholarly journals Customer Service System Design Based on Big Data Machine Learning

2021 ◽  
Vol 2066 (1) ◽  
pp. 012017
Author(s):  
Yuqiang Kong ◽  
Yaoping He
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Customer Service ◽  
Service System ◽  
Rapid Development ◽  
Good Method ◽  
Service Systems ◽  
Labor Costs ◽  
High Turnover ◽  
Online Transactions

Abstract In recent years, with the rapid development of big data, traditional offline transactions have been moved to online in large numbers driven by the Internet. The virtual nature of online transactions has caused it to have problems such as difficulty in guaranteeing product quality and difficulty in user consultation. In addition, consumers are paying more and more attention to the quality of services, and the participation of customer service in the process of online transactions is very important. However, the current e-commerce market in our country is large and the number of online shopping users is extremely large. Customer service personnel are facing great work pressure. In addition, customer service has the characteristics of difficulty in recruiting, high labor costs, and high turnover rate. Such a dilemma is not conducive to our country. The sound development of e-commerce needs to be solved urgently. In order to solve these problems, it is a good method to apply related technologies to realize the automatic response of customer service. The purpose of this article is to design and research a customer service system based on big data machine learning. This article first through the understanding of the basic concepts of big data, and then extend the core technology of big data. Combining with the design ideas and concepts of contemporary customer service systems in our country, we will discuss the design and research of customer service systems based on big data machine learning. Research shows that traditional customer service in the era of big data can no longer meet people’s growing needs, and customer service systems based on big data machine learning are more efficient and convenient.

scholarly journals Prediction of Churn in Telecom Service: Exploring Call Behaviors and using Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 3831-3834
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Customer Service ◽  
Service Providers ◽  
Mobile Network ◽  
High Likelihood ◽  
Telecom Service ◽  
Number Of Customers ◽  
Mobile Network Operators

Churn has a significant impact on mobile network operators and telecommunications service providers. Many studies on churn have been reported, but no one can say that they can create universal human tools for predicting churn or that we can see all the reasons for it. The purpose of this study is to derive the call behavior factors of churning customers and to find ways to reduce the churn of target customers who exhibit these call behaviors. For this, this study uses decision tree and machine learning for the prediction of churn in telecom service. Based on the analysis results, first, the fact that the total number of customers who have more than 316.7 in churn shows that the higher the number of calls, the higher the chance of churn. Second, among customers with total day minutes above 316.7, those with customer service calls above 8.5 show a high likelihood of churn among complaining customers. The overall accuracy is 91.4%. Among the customers who predicted not to be churned, the accuracy that would not be churned was 92.87%, and the accuracy that was churned was 78.4% among the customers predicted to be churned

scholarly journals Indonesian online travel agent sentiment analysis using machine learning methods

2019 ◽  
Vol 14 (1) ◽  
pp. 113 ◽  
Author(s):  
Abimanyu Dharma Poernomo ◽  
Suharjito Suharjito
Keyword(s):  
Machine Learning ◽  
Customer Satisfaction ◽  
Customer Service ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Travel Agent ◽  
Sentiment Score ◽  
Online Travel

Many companies use social media to support their business activities. Three leading online travel agent such as Traveloka, Tiket.com, and Agoda use Facebook for supporting their business as customer service tool. This study is to measure customer satisfaction of Traveloka, Tiket.com, and Agoda by analyzing Facebook posts and comments data from their fan pages. That data will be analyzed with three machine learning algorithms such as K-Nearest Neighbors (KNN), Naïve Bayes, and Support Vector Machine (SVM) to determine the sentiment.  From the classification results, data will be selected with the highest f-score to be used to calculate the Net Sentiment Score used to measure customer satisfaction. The result shows that KNN result better than Naive Bayes and SVM based on f-score. Based on Net Sentiment Score shows companies that get the highest satisfaction value of Traveloka followed by Tiket.com and Agoda

scholarly journals PREDIKSI STATUS PENGIRIMAN BARANG MENGGUNAKAN METODE MACHINE LEARNING

2020 ◽  
Vol 6 (2) ◽  
pp. 100-109
Author(s):  
Hardian Kokoh Pambudi ◽  
Putu Giri Artha Kusuma ◽  
Femi Yulianti ◽  
Kevin Ahessa Julian
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Customer Service ◽  
Business Processes ◽  
Model Development ◽  
Service Level ◽  
Logistics Industry ◽  
Customer Service Level ◽  
Freight Forwarder ◽  
Artificial Neural Network Ann

One of the key performance indicators for the logistics industry, especially freight forwarder company (cargo), is the delivery time. This is still a challenge in this industry in terms of ensuring the customer service level and reducing transportation costs. On the other hand, the development of information technology now allows an organization or company to collect large amounts of data automatically. A decent method that can be used to analyze the data for prediction purposes is machine learning, which is a method of extracting data into a certain pattern of information. This research aims to apply three machine learning methods to estimate the status of shipping goods. The method used in this study follows the machine learning process published by the Cross Industry Standard Process for Data Mining (CRISP-DM), namely; business processes understanding, data understanding, data preparation, model development, evaluation, and implementation. Based on the results of the study, the random forest method produces better accuracy than the logistic regression and artificial neural network (ANN) methods, which is 76.6%, while the results of ANN and logistic regression methods are 73.81% and 72.84% respectively.  

scholarly journals Machine Learning Approaches for Auto Insurance Big Data

Risks ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 42 ◽  
Author(s):  
Mohamed Hanafy ◽  
Ruixing Ming
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Big Data ◽  
Random Forest ◽  
Decision Trees ◽  
Customer Service ◽  
Learning Approaches ◽  
Auto Insurance ◽  
New Methods ◽  
Better Than

The growing trend in the number and severity of auto insurance claims creates a need for new methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solves this problem. As car insurers aim to improve their customer service, these companies have started adopting and applying ML to enhance the interpretation and comprehension of their data for efficiency, thus improving their customer service through a better understanding of their needs. This study considers how automotive insurance providers incorporate machinery learning in their company, and explores how ML models can apply to insurance big data. We utilize various ML methods, such as logistic regression, XGBoost, random forest, decision trees, naïve Bayes, and K-NN, to predict claim occurrence. Furthermore, we evaluate and compare these models’ performances. The results showed that RF is better than other methods with the accuracy, kappa, and AUC values of 0.8677, 0.7117, and 0.840, respectively.

scholarly journals Exploring the Time-efficient Evolutionary-based Feature Selection Algorithms for Speech Data under Stressful Work Condition

2021 ◽  
Vol 9 (1) ◽  
pp. 60-74
Author(s):  
Derry Pramono Adi ◽  
Lukman Junaedi ◽  
Frismanda ◽  
Agustinus Bimo Gumelar ◽  
Andreas Agung Kristanto
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Customer Service ◽  
Harmony Search ◽  
Computation Time ◽  
Principal Component ◽  
Support Vector ◽  
Bee Colony ◽  
Speech Data ◽  
Selection Algorithms

Initially, the goal of Machine Learning (ML) advancements is faster computation time and lower computation resources, while the curse of dimensionality burdens both computation time and resource. This paper describes the benefits of the Feature Selection Algorithms (FSA) for speech data under workload stress. FSA contributes to reducing both data dimension and computation time and simultaneously retains the speech information. We chose to use the robust Evolutionary Algorithm, Harmony Search, Principal Component Analysis, Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, and Bee Colony Optimization, which are then to be evaluated using the hierarchical machine learning models. These FSAs are explored with the conversational workload stress data of a Customer Service hotline, which has daily complaints that trigger stress in speaking. Furthermore, we employed precisely 223 acoustic-based features. Using Random Forest, our evaluation result showed computation time had improved 3.6 faster than the original 223 features employed. Evaluation using Support Vector Machine beat the record with 0.001 seconds of computation time.

scholarly journals Achieving Resilience and Business Sustainability during COVID-19: The Role of Lean Supply Chain Practices and Digitalization

2021 ◽  
Vol 13 (22) ◽  
pp. 12369
Author(s):  
Matteo Trabucco ◽  
Pietro De Giovanni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Supply Chain ◽  
Customer Service ◽  
Mobile Apps ◽  
Production Costs ◽  
Sustainable Business ◽  
Business Sustainability ◽  
Logistic Regression Models

This paper investigates how firms can enjoy a sustainable business even during the COVID-19 pandemic. The adoption of lean coordination mechanisms over the supply chain (SC) and lean approaches in omnichannel strategies can guarantee the business sustainability and resilience. Furthermore, we investigate whether business sustainability, along with digitalization through mobile apps, Artificial Intelligence systems, and Big Data and Machine Learning enable firms’ resilience. We first explore the background on the subject, identify the research gap, and develop some research hypotheses to be tested. Then, we present the data collection process and the sample, which finally consists of firms from different sectors, including retailing, electronics, pharmaceutics, and agriculture. Several logistic regression models are developed and estimated to generate findings and managerial insights. Our results show that a lean omnichannel approach is an effective practice to preserve production costs, SC visibility, inventory available over the SC, and sales. Furthermore, lean coordination with contracts can make a business sustainable by preserving quality, ROI, production costs, customer service, and inventory availability. Finally, firms can be highly sustainable through resilience when they engage in sustainable ROI, SC visibility, and sales; in contrast, the adoption of mobile apps worsens firms’ resilience, which is not influenced by Artificial Intelligence and Big Data and Machine Learning.

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