• Asia pacific journal of information systems
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• v.29 no.4
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• pp.752-770
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• 2019

The purpose of this study is to incorporate telemarketing processes to improve telemarketing performance. For this application, we have attempted to mix the model of machine learning to extract potential customers with personalisation techniques to derive recommended products from actual contact. Most of traditional recommendation systems were mainly in ways such as collaborative filtering, which predicts items with a high likelihood of future purchase, based on existing purchase transactions or preferences for products. But, under these systems, new users or items added to the system do not have sufficient information, and generally cause problems such as a cold start that can not obtain satisfactory recommendation items. Also, indiscriminate telemarketing attempts can backfire as they increase the dissatisfaction and fatigue of customers who do not want to be contacted. To this purpose, this study presented a multi-purpose hybrid recommendation algorithm to achieve two goals: to select customers with high possibility of contact, and to recommend products to selected customers. In addition, we used subscription data from telemarketing agency that handles insurance products to derive realistic applicability of the proposed recommendation system. Our proposed recommendation system would certainly solve the cold start and scarcity problem of existing recommendation algorithm by using contents information such as customer master information and telemarketing history. Also. the model could show excellent performance not only in terms of overall performance but also in terms of the recommendation success rate of the unpopular product.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.197-206
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• 2019

Telemarketing has been used in finance due to the reduction of offline channels. In order to select telemarketing target customers, various machine learning techniques have emerged to maximize the effect of minimum cost. However, there are problems that the class imbalance, which the number of marketing success customers is smaller than the number of failed customers, and the recall rate is lower than accuracy. In this paper, we propose a method that solve the imbalanced class problem and increase the recall rate to improve the efficiency. The hybrid sampling method is applied to balance the data in the class, and the stacked deep network is applied to improve the recall and precision as well as the accuracy. The proposed method is applied to actual bank telemarketing data. As a result of the comparison experiment, the accuracy, the recall, and the precision is improved higher than that of the conventional methods.

• Journal of KIISE
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• v.44 no.7
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• pp.686-691
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• 2017

Telemarketing has become the center of marketing action of the industry in the information society. Recently, machine learning has emerged in many areas, especially, financial prediction. Financial data consists of lots of unlabeled data in most parts, and therefore, it is difficult for humans to perform their labeling. In this paper, we propose a fusion method of semi-supervised learning for automatic labeling of unlabeled data to predict telemarketing. Specifically, we integrate labeling results of label propagation and co-training with a decision tree. The data with lower reliabilities are removed, and the data are extracted that have consistent label from two labeling methods. After adding them to the training set, a decision tree is learned with all of them. To confirm the usefulness of the proposed method, we conduct the experiments with a real telemarketing dataset in a Portugal bank. Accuracy of the proposed method is 83.39%, which is 1.82% higher than that of the conventional method, and precision of the proposed method is 19.37%, which is 2.67% higher than that of the conventional method. As a result, we have shown that the proposed method has a better performance as assessed by the t-test.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.877-891
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• 2008

The purpose in this datamining modeling is to maximize the number of L insurance' new customer selected from the S corp.'s customers through the telemarketing. We demonstrated the superiority of this method by comparing the existing marketing method and campaign result. The used software in this analysis is SAS 9.1 and so on.

• Proceedings of the Safety Management and Science Conference
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• 2007.11a
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• pp.463-467
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• 2007

This paper deals with service quality systems such as nonstore selling service, telemarketing services, call center services, market and opinion research service, franchise service, and, institutional feeding service. These service quality systems include process, infrastructure, and, requirements for the training and development of agents and staffs.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.673-684
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• 2009

Lower Prices are offered through sales by telemarketing. This is to serve our customers by the fastest and most appropriate referral product that is most important to attract insurance. Therefore, Considering the time the customer's preferred products and preferred customer for screening and targeting, depending on what is the difference between the premiums. This study of the logistic regression model using datamining techniques, the life insurance companies in outbound telemarketing to support sales of the effect you want to validate. To join existing life insurance companies for the customer response and sales strategy based on the L segment and by age group, family-love insurance, accident insurance, and cancer insurance were in progress for the modeling. Set model based on the progress of the campaign to existing customers marketing methods and how to extract and run the model results has proven the superiority of the model. In addition, over time, depending on the aging model is set to a decline in operating profit to maximize the profits th update the model which was derived.

• Journal of Service Research and Studies
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• v.12 no.3
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• pp.45-59
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• 2022

This study attempted to investigate how the transformational leadership perceived by flowers of telemarketing organizations. In particular, this study explored the moderating effect of followership in the relationship between transformational leadership and organizational performance. To verify the research model, 321 surveys collected from 10 domestic call centers were analyzed. Reliability, correlation, factor analysis, and hierarchical regression analysis were performed, and as a result of the analysis, it was found that the transformational leadership in the telemarketing organizations had a positive effect on organizational performance. In addition, it was confirmed that goal consistency, proactive participation, critical thinking, and team spirit, which are the four sub-variables of followership, all have a positive (+) effect on organizational performance, and both goal consistency and proactive participation moderate the relationship between transformational leadership and organizational performance. Considering the special working environment of telemarketing organizations, the results of this study suggest the need for a corporate educational role to increase transformational leadership and followership and the need to build environments by setting up an atmosphere for members to exercise follow-up, and opening up communication structures for members to follow.

• Proceedings of the Costume Culture Conference
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• 1999.04a
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• pp.3-16
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• 1999

• The Journal of Information Technology and Database
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• v.6 no.1
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• pp.89-101
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• 1999

Increasing number of companies recognize that the understanding of customers and their markets is indispensable for their survival and business success. The companies are rapidly increasing the amount of investments to develop customer databases which is the basis for the database marketing activities. Database marketing is closely related to data mining. Data mining is the non-trivial extraction of implicit, previously unknown and potentially useful knowledge or patterns from large data. Data mining applied to database marketing can make a great contribution to reinforce the company's competitiveness and sustainable competitive advantages. This paper develops the classification model to select the most responsible customers from the customer databases for telemarketing system and evaluates the performance of the developed model using LIFT measure. The model employs the decision tree algorithm, i.e., CHAID which is one of the well-known data mining techniques. This paper also represents the effective database marketing strategy by applying the data mining technique to a credit card company's telemarketing system.

• Culinary science and hospitality research
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• v.2
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• pp.25-46
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• 1996

The clients of hotel desire to be recognized as special persons who have their own personality and self-consciousness. The other hand, The hotel concentrates upon marketing strategy which acts a part of expanding sales. Namely, They find out the need of client, satisfy the desired need, ensure and manage the existing regular customers and make new clients regular customers. So they gain an advantage over competitive hotels. They make strategics of Telemarketing technique which is one of the developed-marketing techniques, can provide distinctive and familiar service and have an hight effect at the low cost. The attractions of Telemarketing strategy can be explained as follows. 1. It is the medium that have cost efficiency. 2. It possesses attractions to do time management. 3. It has control cash flow. 4. It can improve cash flow. 5. Market expansion can be accomplished. 6. It is a medium fit for prospecting over long-term period. 7. It can improve customer relations. 8. It fits seasonality (seasonal sales) 9. It brings greater order volume. 10. It benefits from lower personnel costs. 11. High volume productivity is realized. 12. It has attraction of versatility. 13. It has ease of start-up.