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Telemarketing outcome prediction using an ensemble-based machine learning technique

conference contribution
posted on 26.07.2021, 00:56 by Shahriar Kaisar, MD Mamunur RashidMD Mamunur Rashid
Business organisations often use telemarketing, which is a form of direct marketing strategy to reach a wide range of customers within a short time. However, such marketing strategies need to target an appropriate subset of customers to offer them products/services instead of contacting everyone as people often get annoyed and disengaged when they receive pre-emptive communication. Machine learning techniques can aid in this scenario to select customers who are likely to positively respond to a telemarketing campaign. Business organisations can use their CRM-based customer information and embed machine learning techniques in the data analysis process to develop an automated decisionmaking system, which can recommend the set of customers to be communicated. A few works in the literature have used machine learning techniques to predict the outcome of telemarketing, however, the majority of them used a single classifier algorithm or used only a balanced dataset. To address this issue, this article proposes an ensemble-based machine learning technique to predict the outcome of telemarking, which works well even with an imbalanced dataset and achieves 90.29% accuracy.

History

Start Page

1

End Page

11

Number of Pages

11

Start Date

01/12/2020

Finish Date

04/12/2020

Location

Wellington, New Zealand

Publisher

AIS

Place of Publication

Online

Additional Rights

CC-BY-NC 3.0 NZ

Peer Reviewed

Yes

Open Access

Yes

Era Eligible

Yes

Name of Conference

Australasian Conference on Information Systems

Parent Title

ACIS 2020 Proceedings