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Title: Click-through rate estimation using CHAID classification tree model : case study of direct benefit transfer in India Authors: Gupta, Rajan (Author) Pal, Saibal K. (Author) Files:  https://doi.org/10.1007/978-981-13-1208-3_5   Language: English Work type: Unknown () Tipology: 1.08 - Published Scientific Conference Contribution Organization: UNG - University of Nova Gorica Abstract: Click-Through Rate (CTR) is referred to as the number of clicks on a particular advertisement as compared to the number of impressions on it. It is an important measure to find the effectiveness of any online advertising campaign. The effectiveness of online advertisements through calculations of ROI can be done through the measurement of CTR. There are multiple ways of detecting CTR in past; however, this study focuses on machine learning based classification model. Important parameters are judged on the basis of user behavior toward online ads and CHAID tree model is used to classify the pattern for successful and unsuccessful clicks. The model is implemented using SPSS version 21.0. The dataset used for the testing has been taken from Kaggle website as the data is from anonymous company’s ad campaign given to Kaggle for research purpose. A total of 83.8% accuracy is reported for the classification model used. This implies that CHAID can be used for less critical problems where very high stakes are not involved. This study is useful for online marketers and analytics professionals for assessing the CHAID model’s performance in online advertising world. Keywords: click-through rate, online advertisements, classification tree, mobile ads, click estimation Year of publishing: 2019 Number of pages: Str. 45-58 COBISS_ID: 58221571  UDC: 004 URN: URN:SI:UNG:REP:CSJA8BFV DOI: 10.1007/978-981-13-1208-3_5  Views: 1011 Downloads: 8 Metadata:     Categories: Document is not linked to any category. Cite this work Plain text BibTeX EndNote XML EndNote/Refer RIS ABNT ACM Ref AMA APA Chicago Harvard IEEE ISO 690 MLA Vancouver :

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Record is a part of a monograph

Title:	Advances in analytics and applications
Publisher:	Springer Nature
ISBN:	978-981-13-1208-3
COBISS.SI-ID:	58217219
Collection title:	Springer proceedings in business and economics (Online)
Collection ISSN:	2198-7254
Place of publishing:	Singapore
Editors:	Arnab Kumar Laha

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