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Naslov: Click-through rate estimation using CHAID classification tree model : case study of direct benefit transfer in India Avtorji: ID Gupta, Rajan (Avtor) ID Pal, Saibal K. (Avtor) Datoteke: https://doi.org/10.1007/978-981-13-1208-3_5   Jezik: Angleški jezik Vrsta gradiva: Neznano Tipologija: 1.08 - Objavljeni znanstveni prispevek na konferenci Organizacija: UNG - Univerza v Novi Gorici Opis: 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. Ključne besede: click-through rate, online advertisements, classification tree, mobile ads, click estimation Leto izida: 2019 Št. strani: Str. 45-58 PID: 20.500.12556/RUNG-6417  COBISS.SI-ID: 58221571  UDK: 004 DOI: 10.1007/978-981-13-1208-3_5  NUK URN: URN:SI:UNG:REP:CSJA8BFV Datum objave v RUNG: 02.04.2021 Število ogledov: 2374 Število prenosov: 15 Metapodatki:    Citiraj gradivo Navadno besedilo BibTeX EndNote XML EndNote/Refer RIS ABNT ACM Ref AMA APA Chicago 17th Author-Date Harvard IEEE ISO 690 MLA Vancouver : Kopiraj citat

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Gradivo je del monografije

Naslov:	Advances in analytics and applications
Uredniki:	Arnab Kumar Laha
Kraj izida:	Singapore
Založnik:	Springer Nature
ISBN:	978-981-13-1208-3
COBISS.SI-ID:	58217219
Naslov zbirke:	Springer proceedings in business and economics (Online)
ISSN zbirke:	2198-7254

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