Customer Segmentation Techniques on E-Commerce

Koul, Sumit; Philip, Trissa Merrin

doi:10.1109/icacite51222.2021.9404659

Cited by 15 publications

(13 citation statements)

References 8 publications

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“…In this sample, a wide range of successful applications across industries can be found, specially in social media (Sinha et al, 2020) or e-commerce (Ballestar et al, 2018;Koul & Philip, 2021;Shen, 2021).…”

Section: Segmenting Online News Readersmentioning

confidence: 99%

“…The first referred to labeled data, the second one to unlabeled data (Gonçalves & Carvalho, 2021). One of the most popular and efficient clustering methods is the k‐means algorithm (Bock, 2007; Koul & Philip, 2021; MacQueen, 1967; Vergani & Binaghi, 2018) which has been evolving since the first approaches proposed in the fifties of the twentieth century (Bock, 2007; MacQueen, 1967). In the digital landscape, many applications have been published, for example, to segment B2C e‐commerce customers (Rajput & Singh, 2023) or online users (La Cruz et al, 2021).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…However, clustering techniques remain as an important and effective data mining solutions to identify users' clusters (Koul & Philip, 2021). Although consumer segmentations are popular across industries (Nasir et al, 2021;Vinothini & Priya, 2018), particularly in e-commerce (Koul & Philip, 2021;Punhani et al, 2021;Shen, 2021), or across B2C DSBM businesses, for example, online social games (Fu et al, 2017), movie industry (Tanuwijaya et al, 2021), education (Smit et al, 2019), and online news readers (La Torre, 2020), no study was found in the particular case of news publishers focused on engagement attributes. Thus, based on the premise that the best predictor of future consumer behavior is past consumer behavior (Kamthania et al, 2018), this research addresses the problem of identifying different reader profiles based on reader engagement attributes (Fernandes et al, 2023) and behavioral features (Shen, 2021) collected from an online newspaper.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A data‐driven approach to improve online consumer subscriptions by combining data visualization and machine learning methods

Fernandes,

Moro,

Cortez

2024

Int J Consumer Studies

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Effective online consumer research helps companies on defining a successful strategy to increase user loyalty and shape brand engagement. Digital innovation introduced a dramatic change in businesses, particularly in the online news industry. Content consumers have a wide offer across different channels which increase the digital challenge for online news media companies to retain their readers and convert them into online subscribers. Furthermore, digital news publishers often strive to balance revenue sources in online business models. Thus, this study fills a gap in the literature on media consumer research by proposing a data‐driven approach that combines two machine learning (ML) models to allow managers dynamically improve their marketing and editorial strategies. Firstly, the authors present an online user profiling to identify consumer segments based on the interplay between several engagement’ variables substantiated in the literature research. Second, as few studies have explored the factors influencing users' intention to pay for such services, the eXtreme Gradient Boosting ML algorithm identifies the predictors of consumer's willingness to pay. Third, a dashboard presents the key performance indicators across the audience funnel. Thus, practical implications and business suggestions are presented in a two‐fold strategy to maximize revenue from digital subscriptions and advertising. Findings provide new insights into an engagement approach and the relation to acquire a digital subscription in online content platforms. We believe that the provided recommendations are potentially useful to help marketing and editorial teams to manage their customer engagement process across the funnel in a more efficient way.

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Section: Segmenting Online News Readersmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A data‐driven approach to improve online consumer subscriptions by combining data visualization and machine learning methods

Fernandes,

Moro,

Cortez

2024

Int J Consumer Studies

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“…Banyak studi penelitian pemasaran mengenai perbedaan faktor pengambilan keputusan, frekuensi dan waktu tinggal berbelanja online yang dipengaruhi oleh perbedaan gender [3]. Segmentasi pelanggan ini memiliki peranan penting untuk mengidentifikasi kebutuhan pelanggan yang nantinya dijadikan rujukan untuk membuat keputusan strategi bisnis [4].…”

Section: Pendahuluanunclassified

Optimasi Decision Tree Dengan Particle Swarm Optimization Untuk Klasifikasi Prediksi Gender Pengguna E-Commerce

Irnawati

Nuraeni

2022

JIEET

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Belanja online telah menjadi gaya hidup saat ini. Hal ini berdampak pada banyaknya perusahaan aplikasi belanja online yang saling bersaing untuk menawarkan kemudahan dan kenyamanan pada pengguna aplikasi belanja online. Informasi mengenai pengguna akan sangat membantu pengusaha dibidang aplikasi belanja online sebagai pengetahuan untuk menerapkan strategi marketing guna terus mempertahankan pengguna aplikasi maupun merekrut pengguna aplikasi baru. Informasi tersebut termasuk mengenai gender pengguna yang dapat dimanfaatkan untuk teknik promosi, pemilihan brand ambasador, upgrade tampilan aplikasi dan lain sebagainya. Informasi mengenai gender pengguna aplikasi belanja online dapat di olah dengan menerapkan data mining menggunakan model klasifikasi dengan algoritma Decision Tree yang kemudian di optimasi dengan Particle Swarm Optimization (PSO). Hasil penelitian pada dataset e-commerce gender prediction menunjukkan adanya peningkatan akurasi Decision Tree yang dioptimasi Particle Swarm Optimization (PSO) sebesar 0,15%, tingkat akurasi yang sebelumnya sebesar 85.22% dengan angka kurva ROC 0.759 menjadi 85,37% dan kurva ROC 0.764 termasuk dalam fair classification.

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“…Las diferencias de comportamiento entre hombres, mujeres, adultos mayores y adolescentes son generalmente mostradas por la mayoría de las personas en esos grupos. Hay que tener cuidado con las variables originales puesto que, aun siendo significativas, no siempre lo son para todos los individuos del grupo (Gichuru & Limiri, 2017;Koul & Philip, 2021;Nandapala et al, 2020).…”

Section: Demográficosunclassified

Características de los emprendimientos de miel de abeja desde la perspectiva de los consumidores: Narihuala, Catacaos, Perú

2022

Savez Editorial

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Customer Segmentation Techniques on E-Commerce

Cited by 15 publications

References 8 publications

A data‐driven approach to improve online consumer subscriptions by combining data visualization and machine learning methods

A data‐driven approach to improve online consumer subscriptions by combining data visualization and machine learning methods

Optimasi Decision Tree Dengan Particle Swarm Optimization Untuk Klasifikasi Prediksi Gender Pengguna E-Commerce

Características de los emprendimientos de miel de abeja desde la perspectiva de los consumidores: Narihuala, Catacaos, Perú

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