Review on customer segmentation technique on ecommerce

JN Sari, LE Nugroho, R Ferdiana… - Advanced Science …, 2016 - ingentaconnect.com
Ecommerce transactions are no longer a new thing. Many people shop with ecommerce and
many companies use ecommerce to promote and to sell their products. Because of that …

[PDF][PDF] Clustering optimization in RFM analysis based on k-means

R Gustriansyah, N Suhandi, F Antony - Indonesian Journal of …, 2020 - academia.edu
RFM stands for Recency, Frequency, and Monetary. RFM is a simple but effective method
that can be applied to market segmentation. RFM analysis is used to analyze customer's …

Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis

A Dursun, M Caber - Tourism management perspectives, 2016 - Elsevier
This study focuses on profiling profitable hotel customers by RFM analysis, which is a data
mining technique. In RFM analysis, Recency, Frequency and Monetary indicators are …

The impact of big data market segmentation using data mining and clustering techniques

F Yoseph, NH Ahamed Hassain Malim… - Journal of Intelligent …, 2020 - content.iospress.com
Targeted marketing strategy is a prominent topic that has received substantial attention from
both industries and academia. Market segmentation is a widely used approach in …

[PDF][PDF] Clustering prediction techniques in defining and predicting customers defection: The case of e-commerce context

AD Rachid, A Abdellah, B Belaid… - … Journal of Electrical …, 2018 - pdfs.semanticscholar.org
With the growth of the e-commerce sector, customers have more choices, a fact which
encourages them to divide their purchases amongst several ecommerce sites and compare …

Modified dynamic fuzzy c-means clustering algorithm–Application in dynamic customer segmentation

S Munusamy, P Murugesan - Applied Intelligence, 2020 - Springer
The dynamic customer segmentation (DCS) is a useful tool for managers in implementing
marketing strategies by observing dynamic changes that are happening in the customer …

Big data and the danger of being precisely inaccurate

DA McFarland, HR McFarland - Big Data & Society, 2015 - journals.sagepub.com
Social scientists and data analysts are increasingly making use of Big Data in their analyses.
These data sets are often “found data” arising from purely observational sources rather than …

Improved customer lifetime value prediction with sequence-to-sequence learning and feature-based models

J Bauer, D Jannach - ACM Transactions on Knowledge Discovery from …, 2021 - dl.acm.org
The prediction of the Customer Lifetime Value (CLV) is an important asset for tool-supported
marketing by customer relationship managers. Since standard methods based on purchase …

Analisis segmentasi pelanggan menggunakan kombinasi RFM model dan teknik clustering

BE Adiana, I Soesanti… - Jurnal Terapan Teknologi …, 2018 - jutei.ukdw.ac.id
Intisari–Persaingan yang ketat di bidang bisnis memotivasi sebuah usaha kecil dan
menengah (UKM) untuk mengelola pelayanan terhadap konsumen tetap (pelanggan) …

A multi-attribute data mining model for rule extraction and service operations benchmarking

H Amoozad Mahdiraji, M Tavana… - Benchmarking: an …, 2022 - emerald.com
Purpose Customer differences and similarities play a crucial role in service operations, and
service industries need to develop various strategies for different customer types. This study …