Results 61 to 70 of about 192 (158)

Review and Analysis of Selected Customer Value Measurement Methods

Studia i Materiały, 2020
The methods of measuring customer value are of constant interest. This area is widely described in the literature, but nevertheless somewhat generally.
Maria Kubacka
doaj   +1 more source

Analysis and Optimization of Customer Lifetime Value Prediction using Machine Learning and Deep Learning Models by RFM Techniques [PDF]

International Journal of Web Research
In today’s data-driven hospitality sector, customer interactions increasingly occur through digital platforms, generating extensive behavioral and transactional information. This study analyse the prediction of Customer Lifetime Value (CLV) using machine
Leila Taherkhani, Amir Daneshvar, Hossein Amoozad Khalili, MohammadReza Sanaei   +3 more
doaj   +1 more source

Customer Clustering Based on Customer Lifetime Value: A Case Study of an Iranian Bank

International Journal of Information and Communication Technology Research, 2015
Customer lifetime value (CLV) as a quantifiable parameter plays an important role in customer clustering. Clustering based on CLV helps organizations to form distinct customer groups, reveal buying patterns, and create longterm relationships with their ...
Arezoo Nekooei, Mohammad Jafar Tarokh
doaj  

Inclusive

Critical Quarterly, Volume 66, Issue 3, Page 95-100, October 2024.
Kathryn Allan
wiley   +1 more source

User Behavior Sequence Mining via Graph Neural Networks for Customer Lifetime Value Prediction and Precision Marketing Strategy Formulation

Journal of Applied Science and Engineering
Customer Lifetime Value (CLV) prediction is a core task in retail and e-commerce, enabling enterprises to optimize resource allocation and formulate precision marketing strategies.
Yalin Pang
doaj   +1 more source

A Customer Lifetime Value-aware Framework for Strategic Churn Prediction Using Deep Learning

Advanced Engineering Research
Introduction. Customer churn prediction represents a challenge in the current era of rapid digital transformation, hyper-competition, and data-driven marketing.
Gurusamy Uma Maheswari, Anantharaman Meenakshi, Selvamani Ram Prasath, Vijayarajan Sangeetha   +3 more
doaj   +1 more source

Segmentasi Pelanggan Menggunakan Kerangka LRFMV dan Algoritma K-Means untuk Optimalisasi Strategi Pemasaran

Edumatic
In this competitive digital era, customer behavior is key to maintaining loyalty and increasing profitability. This study aims to implement customer segmentation using the Length, Recency, Frequency, Monetary, Volume (LRFMV) approach and the K-Means ...
A. Nolly Sandra Wawagalang, Syahrullah Syahrullah, Rizka Ardiyansyah, Dwi Shinta Angreni, Septiano Anggun Pratama, Deny Wiria Nugraha   +5 more
doaj   +1 more source

Customer Lifetime Value (CLV) Modeling in B2B Sales: A Project Report on Long-Term Marketing and Sales Strategy Development

, 2020
Customer Lifetime Value (CLV) modeling has increasingly become a critical strategy for optimizing long-term relationships in business-to-business (B2B) sales. This project explores the application of CLV modeling in B2B contexts, focusing on its impact on marketing and sales strategies to maximize client retention and revenue.
Jolaoso, Victor, Badmus, Oluwaseun
openaire   +1 more source

Customer Lifetime Value (CLV) in SME Banking: Strategic Branding and Relationship Building

International Journal of Current Science Research and Review
Customer Lifetime Value (CLV) is a critical metric for understanding the long-term profitability of customer relationships in the banking sector, particularly for Small and Medium Enterprises (SMEs). This study explores the role of CLV in enhancing brand loyalty, customer retention, and strategic growth for Septemberbank in the West Java region.
openaire   +1 more source

Predictive Analytics for Customer Lifetime Value (CLV) in Fashion E-commerce

This study enhances CLV prediction in fashion e-commerce through machine learning models trained on 4.2 million transactions from a leading European retailer. Building on [1], gradient boosting achieves 89% precision in 12-month CLV forecasts, outperforming Pareto/NBD baselines by 18% RMSE reduction. The framework integrates real-time browsing patterns
openaire   +1 more source