Identifikasi Loyalitas Pelanggan PLN MOBILE Menggunakan Klasterisasi LRFM Dengan Algoritma MINI-BATCH K-MEANS

Adi, Okky Wicaksono (2025) Identifikasi Loyalitas Pelanggan PLN MOBILE Menggunakan Klasterisasi LRFM Dengan Algoritma MINI-BATCH K-MEANS. Masters thesis, Institut Teknologi Sepuluh Nopember.

Text 6032222108-Master_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 April 2027. Download (3MB) | Request a copy

Abstract

PLN Mobile merupakan aplikasi layanan listrik berbasis seluler yang dikembangkan oleh PT PLN (Persero) untuk memudahkan pengguna dalam melakukan transaksi dan mengakses informasi terkait listrik. Sejak diluncurkan, aplikasi ini telah mengalami pertumbuhan signifikan dalam hal adopsi kebutuhan pengguna. Namun, dalam satu tahun terakhir, PLN Mobile menghadapi tantangan dalam memahami perilaku pengguna dan mengembangkan strategi CRM (Customer Relationship Management) yang efektif. Permasalahan utama yang dihadapi adalah bagaimana mengelompokkan pelanggan dengan akurat untuk merumuskan strategi yang tepat guna meningkatkan kepuasan dan loyalitas mereka. Penelitian ini bertujuan untuk mengidentifikasi segmen pelanggan PLN Mobile berdasarkan analisis data transaksi dan aktivitas login pengguna PLN Mobile dari Februari 2024 hingga Juli 2024 menggunakan model LRFM (Length, Recency, Frequency, Monetary) dan dengan pengklasteran menggunakan Mini-Batch K-Means. Data yang diperoleh kemudian dianalisis menggunakan pembobotan AHP (Analytical Hierarchy Process) untuk menghitung nilai CLV (Customer Lifetime Value) dari setiap segmen pelanggan. Ranking CLV yang dihasilkan digunakan sebagai dasar untuk menentukan strategi CRM yang sesuai dengan karakteristik masing-masing segmen. Strategi ini bertujuan untuk meningkatkan kepuasan dan loyalitas pelanggan melalui pendekatan yang lebih personal dan terarah. Untuk membuat strategi CRM (Customer Relationship Management) yang berhasil, penelitian ini menganalisis segmentasi dan loyalitas pelanggan aplikasi PLN Mobile. Tahapan praproses data digunakan untuk segregasi, pemodelan LRFM (panjang, frekuensi, uang, dan moneter) dan pengelompokan menggunakan Mini Batch K-Means. Ini menghasilkan tiga klaster pelanggan dengan koefisien silhouette tertinggi sebesar 0,612 dan DBI sebesar 0.577. Loyal High-Spenders memiliki skor CLV tertinggi (0.399666) dari analisis Customer Lifetime Value (CLV), diikuti oleh Dormant Customers (0.293644) kemudian Recent Low-Spenders (0.139700). Masing-masing klaster memiliki strategi CRM yang berbeda. Ini termasuk program loyalitas, skema poin, insentif langsung, dan kampanye reaktivasi. Metode ini secara efektif meningkatkan loyalitas dan kontribusi pelanggan.
==================================================================================================================================

PLN Mobile is a cellular-based electricity service application developed by PT PLN (Persero) to facilitate users in performing transactions and accessing electricity-related information. Since its launch, the application has experienced significant growth in user adoption. However, over the past year, PLN Mobile has faced challenges understanding user behavior and developing effective Customer Relationship Management (CRM) strategies. The main issue is how to accurately segment customers to formulate the right strategies to enhance their satisfaction and loyalty. This study aims to identify PLN Mobile customer segments based on an analysis of transaction data and login activity from June 2023 to June 2024, using the LRFM (Length, Recency, Frequency, Monetary) model and clustering with Mini-Batch K-Means. The obtained data is then analyzed using the Analytical Hierarchy Process (AHP) weighting to calculate the Customer Lifetime Value (CLV) for each customer segment. The resulting CLV rankings are used as the basis for determining CRM strategies tailored to the characteristics of each segment. These strategies aim to improve customer satisfaction and loyalty through a more personalized and targeted approach. The results of this study are expected to provide deep insights into PLN Mobile customer segments and offer recommendations for more effective CRM strategies to enhance customer satisfaction and loyalty. To develop a successful Customer Relationship Management (CRM) strategy, this study analyzed the segmentation and loyalty of PLN Mobile application customers. Data preprocessing stages were employed for segregation, LRFM modeling (Length, Recency, Frequency, and Monetary), and clustering using Mini Batch K-Means, resulting in four customer clusters with the highest silhouette coefficient of 0.612 and DBI of 0.577. Loyal High-Spenders had the highest Customer Lifetime Value (CLV) score (0.399666), followed by Dormant Customers (0.293644) and, Recent Low-Spenders (0.139700), and. Each cluster was assigned a tailored CRM strategy, including loyalty programs, point schemes, direct incentives, and reactivation campaigns. This approach effectively enhances customer loyalty and contribution.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Customer Segmentation, LRFM, Mini-Batch K-Means, Customer Lifetime Value, PLN Mobile
Subjects:	T Technology > T Technology (General) > T385 Visualization--Technique T Technology > T Technology (General) > T57.5 Data Processing T Technology > T Technology (General) > T58.6 Management information systems T Technology > T Technology (General) > T58.62 Decision support systems
Divisions:	Interdisciplinary School of Management and Technology (SIMT) > 61101-Master of Technology Management (MMT)
Depositing User:	OKKY WICAKSONO ADI
Date Deposited:	05 Feb 2025 06:13
Last Modified:	05 Feb 2025 06:13
URI:	http://repository.its.ac.id/id/eprint/118329

Actions (login required)

View Item