PENETAPAN STRATEGI BAURAN PEMASARAN 4P EFEKTIF BERDASARKAN SEGMENTASI CUSTOMER DENGAN PEMANFAATAN MACHINE LEARNING (Studi kasus pada konsultan IT - PT XYZ di DKI JAKARTA)

Dwi Teguh Argo Wibowo Doddy, Doddy (2023) PENETAPAN STRATEGI BAURAN PEMASARAN 4P EFEKTIF BERDASARKAN SEGMENTASI CUSTOMER DENGAN PEMANFAATAN MACHINE LEARNING (Studi kasus pada konsultan IT - PT XYZ di DKI JAKARTA). Tesis thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

Marketing activities require high costs in selling a product. So that effective and efficient marketing is a must for organization who must be able to encourage customers to buy more products. The activities to determine an effective and efficient marketing strategy is by segmenting customers and formulating appropriate actions for each customer segment. Currently, most organization have digital data, including customer transaction data. The knowledge to perform digital data analysis and convert it into a form of information is also growing from time to time. One of the techniques in digital data analysis that is developing quite rapidly and is receiving great attention from researchers is Machine Learning, which enables computing to manipulate data with human mindsets. This study conducts a customer segmentation process to determine an effective and efficient marketing strategy by utilizing Machine Learning techniques. Segmentation is carried out on customers who are analyzed based on 3 (three) parameters known as the RFM method, namely Recency or date of the last transaction, Frequency which counts the number of times a purchase transaction occurs, and Monetary to calculate the total money spent on the product purchased. man. The results of this study indicate that Machine Learning with the K-Means Clustering method can be used for customer segmentation so that organizational management can determine an effective 4P Marketing Mix strategy for each segment or cluster to obtain marketing cost efficiency. Keywords: 4P Marketing Mix, Customer Segmentation, K-Means Clustering, Machine Learning, RFM Method.

Item Type: Thesis (Tesis)
Additional Information: [No.Panggil: 2110121040] [Pembimbing: Alfatih S. Manggabarani] [Penguji 1: Miguna Astuti] [Penguji 2: Maria Assumpta Wikantari]
Uncontrolled Keywords: 4P Marketing Mix, Customer Segmentation, K-Means Clustering, Machine Learning, RFM Method.
Subjects: H Social Sciences > HB Economic Theory
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Ekonomi dan Bisnis > Program Studi Manajemen (S2)
Depositing User: Dwi Teguh Argo Wibowo Doddy
Date Deposited: 21 Sep 2023 02:27
Last Modified: 21 Sep 2023 02:27
URI: http://repository.upnvj.ac.id/id/eprint/23619

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