Segmentasi Pelanggan Menggunakan Algoritma K-Means dan Analisis RFM di Ova Gaming E-Sports Arena Kediri

Wijaya, Kartika Zahretta (2021) Segmentasi Pelanggan Menggunakan Algoritma K-Means dan Analisis RFM di Ova Gaming E-Sports Arena Kediri. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Selama sepuluh tahun berdiri, Ova Gaming E-Sports Are-na belum menerapkan strategi retensi pelanggan. Persaingan bisnis di daerah ini dapat dibilang cukup ketat, karena dalam radius 500 meter terdapat dua kompetitor bisnis di bidang yang sama. Dengan semakin banyaknya e-sports arena di Kediri, Ova tentu harus melakukan perancangan strategi retensi pelanggan di samping meningkatkan kualitas layanan. Tugas akhir ini melakukan segmentasi pelanggan Ova Gaming E-Sport Arena menggunakan algoritma K-Means dan model RFM. Algoritma K-Means dipilih karena memiliki hasil clustering yang lebih baik dibandingkan metode lainnya. Jumlah seg¬men optimum didapatkan dengan menggunakan metode Elbow dan Silhouette Coefficient. Dilakukan perhitungan Customer Live Value (CLV) dengan menggunakan bobot RFM perhitungan AHP untuk mengetahui urutan prioritas strategi retensi berdasarkan rata-rata CLV segmen terbesar. Setiap segmen pelanggan yang terbentuk selanjutnya dilakukan analisis karakteristik RFM, demografi, dan perilaku sebagai landasan penyusunan strategi retensi pelanggan. Melalui segmentasi pelanggan, diharapkan dapat menjadi upaya dalam meningkatkan pertumbuhan jangka panjang dan profitabilitas perusahaan dengan mengetahui menerapkan strategi retensi pelanggan yang tepat. Hasil penentuan jumlah segmen optimal menggunakan metode Elbow dan Silhouette Coefficient berturut-turut sebesar empat. Berdasarkan hasil tersebut, dalam tugas akhir ini digunakan segmen pelanggan sebesar empat. Berdasarkan analisis karakteristik, masing-masing segmen diurutkan sesuai hasil perhitungan CLV menggunakan pembobotan AHP diberi label superstar, everyday, occasional, dan dormant. Hasil analisis demografi menggunakan atribut usia dan pekerjaan menghasilkan pelanggan usia muda dan berstatus pelajar sebagai target pasar utama perusahaan. Hasil analisis perilaku menunjukkan bahwa hari jumat dan sabtu sebagai waktu ramai. Berdasarkan ketiga hasil analisis yang telah dilakukan, strategi retensi pelanggan menghasilkan antara lain penawaran program loyalitas, pemberian reward, publisitas pemberlakuan protokol kesehatan, dan pemberian informasi layanan dan produk baru.
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Ova Gaming E-Sports was first established ten years ago, yet they have not applied customer retention strategies for their customers. The business competition within the Ova Gaming E-Sport’s area is quite competitive since there are two other business competitors within a radius of 500 meters. Concerning the increasing number of e-sports arenas in Kediri, Ova should start planning customer retention strategies besides improving the quality of their service. This under graduate final project implemented customer segmentation of Ova Gaming E-Sports Arena using the RFM model and K-Means algorithm. The K-Means algorithm was chosen due to its capability to produce better clusters compared to other methods. The optimum number of segments was found using the elbow method and the silhouette coefficient. The Customer Lifetime Value (CLV) was calculated using the weight of each variable obtained from Analytical Hierarchy Process (AHP) and was used to rank the priority of retention strategies based on the highest average of CLV of each segment. Each segment was analyzed based on RFM, demographic, and behavior characteristics as the basis for composing the customer retention strategies. By implementing customer segmentation, it is expected to increase the establishment of long-term relationships with customers and the company’s profit by acknowledging the proper customer retention strategies. The elbow method and the silhouette coefficient showed that the optimal number of segments is four. Furthermore, this result was used as the basis for the generated number of segments, which is also four. Based on the analysis of the characteristics, each segment was ranked based on the calculation result of CLV using the weighting process of the AHP method and was given a label that represented the segments, such as superstar, everyday, occasional, and dormant. The demographic analysis, which was performed based on the information of age and profession, showed that young customers who are likely to be students are the main market target of the company. The behavior analysis showed that friday and saturday are the most packed days within the week. Based on the analysis results, by implementing customer retention strategies, the company can offer loyalty programs, allocation of rewards, publicity of the health protocol implementation, and the spread of new services and products information.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Segmentasi Pelanggan, Clustering, Analytical Hierachy Process, Algoritma K-Means, Model RFM, Customer Segmentation, Clustering, K-Means Algorithm, Analytical Hierachy Process, RFM Model
Subjects:	H Social Sciences > HF Commerce > HF5415.127 Market segmentation. Target marketing Q Science > QA Mathematics > QA278.55 Cluster analysis
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 57201-(S1) Undergraduate Thesis
Depositing User:	Wijaya Kartika Zahretta
Date Deposited:	18 Aug 2021 01:35
Last Modified:	18 Aug 2021 01:35
URI:	http://repository.its.ac.id/id/eprint/87369

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