Customer Segmentation using K-means Clustering

Kansal, Tushar; Bahuguna, Suraj; Singh, Vishal; Choudhury, Tanupriya

doi:10.1109/ctems.2018.8769171

Cited by 102 publications

(40 citation statements)

References 1 publication

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“…From the Dendrogram, which is obtained as an output of hierarchical clustering, around 3 clusters are formed. Kmeans clustering is performed for 2 to 5 clusters iteratively [18]. The optimum number of clusters (n) which have a uniform distribution of customers are found when n = 3.…”

Section: F Cluster Analysismentioning

confidence: 99%

A behavioral study on the factors influencing selection of restaurants online during COVID-19 using multivariate statistical analysis

Shanmugam¹,

Krishnan²,

Tholath³

2021

2021 11th International Conference on Cloud Computing, Data Science &Amp; Engineering (Confluence)

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In e-commerce landscapes, online food ordering and delivery service is emerging as the fastest growing industry. Indian food delivery market size is expected to reach 7.5 to 8 billion US dollars in 2023. With the onset of COVID-19 pandemic, many dine-in restaurants are closed and online food ordering is affected enormously. This study is aimed to capture the impact of the COVID-19 pandemic on customers who order food online and analyze the various factors that influence the selection of restaurants online. The role of demographic variables of the respondents on the influencing factors, mode of payment, satisfaction, number of orders, and the most ordered meal is also studied. A self-administered questionnaire is used in this study to collect responses from 403 customers with a past experience of ordering food online. Two major factors emphasising on product and service are obtained as an outcome from Exploratory Factor Analysis that explains 67.72% of the total variance of choosing a restaurant online. Further, the measurement model fit is validated through Structural Equation Modeling. From the validated model, the product factor includes taste, price, discount and offer, the quantity of food, hygiene, and brand name. The service factor includes novelty, location of the restaurant, variety, packaging, nutrition, and promptness of delivery. Due to COVID-19, there is a 70.9% drop in the average frequency of ordering food online during the study period. Among the genders, men place higher food orders per month than women. Dinner is the most ordered meal and breakfast is the least ordered meal among all age categories. Taste is the most influencing factor that influences the customer in selecting a restaurant online, followed by hygiene, the quantity of food, discount and offers.

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Section: F Cluster Analysismentioning

confidence: 99%

A behavioral study on the factors influencing selection of restaurants online during COVID-19 using multivariate statistical analysis

Shanmugam¹,

Krishnan²,

Tholath³

2021

2021 11th International Conference on Cloud Computing, Data Science &Amp; Engineering (Confluence)

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“…Clustering finds broad applications in areas ranging from customer segmentation (Kansal et al, 2018) to agriculture (Mehta et al, 2015), but it also plays an important role in wireless communication networks. For instance, in wireless sensor networks (WSNs), clustering plays a vital role in detecting anomalies in a network (Zhang et al, 2010).…”

Section: Unsupervised Learningmentioning

confidence: 99%

The Role of Artificial Intelligence Driven 5G Networks in COVID-19 Outbreak: Opportunities, Challenges, and Future Outlook

et al. 2020

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“…Nilai K (Cluster) akan ditentukan sebelum dibentuk Cluster nya. Penggunaan Metode Elbow pada beberapa penelitian diyakini baik untuk penentuan nilai K pada metode K-Means [5] [6]. Metode Elbow memiliki kinerja dengan cara menguji coba satu per satu nilai K yang akan disimpan pada Sum Square Error (SSE), setelah ditemukan nilai K Cluster yang optimal maka K Centeroid akan menghasilkan titik pusat secara acak, dan tiap data akan dikalkulasi sampai membentuk suatu Cluster sesuai nilai K Cluster yang dibuat.…”

Section: Pendahuluanunclassified

“…Metode Elbow memiliki kinerja dengan cara menguji coba satu per satu nilai K yang akan disimpan pada Sum Square Error (SSE), setelah ditemukan nilai K Cluster yang optimal maka K Centeroid akan menghasilkan titik pusat secara acak, dan tiap data akan dikalkulasi sampai membentuk suatu Cluster sesuai nilai K Cluster yang dibuat. Pada penulisan ini nilai yang akan di Clustering berasal dari hasil mapping pada model RFM [6]. Pada penelitian [1] [2] [4] [6] telah menjelaskan keuntungan dari penerapan Metode K-Means, Metode K-Medoids dengan model RFM memiliki kinerja yang sesuai dalam pembuatan segmentasi pelanggan berdasarkan dari data pelanggan.…”

Section: Pendahuluanunclassified

“…Pada penulisan ini nilai yang akan di Clustering berasal dari hasil mapping pada model RFM [6]. Pada penelitian [1] [2] [4] [6] telah menjelaskan keuntungan dari penerapan Metode K-Means, Metode K-Medoids dengan model RFM memiliki kinerja yang sesuai dalam pembuatan segmentasi pelanggan berdasarkan dari data pelanggan. Alasan penulis tidak menggunakan metode K-Medoids dikarenakan pada hasil penelitian [7] dan penjelasan dasar dari metode K-Medoids [8] disimpulkan bahwa metode tersebut tidak unggul dalam menganalisa Big-Data atau sekumpulan data yang memiliki banyak variabel.…”

Section: Pendahuluanunclassified

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PENERAPAN METODE K-MEANS DENGAN METODE ELBOW UNTUK SEGMENTASI PELANGGAN MENGGUNAKAN MODEL RFM(Recency, Frequency, & Monetary)

Kiat

Azhar

Rahmayanti

2020

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Segmentasi pelanggan pada perusahaan merupakan tindakan yang dapat mempermudah perusahaan dalam mengambil keputusan ke depan. Pada penelitian ini data yang digunakan berasal dari perusahaan otomotif, PT Hasjrat Abadi Ambon. Data yang dipakai terdiri dari data transaksi dan pelanggan kendaraan bermotor. Penerapan model RFM dapat mengelompokkan pelanggan-pelanggan berdasarkan nilai variabel Recency, Frequency dan Monetary. Hasil dari model RFM akan memperoleh status baru pada tiap pelanggan dari skala terbaik sampai terburuk. Pelanggan yang telah memiliki status akan dikelompokkan menggunakan metode K-Means menjadi beberapa Cluster(kelompok). Dalam menentukan jumlah Cluster yang optimal maka diterapkan metode Elbow. Algoritma yang digunakan dalam pembentukan Cluster terdiri dari Euclidean Distance dan Manhattan Distance. Kedua algoritma akan dibandingkan kualitas pembentukan Clusternya menggunakan metode Silhoutte Coefficient. Hasil yang diberikan pada penelitian ini berupa data yang terbagi atas 5 kelompok dengan dilakukannya lima kali pengujian untuk menentukan centroid yang unggul. Cluster yang unggul akan dibuatkan visualisasi datanya untuk memudahkan perusahaan dalam mengambil keputusan. Berdasarkan penerapan Silhoutte Coefficient, algoritma yang lebih unggul yaitu Manhattan Distance dengan nilai s(i) sebesar 0.152695. Customer segmentation at the company is an action that can facilitate the company in making decisions going forward. In this study the data used came from an automotive company, PT Hasjrat Abadi Ambon. The data used consists of transaction data and motor vehicle customers. The application of the RFM model can classify customers based on the value of the Recency, Frequency and Monetary variables. The results of the RFM model will obtain a new status on each customer from the best to the worst scale. Customers who already have status will be grouped using the K-Means method into several Clusters (groups). In determining the optimal number of Clusters, the Elbow method is applied. The algorithm used in Cluster formation consists of Euclidean Distance and Manhattan Distance. The two algorithms will be compared the quality of the Cluster formation using the Silhoutte Coefficient method. The results given in this study are in the form of data divided into 5 groups by conducting five tests to determine superior centroids. Excellent clusters will be made of data visualization to facilitate the company in making decisions. Based on the application of Silhoutte Coefficient, a superior algorithm is Manhattan Distance with value s(i) : 0.152695.

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Customer Segmentation using K-means Clustering

Cited by 102 publications

References 1 publication

A behavioral study on the factors influencing selection of restaurants online during COVID-19 using multivariate statistical analysis

A behavioral study on the factors influencing selection of restaurants online during COVID-19 using multivariate statistical analysis

The Role of Artificial Intelligence Driven 5G Networks in COVID-19 Outbreak: Opportunities, Challenges, and Future Outlook

PENERAPAN METODE K-MEANS DENGAN METODE ELBOW UNTUK SEGMENTASI PELANGGAN MENGGUNAKAN MODEL RFM(Recency, Frequency, & Monetary)

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