RFM-based repurchase behavior for customer classification and segmentation

Rahim, Mussadiq Abdul; Mushafiq, Muhammad; Khan, Salabat; Arain, Zulfiqar Ali

doi:10.1016/j.jretconser.2021.102566

Cited by 56 publications

(25 citation statements)

References 12 publications

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“…Dawane, Waghodekar, and Pagare (2021) used the RFM model and the k ‐means algorithm to cluster customers' behavior. Rahim, Mushafiq, Khan, and Arain (2021) detected the repurchase pattern of customers based on RFM features and statistical analysis of the purchase data. Zong and Xing (2021) improved the RFM model based on the impact of cost to provide a more comprehensive evaluation of customer value.…”

Section: Materials and Backgroundmentioning

confidence: 99%

STFTiS: Introducing a spatio‐temporal FTiS model to investigate the level of citizens' satisfaction of 311 non‐emergency services

Mohammadi

Taleai

Alizadeh

et al. 2022

Transactions in GIS

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Using the 311 service, citizens can help the government gain a deeper understanding of citizens' needs by reporting the status of municipal services. This study proposes the STFTiS model to examine the level of citizens' satisfaction by investigating the spatio‐temporal patterns of 311 reports provided by citizens regarding municipal services. The main novelty of the proposed STFTiS model is its ability to integrate the spatial domain into the traditional FTiS (frequency, time interval, and satisfaction) model. For this purpose, Moran's I index was used to cluster the 311 reports from a spatial perspective. Afterward, the reports were clustered based on the spatial labels and the parameters of time interval and frequency. A fuzzy time series and an autoregressive moving average prediction model have been combined to find the best temporal window for association rule mining. The proposed model was applied to a database of the Boston 311 service. The results from interpreting clusters indicate that Boston's citizens are most interested in government responses to address Transportation and Inspection Service issues. Besides, the most important rule states a strong relationship between the Mayor's 24‐Hour Hotline and Inspection Service reports, with 86% support and 95.7% confidence. The evaluation shows that the STFTiS model has significantly improved the results of the non‐spatial FTiS model. The results can be used by policymakers to effectively manage citizens' needs and provide necessary on‐demand support by discovering the most critical needs of citizens.

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Section: Materials and Backgroundmentioning

confidence: 99%

STFTiS: Introducing a spatio‐temporal FTiS model to investigate the level of citizens' satisfaction of 311 non‐emergency services

Mohammadi

Taleai

Alizadeh

et al. 2022

Transactions in GIS

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“…Case study of a retail business [40]; Genomics-first evaluation of heart disease associated with titin-truncating variants [41]; Marketing strategies evaluation based on big data analysis: a CLUSTERING-MCDM approach [42]; Predicting customer behavior with activation loyalty per period. From RFM to RFMAP [43]; A review of the application of RFM model [5]; Predicting customer value per product: From RFM to RFM/P [44]; RFM-based repurchase behavior for customer classification and segmentation [45]; Customer stratification theory and value evaluation-analysis based on improved RFM model [46].…”

Section: Studiesmentioning

confidence: 99%

RFID: A Fuzzy Linguistic Model to Manage Customers from the Perspective of Their Interactions with the Contact Center

2021

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In an increasingly globalized market, the relationship between the customer and the brand goes beyond the purchasing process. It is very important to understand the customer, to know their needs and to propose actions to increase the value of the brand for them. In the literature, there are several models capable of determining and segmenting customers according to variables dependent on the purchasing process. However, we have not found any study that applies to the business case of classifying customers according to their relationship with the contact centre. In this paper, we establish a working model that allows us to define the value of the customer in the process of interaction with the contact centre, so that we can propose actions both in the sales phase and during the post-sales service, so that the value and perception of the brand is increased. In this model, we propose using the value of recency, frequency, importance and duration of customer interactions with the post-sales service, thus obtaining a ranking, and grouping of customers to help establish personalized communication strategies. We have verified this model by presenting a business case applied to the telecom sector.

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“…Sementara itu, setiap perusahaan memiliki tantangannya sendiri, seperti proses mengumpulkan, memvisualisasikan, dan menganalisis data pelanggan, yang merupakan aset yang paling berharga [9]. Setiap perusaan melakukan analisis data untuk membuka jalan baru bagi lebih banyak pelanggan, lebih banyak keuntungan, dan pengambilan keputusan yang lebih baik [10], [11]. Umunya, perusahaan memiliki banyak data pelanggan, sehingga penerapan DS dapat memanfaatkan data dari umpan balik pelanggan dunia nyata untuk mengembangkan dan menginformasikan produk dan strategi pemasaran mereka [12].…”

Section: Pendahuluanunclassified

“…Dalam manajemen pemasaran, potensi dalam membuka wawasan untuk memenangkan, mempertahankan pelanggan, mendorong efisiensi bisnis, dan pada akhirnya meningkatkan kinerja dalam hal penjualan dan minat pelanggan. Dalam beberapa tahun ini, banyak peneliti mengusulkan analisis recency, frequency dan monetary (RFM) seperti Rahim, M et al [10] menerapkan pemodelan RFM dan teknik pemodelan data untuk mendeteksi pola perilaku pelanggan. Mereka mengusulkan tiga model berbeda untuk klasifikasi pelanggan yaitu Multi-Layer Perceptron dengan tingkat akurasi 99% lebih unggul dari metode Suport Vector Machine (SVM) dengan akurasi 95% dan Decision Tree Classification (DTC) sebesar 98%.…”

Section: Tinjauan Literaturunclassified

Data Science bidang Pemasaran : Analisis Prilaku Pelanggan

Harahap

Lubis

Situmorang

2021

dsi

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Dalam kegiatan pemasaran digital, data Science (DS) memiliki peran penting dalam memahami kinerja industri pemasaran sebelum menerapkan teknik pemasaran digital pada pemasaran produk. Hal ini dikarenakan setiap pelanggan merespons secara berbeda setiap penawaran. Perilaku pelanggan juga berubah berdasarkan waktu karena mereka mungkin memiliki kebutuhan yang berbeda pada situasi yang berbeda. Pada makalah ini fokus menyajikan analisis bisnis dengan penerapan DS untuk mengeksplorasi pola perilaku dan juga memprediksi bagaimana pelanggan akan merespons penawaran yang berbeda. Penerapan analisis data eksplorasi juga diterapkan untuk menjawab beberapa pertanyaan bisnis, dari hasil pengamatan menghasilkan lima kelompok pelanggan yang disajikan dalam bentuk visualisasi dan model Random Forest Classifier memiliki skor akurasi prediksi terbaik sebesar 91%, kemudian K neighbors Classifier dan Logistic Regression.

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RFM-based repurchase behavior for customer classification and segmentation

Cited by 56 publications

References 12 publications

STFTiS: Introducing a spatio‐temporal FTiS model to investigate the level of citizens' satisfaction of 311 non‐emergency services

STFTiS: Introducing a spatio‐temporal FTiS model to investigate the level of citizens' satisfaction of 311 non‐emergency services

RFID: A Fuzzy Linguistic Model to Manage Customers from the Perspective of Their Interactions with the Contact Center

Data Science bidang Pemasaran : Analisis Prilaku Pelanggan

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