GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM

A rating system or reviews are generally used to assist in making decisions. Rating system widely used as a technique in the recommendation of one of them used by the customer, as in determining the resort to be used. However, the credibility of the rating looks vague because the rating could only represent some points of service. So that customer preference with each other is very different. Personalized recommendation systems offer more personalized advice, precisely knowing the preferences or tastes of the customers. Especially for customers who have a transaction history or reservation as at their resorts provide good information used by managers to design a recommendation model for their customers. In this study aims to create a model of resort recommendations based on a rating of frequency. This frequency is the number of resort use by the customer within the specified time frame. With the frequency can represent the preferences of customers. The RFM method is used to measure the reservation frequency value of the customer. The K-Means method is used to categorize customer data with its frequency and classify the type of resort. Recommendation resort to the customer based on the dominant use in one of the resort types. The recommended type of resort based on the similarity between the types of resorts used with other types of resorts

Wrapped feature selection for neural networks in direct marketing.

In this paper, we try to validate existing theory on and develop additional insight into repeat purchasing behaviour in a direct-marketing setting by means of an illuminating case study. The case involves the detection and qualification of the most relevant RFM (Recency, Frequency and Monetary) features, using a wrapped feature selection method in a neural network context. Results indicate that elimination of redundant/irrelevant features by means of the discussed feature selection method, allows to significantly reduce model complexity without degrading generalisation ability. It is precisely this issue that will allow to infer some very interesting marketing conclusions concerning the relative importance of the RFM-predictor categories. The empirical findings highlight the importance of a combined use of all three RFM variables in predicting repeat purchase behaviour. However, the study also reveals the dominant role of the frequency variable. Results indicate that a model including only frequency variables still yields satisfactory classification accuracy compared to the optimally reduced model.Marketing; Networks; Selection; Theory; Purchasing; Case studies; Studies; Model; Variables; Yield; Classification; Neural networks;

Combining Clustering and MCDM Approach for Evaluating Customer Lifetime Value Ratings

Creating successful transaction actions to retain customers for future re-purchasing is extremely important in fiercely competitive environments. Moreover, different market strategies should be practiced for customers with different lifetime values and loyalty ratings. This work proposes a method, which combines clustering analysis and multiple criteria decision-making approach to evaluate customer lifetime value ratings, and construct the classification rules for individual clusters in market segmentation. An empirical case involving a hardware retailer is illustrated to show the usefulness for evaluating customer lifetime value ratings

A Survey of Sequential Pattern Based E-Commerce Recommendation Systems

E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user–item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining of historical purchase and/or click sequences into a user–item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user–item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems

Marketing Automation Customers Segmentation

Understanding the various facets of a customer’s data is very important for businesses. This insight can help them identify patterns and opportunities to improve their operations. RFM values are often used to identify which customers are valuable for a company. They are then used to identify which promotional activities are most appropriate for them. K-means clustering is an unsupervised learning technique that works when you have unlabeled data. It allows the identification of new data points and groups. This paper proposes a novel approach that combines data cleaning and analyzing customer data to divide a broad market into various consumer groups, and then designing and implementing marketing campaigns that effectively target these consumers. This study aims to identify profitable segments based on historical data (such as purchased items and the associative monetary expenses). The proposed model is formulated using a decision tree and the RFM model to put the customers in three segments (Gold or Silver or Bronze). The decision tree is a type of supervised algorithm that can interpret clustering. It will use labels to classify the data and then plot the clusters

Relation between client opinion (Net Promoter Score) and transactional data: A Pratical Example in Retail at WORTEN

Internship Report presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research and CRMThis professional internship took place at Worten, in Lisbon, with a duration of 9 months in the year 2021/2022 in order to apply and consolidate, in a practical context, the theoretical knowledge acquired in the 1st and 2nd semester of the Master with guidance and supervision, with the to complete the master's degree and gain experience in the area. The main objective of this study was to try to understand customer behaviour considering their opinion given in the NPS (Net Promoter Score) process, trying to measure, classify and predict the customer's transactional behaviour in the company. Although this metric has been criticized by the academic community due to its poor predictive sales performance, NPS remains the most notorious metric in the market adopted by managers as a metric of consumer mindset. This internship report validates that NPS is a bad predictor of Sales in the long term, but a good predictor of frequency of purchase in the short term. This report also emphasizes the significance of conducting a segmented and in-depth analysis of each business area in order to identify the areas that are harming the company the most and those that may have potential churners. Finally, this report offers a comprehensive view of the company and its relationship with the NPS metric

Pengklasteran Data Pelanggan Dari Aplikasi Mobile Untuk Penentuan Strategi Pemasaran Produk PLN

Perkembangan teknologi dan internet memungkinkan pengguna bertransaksi melalui aplikasi seluler, termasuk pembayaran tagihan dan pembelian token listrik. Aplikasi PLN Mobile telah diunduh lebih dari 35 juta pengguna sejak 2016. Pertumbuhan pengguna juga meningkatkan riwayat transaksi listrik. Data tersebut belum dimanfaatkan oleh PLN UID Sulselrabar. Menariknya kinerja penjualan selama empat bulan terakhir menunjukkan adanya masalah di akhir tahun 2023. Penelitian ini menggunakan model Recency-Frequency-Monetary (RFM) dengan mengamati riwayat transaksi listrik selama satu tahun terakhir pada aplikasi PLN Mobile, untuk membantu perusahaan dalam menentukan strategi pemasaran efektif. Data dari Virtual Command Center PLN tanggal 11 Januari 2023 digunakan untuk memodelkan RFM dengan mengkategorikan dan mengevaluasi perilaku pelanggan berdasarkan tiga faktor: recency (jarak transaksi), frequency (jumlah transaksi), dan monetary (total nilai transaksi). Setelah itu, klaster-klaster akan diberi label berdasarkan karakteristik anggotanya dan strategi pemasaran akan ditentukan untuk setiap target pasar yang dipilih. Hasil analisis menunjukkan bahwa model RFM berguna untuk menganalisis perilaku pelanggan dan memberikan segmentasi yang bernilai bagi perusahaan. Terdapat 3 klaster yang dihasilkan, yaitu Dormant Customers, Typical Customers, dan ‘Everyday’ Shoppers. Berdasarkan model RFM, dipilih pendekatan Full Market Coverage dan Multiple Segment Specialization dalam menentukan target pasar. Hal ini memudahkan dalam merumuskan strategi pemasaran dalam upaya untuk meningkatkan kinerja penjualan perusahaan

Pengklasteran Data Pelanggan Dari Aplikasi Mobile Untuk Penentuan Strategi Pemasaran Produk PLN

Perkembangan teknologi dan internet memungkinkan pengguna bertransaksi melalui aplikasi seluler, termasuk pembayaran tagihan dan pembelian token listrik. Aplikasi PLN Mobile telah diunduh lebih dari 35 juta pengguna sejak 2016. Pertumbuhan pengguna juga meningkatkan riwayat transaksi listrik. Data tersebut belum dimanfaatkan oleh PLN UID Sulselrabar. Menariknya kinerja penjualan selama empat bulan terakhir menunjukkan adanya masalah di akhir tahun 2023. Penelitian ini menggunakan model Recency-Frequency-Monetary (RFM) dengan mengamati riwayat transaksi listrik selama satu tahun terakhir pada aplikasi PLN Mobile, untuk membantu perusahaan dalam menentukan strategi pemasaran efektif. Data dari Virtual Command Center PLN tanggal 11 Januari 2023 digunakan untuk memodelkan RFM dengan mengkategorikan dan mengevaluasi perilaku pelanggan berdasarkan tiga faktor: recency (jarak transaksi), frequency (jumlah transaksi), dan monetary (total nilai transaksi). Setelah itu, klaster-klaster akan diberi label berdasarkan karakteristik anggotanya dan strategi pemasaran akan ditentukan untuk setiap target pasar yang dipilih. Hasil analisis menunjukkan bahwa model RFM berguna untuk menganalisis perilaku pelanggan dan memberikan segmentasi yang bernilai bagi perusahaan. Terdapat 3 klaster yang dihasilkan, yaitu Dormant Customers, Typical Customers, dan ‘Everyday’ Shoppers. Berdasarkan model RFM, dipilih pendekatan Full Market Coverage dan Multiple Segment Specialization dalam menentukan target pasar. Hal ini memudahkan dalam merumuskan strategi pemasaran dalam upaya untuk meningkatkan kinerja penjualan perusahaan