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Journal articles on the topic 'Machine Learning,Customer Segmentation'

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Published: 18 April 2022

Last updated: 27 April 2022

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Hong Dien, Le, Nguyen Phuc Son, Pham Hoang Uyen, and Le Van Hinh. "On a segmentation of Coopextra customers in Thu Duc district." Science & Technology Development Journal - Economics - Law and Management 3, no. 1 (2019): 28–36. http://dx.doi.org/10.32508/stdjelm.v3i1.537.

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Customer segmentation is the process of grouping customers based on similar characteristics such as behavior, shopping habits…so that businesses can do marketing to each customer group effectively and appropriately. Customer segmentation helps businesses determine different strategies and different marketing approaches to different groups. Customer segmentation helps marketers better understand customers as well as provide goals, strategies and marketing methods for different target groups. This paper aims to examine the customer segmentation using clustering method in statistics and unsupervised machine learning. The algorithms used are K-means and Elbow which are famous algorithms that have been successfully applied in many areas such as marketing, biology, library, insurance, finance... The purpose of clustering is to find meaningful market segments. However, the adoption and adjustment of parameters in the algorithms so as to find significant customer segmentations remain a challenge at present. In this paper, we used data of customers of Thu Duc CoopExtra and found significant customer segmentations which can be useful for more effective marketing and customer care by the supermarket.

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Smeureanu, Ion, Gheorghe Ruxanda, and Laura Maria Badea. "CUSTOMER SEGMENTATION IN PRIVATE BANKING SECTOR USING MACHINE LEARNING TECHNIQUES." Journal of Business Economics and Management 14, no. 5 (2013): 923–39. http://dx.doi.org/10.3846/16111699.2012.749807.

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Machine learning techniques have proven good performance in classification matters of all kinds: medical diagnosis, character recognition, credit default and fraud prediction, and also foreign exchange market prognosis. Customer segmentation in private banking sector is an important step for profitable business development, enabling financial institutions to address their products and services to homogeneous classes of customers. This paper approaches two of the most popular machine learning techniques, Neural Networks and Support Vector Machines, and describes how each of these perform in a segmentation process.

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PALANGAD OTHAYOTH, SAMYUKTHA, and Raja Muthalagu. "Customer Segmentation Using Various Machine Learning Techniques." International Journal of Business Intelligence and Data Mining 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijbidm.2022.10036753.

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Monil, Patel. "Customer Segmentation using Machine Learnin." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (2020): 2104–8. http://dx.doi.org/10.22214/ijraset.2020.6344.

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Jaiswal, Anshumala. "Data Mining Approach for Customer Segmentation." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1008–12. http://dx.doi.org/10.22214/ijraset.2021.35140.

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In Marketing world, rapidly increasing competition makes it difficult to sustain in this field, marketers have to take decisions that satisfy their customers. Growth of an organization is highly depended on right decisions by the organization. For that, they have to collect deep knowledge about their customer's needs. Substantial amount of data of customers is collected daily. To manage such a huge data is not a piece of cake. An idea is to segment customers in different groups and go through each group and find the potential group among pool of customers. If it is done manually, it will require lot of human efforts and also consume lot of time. For reducing the human efforts, machine learning plays an important role. One can find various patterns which is used to analyze customers database using machine learning algorithms. Using clustering technique, customers can be segmented on the basis of some similarities. One of the best procedures for clustering technique is by using K-means algorithm. The k-means clustering algorithm is one of the widely used data clustering methods where the datasets having “n” data points are partitioned into “k” groups or cluster [1].in this paper. K is number of clusters or groups or segments and elbow method is used for determining value of K.

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Tan, Kok Sheng, and Preethi Subramanian. "Proposition of Machine Learning Driven Personalized Marketing Approach for E-Commerce." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3532–37. http://dx.doi.org/10.1166/jctn.2019.8319.

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The ubiquity of digital devices and Internet has formed a constantly connected online environment which led to the extensive adoption of e-commerce. However, the active participation of growing number of stakeholders intensifies the highly competitive landscape of the dynamic e-commerce market and the scarcity of trust in e-commerce business impede the generation of consistent sales growth. The obstruction necessitates the implementation of innovative marketing strategies to enhance the relationships with customers to develop customer loyalty. Therefore, a machine learning driven personalized marketing approach is proposed to facilitate the implementation of personalized marketing in which there are 2 significant sequential elements namely, the development of personalized marketing contents and delivery of the contents to prospective customers. Cluster analysis is employed to perform customer segmentation to discover customer segments due to the capability of the analysis to identify similarities in customer preferences in which the discovered customer segments are used to construct personalized marketing contents. In addition, artificial neural network is employed to predict prospective customers due to the capability of artificial neural network to comprehend complex relationships between customer demographics and buying behaviour in which the prediction facilitates the delivery of the constructed personalized marketing contents to potential repeat customer to optimize the marketing initiative. The combination of cluster analysis and artificial neural network empowers the construction of an efficacious marketing pipeline which enhances the competency of e-commerce businesses.

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Durojaye, D. I., and Georgina Obunadike. "ANALYSIS AND VISUALIZATION OF MARKET SEGEMENTATION IN BANKING SECTOR USING KMEANS MACHINE LEARNING ALGORITHM." FUDMA JOURNAL OF SCIENCES 6, no. 1 (2022): 387–93. http://dx.doi.org/10.33003/fjs-2022-0601-910.

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Segmentation is a way of assigning each dataset to a segment called cluster. It is widely applied in different area of human endeavor such as banking sector, health sector, retail, media etc. Many organizations are faced with problems of ineffective customer care services and intelligent management decisions because of inability to effectively analyze customer data that will give insight to the nature of customers to help in effective customer services and intelligent management decision. Kmeans algorithm is the widely used algorithm for market segmentation, normally the k value of Kmeans algorithm are randomly picked. Picking the optimal k value is usually a challenge in application of Kmeans algorithm and this usually affects the performance of Kmeans algorithm. This work applies elbow method to obtain the optimal k value that was applied to analyze dataset from banking sector (in this case United Bank of Africa) for better insight, business management and marketing strategy. The customer cluster created was evaluated using visual plots and cluster centers. The optimal k value of six (6) was obtained using the elbow function. The dataset was thus segmented based on the optimal k value of 6 obtained. The clustering results obtained showed high intra cluster similarity (data within a cluster are similar) and low inter cluster similarity (data from different clusters are dissimilar). The result also showed that customers in cluster 3 and 4 has similar marketing needs and can be served together

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Dullaghan, Cormac, and Eleni Rozaki. "Integration of Machine Learning Techniques to Evaluate Dynamic Customer Segmentation Analysis for Mobile Customers." International Journal of Data Mining & Knowledge Management Process 7, no. 1 (2017): 13–24. http://dx.doi.org/10.5121/ijdkp.2017.7102.

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Peng, Zou, Hao Yuanyuan, and Li Yijun. "Customer value segmentation based on cost-sensitive learning Support Vector Machine." International Journal of Services Technology and Management 14, no. 1 (2010): 126. http://dx.doi.org/10.1504/ijstm.2010.032888.

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Jiang, Shimiao, Shuqin Cai, Georges Olle Olle, and Zhiyong Qin. "Durable product review mining for customer segmentation." Kybernetes 44, no. 1 (2015): 124–38. http://dx.doi.org/10.1108/k-06-2014-0117.

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Purpose – More and more e-commerce web sites are using online customer reviews (OCRs) for customer segmentation. However, for durable products, customer purchases, and reviews only once for a long time, as while the product review score may highly affected by service factors or be “gently” evaluated. Existing regression or machine learning-based methods suffer from low accuracy when applied to the OCRs of durable products on e-commerce web sites. The purpose of this paper is to propose a new approach for customer segment analysis base on OCRs of durable products. Design/methodology/approach – The research proposes a two-stage approach that employs latent class analysis (LCA): the feature-mention matrix construction stage and the LCA-based customer segmentation stage. The approach considers reviewers’ mention on product features, and the probability-based LCA method is adopted upon the characteristics of online reviews, to effectively cluster reviewers into specified segmentations. Findings – The research finding is that, using feature-mention instead of feature-opinion records makes segment analysis more effective. The research also finds that, LCA method can better explain the characteristics of the OCR data of durable products for customer segmentation. Practical implications – The research proposes a new approach to durable product review mining for customer segmentation analysis. The segment analysis result can provide supports for new product design and development, repositioning of existing products, marketing strategy development and product differentiation. Originality/value – A new approach for customer segmentation analysis base on OCRs of durable products is proposed.

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Rogic, Suncica, and Ljiljana Kascelan. "Class balancing in customer segments classification using support vector machine rule extraction and ensemble learning." Computer Science and Information Systems, no. 00 (2020): 52. http://dx.doi.org/10.2298/csis200530052r.

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An objective and data-based market segmentation is a precondition for efficient targeting in direct marketing campaigns. The role of customer segments classification in direct marketing is to predict the segment of most valuable customers who is likely to respond to a campaign based on previous purchasing behavior. A good-performing predictive model can significantly increase revenue, but also, reduce unnecessary marketing campaign costs. As this segment of customers is generally the smallest, most classification methods lead to misclassification of the minor class. To overcome this problem, this paper proposes a class balancing approach based on Support Vector Machine-Rule Extraction (SVM-RE) and ensemble learning. Additionally, this approach allows for rule extraction, which can describe and explain different customer segments. Using a customer base from a company?s direct marketing campaigns, the proposed approach is compared to other data balancing methods in terms of overall prediction accuracy, recall and precision for the minor class, as well as profitability of the campaign. It was found that the method performs better than other compared class balancing methods in terms of all mentioned criteria. Finally, the results confirm the superiority of the ensemble SVM method as a preprocessor, which effectively balances data in the process of customer segments classification

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Singh, Krishna Kant, Akansha Singh, Pushpa Singh, and Narendra Singh. "Machine Learning based Classification and Segmentation Techniques for CRM: A Customer Analytics." International Journal of Business Forecasting and Marketing Intelligence 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijbfmi.2020.10031824.

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Singh, Narendra, Pushpa Singh, Krishna Kant Singh, and Akansha Singh. "Machine learning based classification and segmentation techniques for CRM: a customer analytics." International Journal of Business Forecasting and Marketing Intelligence 6, no. 2 (2020): 99. http://dx.doi.org/10.1504/ijbfmi.2020.109878.

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Koca, Orkun Berk. "Determining customer segmentation and behaviour models with database marketing and machine learning." Pressacademia 8, no. 2 (2021): 89–111. http://dx.doi.org/10.17261/pressacademia.2021.1409.

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De Marco, Marco, Paolo Fantozzi, Claudio Fornaro, Luigi Laura, and Antonio Miloso. "Cognitive analytics management of the customer lifetime value: an artificial neural network approach." Journal of Enterprise Information Management 34, no. 2 (2021): 679–96. http://dx.doi.org/10.1108/jeim-01-2020-0029.

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PurposeThe purpose of this study is to show that the use of CAM (cognitive analytics management) methodology is a valid tool to describe new technology implementations for businesses.Design/methodology/approachStarting from a dataset of recipes, we were able to describe consumers through a variant of the RFM (recency, frequency and monetary value) model. It has been possible to categorize the customers into clusters and to measure their profitability thanks to the customer lifetime value (CLV).FindingsAfter comparing two machine learning algorithms, we found out that self-organizing map better classifies the customer base of the retailer. The algorithm was able to extract three clusters that were described as personas using the values of the customer lifetime value and the scores of the variant of the RFM model.Research limitations/implicationsThe results of this methodology are strictly applicable to the retailer which provided the data.Practical implicationsEven though, this methodology can produce useful information for designing promotional strategies and improving the relationship between company and customers.Social implicationsCustomer segmentation is an essential part of the marketing process. Improving further segmentation methods allow even small and medium companies to effectively target customers to better deliver to society the value they offer.Originality/valueThis paper shows the application of CAM methodology to guide the implementation and the adoption of a new customer segmentation algorithm based on the CLV.

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Sroka, Łukasz. "The use of the k-prototypes method in the segmentation of customers of a company in the Multi-Level Marketing." Wiadomości Statystyczne. The Polish Statistician 66, no. 7 (2021): 44–56. http://dx.doi.org/10.5604/01.3001.0015.0482.

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Segmentation of clients plays an important role in designing a company’s marketing strategy. Differentiating between groups of customers in terms of their characteristics and behaviours, and understanding how customer preferences and needs are shaped, is key to determining effective marketing tools. The aim of the paper is to present the potential of the k-prototypes method in the customer segmentation process. In the study, conducted according to the above-mentioned type of machine learning algorithm, clusters were extracted and the statistical analysis of the groups thus obtained was carried out, using sales data of a trading company operating in the Multi-Level Marketing (MLM) system for the period from September to October 2020. As a result, the company’s customers were divided into six segments, significantly different from each other in terms of features characteristic for clients of an MLM enterprise. The k-prototypes algorithm, adopted here as a segmentation method, satisfactorily processed both numerical and categorical data, and made it possible to identify the clusters. The results obtained by means of this method demonstrated that there are groups of clients of the examined company which focused on maximizing the benefits indicated in the marketing plan as the aim of the company’s operations, customers undecided as to whether to continue in the entity’s structure, and customers who did not plan to associate their future with the examined company.

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Wang, Bihong. "Deep Neural Network-Based Business Data Classification in Intelligent Business Management." Mathematical Problems in Engineering 2022 (March 31, 2022): 1–8. http://dx.doi.org/10.1155/2022/7104750.

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The purpose of this paper is to explore how intelligent data mining technology can be used to improve the customer service capability of commercial companies. Based on extensive research on commercial business, this paper uses data mining and machine learning techniques to build an overall framework for applying intelligent technologies to business improvement, and uses multilayer perceptrons and integrated learning algorithms to build classifiers for customer segmentation; uses association rule mining to assist commercial companies in business decisions; uses clustering algorithms and visualization techniques to further analyze claims cases and assist in commercial fraud detection. The multilayer perceptron classification makes the classification of commercial customers more detailed and reasonable, and the company’s business staff can sell products in a more targeted manner; association rule mining greatly improves the quality and efficiency of the company’s management’s decision making.

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Lee, Zne-Jung, Chou-Yuan Lee, Li-Yun Chang, and Natsuki Sano. "Clustering and Classification Based on Distributed Automatic Feature Engineering for Customer Segmentation." Symmetry 13, no. 9 (2021): 1557. http://dx.doi.org/10.3390/sym13091557.

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To beat competition and obtain valuable information, decision-makers must conduct in-depth machine learning or data mining for data analytics. Traditionally, clustering and classification are two common methods used in machine mining. For clustering, data are divided into various groups according to the similarity or common features. On the other hand, classification refers to building a model by given training data, where the target class or label is predicted for the test data. In recent years, many researchers focus on the hybrid of clustering and classification. These techniques have admirable achievements, but there is still room to ameliorate performances, such as distributed process. Therefore, we propose clustering and classification based on distributed automatic feature engineering (AFE) for customer segmentation in this paper. In the proposed algorithm, AFE uses artificial bee colony (ABC) to select valuable features of input data, and then RFM provides the basic data analytics. In AFE, it first initializes the number of cluster k. Moreover, the clustering methods of k-means, Wald method, and fuzzy c-means (FCM) are processed to cluster the examples in variant groups. Finally, the classification method of an improved fuzzy decision tree classifies the target data and generates decision rules for explaining the detail situations. AFE also determines the value of the split number in the improved fuzzy decision tree to increase classification accuracy. The proposed clustering and classification based on automatic feature engineering is distributed, performed in Apache Spark platform. The topic of this paper is about solving the problem of clustering and classification for machine learning. From the results, the corresponding classification accuracy outperforms other approaches. Moreover, we also provide useful strategies and decision rules from data analytics for decision-makers.

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Nilashi, Mardani, Liao, Ahmadi, Manaf, and Almukadi. "A Hybrid Method with TOPSIS and Machine Learning Techniques for Sustainable Development of Green Hotels Considering Online Reviews." Sustainability 11, no. 21 (2019): 6013. http://dx.doi.org/10.3390/su11216013.

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This paper proposes a hybrid method for online reviews analysis through multi-criteria decision-making, text mining and predictive learning techniques to find the relative importance of factors affecting travelers’ decision-making in selecting green hotels with spa services. The proposed method is developed for the first time in the context of tourism and hospitality by this research, especially for customer segmentation in green hotels through customers’ online reviews. We use Self-Organizing Map (SOM) for cluster analysis, Latent Dirichlet Analysis (LDA) technique for analyzing textual reviews, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ranking hotel features, and Neuro-Fuzzy technique to reveal the customer satisfaction levels. The impact of green hotels with spa and non-spa services on travelers’ satisfaction is investigated for four travelling groups: Travelled solo, Travelled with family, Travelled as a couple and Travelled with friends. The proposed method is evaluated on the travelers’ reviews on 152 hotels in Malaysia. The findings of this study provide an important method for travelers’ decision-making for hotel selection through User-Generated Content (UGC) and help hotel managers to improve their service quality and marketing strategies.

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Ko, Taehoon, Je Hyuk Lee, Hyunchang Cho, Sungzoon Cho, Wounjoo Lee, and Miji Lee. "Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data." Industrial Management & Data Systems 117, no. 5 (2017): 927–45. http://dx.doi.org/10.1108/imds-06-2016-0195.

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Purpose Quality management of products is an important part of manufacturing process. One way to manage and assure product quality is to use machine learning algorithms based on relationship among various process steps. The purpose of this paper is to integrate manufacturing, inspection and after-sales service data to make full use of machine learning algorithms for estimating the products’ quality in a supervised fashion. Proposed frameworks and methods are applied to actual data associated with heavy machinery engines. Design/methodology/approach By following Lenzerini’s formula, manufacturing, inspection and after-sales service data from various sources are integrated. The after-sales service data are used to label each engine as normal or abnormal. In this study, one-class classification algorithms are used due to class imbalance problem. To address multi-dimensionality of time series data, the symbolic aggregate approximation algorithm is used for data segmentation. Then, binary genetic algorithm-based wrapper approach is applied to segmented data to find the optimal feature subset. Findings By employing machine learning-based anomaly detection models, an anomaly score for each engine is calculated. Experimental results show that the proposed method can detect defective engines with a high probability before they are shipped. Originality/value Through data integration, the actual customer-perceived quality from after-sales service is linked to data from manufacturing and inspection process. In terms of business application, data integration and machine learning-based anomaly detection can help manufacturers establish quality management policies that reflect the actual customer-perceived quality by predicting defective engines.

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Yadegaridehkordi, Elaheh, Mehrbakhsh Nilashi, Mohd Hairul Nizam Bin Md Nasir, et al. "Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques." Technology in Society 65 (May 2021): 101528. http://dx.doi.org/10.1016/j.techsoc.2021.101528.

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Hananto, Valentinus Roby, Uwe Serdült, and Victor Kryssanov. "A Text Segmentation Approach for Automated Annotation of Online Customer Reviews, Based on Topic Modeling." Applied Sciences 12, no. 7 (2022): 3412. http://dx.doi.org/10.3390/app12073412.

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Online customer review classification and analysis have been recognized as an important problem in many domains, such as business intelligence, marketing, and e-governance. To solve this problem, a variety of machine learning methods was developed in the past decade. Existing methods, however, either rely on human labeling or have high computing cost, or both. This makes them a poor fit to deal with dynamic and ever-growing collections of short but semantically noisy texts of customer reviews. In the present study, the problem of multi-topic online review clustering is addressed by generating high quality bronze-standard labeled sets for training efficient classifier models. A novel unsupervised algorithm is developed to break reviews into sequential semantically homogeneous segments. Segment data is then used to fine-tune a Latent Dirichlet Allocation (LDA) model obtained for the reviews, and to classify them along categories detected through topic modeling. After testing the segmentation algorithm on a benchmark text collection, it was successfully applied in a case study of tourism review classification. In all experiments conducted, the proposed approach produced results similar to or better than baseline methods. The paper critically discusses the main findings and paves ways for future work.

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Laksono, Bagaskoro Cahyo, and Ika Yuni Wulansari. "PEMODELAN DAN PENERAPAN METODE RFM PADA ESTIMASI NILAI KONSUMEN (CUSTOMER LIFETIME VALUE) MENGGUNAKAN K-MEANS CLUSTERING MACHINE LEARNING." Seminar Nasional Official Statistics 2020, no. 1 (2021): 1277–85. http://dx.doi.org/10.34123/semnasoffstat.v2020i1.689.

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Krisis Covid-19 berdampak pada revenue perusahaan, jika perusahaan tidak meningkatkan strategi pemasaran yang tepat terhadap konsumen, akan beresiko gulung tikar karena tidak memiliki target pasar yang jelas. Disamping itu, perusahaan dapat mengembangkan bisnisnya menggunakan big data untuk mendukung decision making. Big data dalam industry e-commerce yang mencakup ukuran dan kecepatan transaksi yang tinggi dapat digunakan untuk menganalisis perilaku konsumen bahkan memprediksi nilai konsumen. Pada zaman sekarang perusahaan mulai mengembangkan ketertarikan bisnis yang berorientasi konsumen daripada berorientasi produk. Salah satu cara yang dapat digunakan untuk menentukan nilai konsumen yaitu dengan menghitung Customer Lifetime Value (CLV). Dengan mengetahui CLV di level individu, akan berguna untuk membantu pengambil keputusan untuk mengembangkan segmentasi konsumen dan alokasi sumber daya. Penting dilakukan segmentasi atau pengelompokkan konsumen yang menggambarkan kelompok loyalitas konsumen. Oleh karena itu tujuan dalam penelitian ini adalah melakukan penghitungan CLV dan segmentasi konsumen dengan menggunakan metode analisis RFM dengan K-Means Clustering Machine Learning Model. Tahapan analisis diantaranya mendefinisikan RFM Segmentation Value yang merupakan clustering yang dibangun dari angka kumulatif yang berisi penjumlahan Recency, Frequency dan Monetary Level yang dimiliki masing-masing konsumen. Kombinasi nilai level yang tercipta berkisar antara 0,1,2,3,4,5,6 yang artinya semakin tinggi nilainya maka semakin berharga konsumen tersebut. Pada akhirnya, metode segmentasi konsumen yang di bangun penulis dapat digunakan untuk optimasi strategi perusahaan untuk mendapat profit yang maksimum. Metode ini dapat diterapkan pada berbagai kasus dan perusahaan lain. Hasil penelitian ini diharapkan dapat membantu perusahaan untuk bertahan di tengah krisis akibat Covid-19.

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Sundareswaran, Ghanasiyaa, Harshini Kamaraj, Shanmathy Sanjay, Akalya Devi, Poojashree Elangovan, and Kruthikkha P. "Consumer Behavior Analysis." International Journal of Research and Applied Technology 2, no. 1 (2022): 82–90. http://dx.doi.org/10.34010/injuratech.v2i1.6536.

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Research on consumer behavior has become essential in recent years as it plays an important role in business marketing and growth. Consumers are the king of the market. For-profit organizations cannot function without customers. All the activities of the company end with the consumer and their satisfaction. Consumer behavior is the study of consumers and how they choose or eliminate products. This theory extends not only to products but also to services consumed. To develop a framework for studying consumer behavior, first look at the factors that influence consumer buying behavior, as well as the various thinking paradigms that have influenced the progress and discipline of consumer research. Modeling customer behavior is nothing more than creating a mathematical structure to map the general behavior of a particular customer group. This is done to predict how consumers will react in a particular situation. The purpose of the survey is to better understand consumer behavior by examining the factors that influence the consumer's purchasing process. The main purpose of studying consumer behavior is to understand how consumers feel and think. Building a recommendation engine is another application for studying consumer behavior. The recommendation engine basically recommends several products based on a variety of factors, including previous purchases by consumers, age, etc. It's a kind of data filtering tool that uses machine learning algorithms to recommend the most relevant items to a particular customer. The purpose of this paper is to analyze consumer segmentation and sentiment regarding product reviews and build a product recommendation system.

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Khan, Mohammad Farhan, Farnaz Haider, Ahmed Al-Hmouz, and Mohammad Mursaleen. "Development of an Intelligent Decision Support System for Attaining Sustainable Growth within a Life Insurance Company." Mathematics 9, no. 12 (2021): 1369. http://dx.doi.org/10.3390/math9121369.

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Consumer behaviour is one of the most important and complex areas of research. It acknowledges the buying behaviour of consumer clusters towards any product, such as life insurance policies. Among various factors, the three most well-known determinants on which human conjecture depends for preferring a product are demographic, economic and psychographic factors, which can help in developing an accurate market design and strategy for the sustainable growth of a company. In this paper, the study of customer satisfaction with regard to a life insurance company is presented, which focused on comparing artificial intelligence-based, data-driven approaches to classical market segmentation approaches. In this work, an artificial intelligence-based decision support system was developed which utilises the aforementioned factors for the accurate classification of potential buyers. The novelty of this paper lies in developing supervised machine learning models that have a tendency to accurately identify the cluster of potential buyers with the help of demographic, economic and psychographic factors. By considering a combination of the factors that are related to the demographic, economic and psychographic elements, the proposed support vector machine model and logistic regression model-based decision support systems were able to identify the cluster of potential buyers with collective accuracies of 98.82% and 89.20%, respectively. The substantial accuracy of a support vector machine model would be helpful for a life insurance company which needs a decision support system for targeting potential customers and sustaining its share within the market.

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Hemamalini, V., S. Rajarajeswari, S. Nachiyappan, et al. "Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System." Journal of Food Quality 2022 (February 11, 2022): 1–6. http://dx.doi.org/10.1155/2022/5262294.

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One of the most critical aspects of quality assurance is inspecting products for defects before they are sold or shipped. A good product is more vital than having more of the same item for a customer’s enjoyment. The client has a significant role in determining the quality of a product. Another way to think about quality is as the total of all the characteristics that contribute to the creation of items that the client enjoys. Recently, the application of machine vision and image processing technology to improve the surface quality of fruits and other foods has increased significantly. This is primarily because these technologies make significant advancements in areas where the human eye falls short. This means that, by utilizing computer vision and image processing techniques, time-consuming and subjective industrial quality control processes can be eliminated. This article discusses how to check and assess food using picture segmentation and machine learning. It is capable of classifying fruits and determining whether a piece of fruit is rotten. To begin, Gaussian elimination is used to remove noise from images. Then, photos are subjected to histogram equalization in order to improve their quality. Segmentation of the image is carried out using the K-means clustering technique. Then, fruit photos are classified using machine learning methods such as KNN, SVM, and C4.5. These algorithms determine if a fruit is damaged or not.

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Troncoso Espinosa, Fredy Humberto, and Javiera Valentina Ruiz Tapia. "PREDICCIÓN DE FUGA DE CLIENTES EN UNA EMPRESA DE DISTRIBUCIÓN DE GAS NATURAL MEDIANTE EL USO DE MINERÍA DE DATOS." Universidad Ciencia y Tecnología 24, no. 106 (2020): 79–87. http://dx.doi.org/10.47460/uct.v24i106.399.

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La fuga de clientes es un problema relevante al que enfrentan las empresas de servicios y que les puede generar pérdidas económicas significativas. Identificar los elementos que llevan a un cliente a dejar de consumir un servicio es una tarea compleja, sin embargo, mediante su comportamiento es posible estimar una probabilidad de fuga asociada a cada uno de ellos. Esta investigación aplica minería de datos para la predicción de la fuga de clientes en una empresa de distribución de gas natural, mediante dos técnicas de machine learning: redes neuronales y support vector machine. Los resultados muestran que mediante la aplicación de estas técnicas es posible identificar los clientes con mayor probabilidad de fuga para tomar sobre estas acciones de retenciónoportunas y focalizadas, minimizando los costos asociados al error en la identificación de estos clientes. Palabras Clave: fuga de clientes, minería de datos, machine learning, distribución de gas natural. Referencias [1]J. Miranda, P. Rey y R. Weber, «Predicción de Fugas de Clientes para una Institución Financiera Mediante Support Vector Machines,» Revista Ingeniería de Sistemas Volumen XIX, pp. 49-68, 2005. [2]P. A. Pérez V., «Modelo de predicción de fuga de clientes de telefonía movil post pago,» Universidad de Chile, Santiago, Chile, 2014. [3]Gas Sur S.A., «https://www.gassur.cl/Quienes-Somos/,» [En línea]. [4]J. Xiao, X. Jiang, C. He y G. Teng, «Churn prediction in customer relationship management via GMDH-based multiple classifiers ensemble,» IEEE IntelligentSystems, vol. 31, nº 2, pp. 37-44, 2016. [5]A. M. Almana, M. S. Aksoy y R. Alzahrani, «A survey on data mining techniques in customer churn analysis for telecom industry,» International Journal of Engineering Research and Applications, vol. 4, nº 5, pp. 165-171, 2014. [6]A. Jelvez, M. Moreno, V. Ovalle, C. Torres y F. Troncoso, «Modelo predictivo de fuga de clientes utilizando mineríaa de datos para una empresa de telecomunicaciones en chile,» Universidad, Ciencia y Tecnología, vol. 18, nº 72, pp. 100-109, 2014. [7]D. Anil Kumar y V. Ravi, «Predicting credit card customer churn in banks using data mining,» International Journal of Data Analysis Techniques and Strategies, vol. 1, nº 1, pp. 4-28, 2008. [8]E. Aydoğan, C. Gencer y S. Akbulut, «Churn analysis and customer segmentation of a cosmetics brand using data mining techniques,» Journal of Engineeringand Natural Sciences, vol. 26, nº 1, 2008. [9]G. Dror, D. Pelleg, O. Rokhlenko y I. Szpektor, «Churn prediction in new users of Yahoo! answers,» de Proceedings of the 21st International Conference onWorld Wide Web, 2012. [10]T. Vafeiadis, K. Diamantaras, G. Sarigiannidis y K. Chatzisavvas, «A comparison of machine learning techniques for customer churn prediction,» SimulationModelling Practice and Theory, vol. 55, pp. 1-9, 2015. [11]Y. Xie, X. Li, E. Ngai y W. Ying, «Customer churn prediction using improved balanced random forests,» Expert Systems with Applications, vol. 36, nº 3, pp.5445-5449, 2009. [12]U. Fayyad, G. Piatetsky-Shapiro y P. Smyth, «Knowledge Discovery and Data Mining: Towards a Unifying Framework,» de KDD-96 Proceedings, 1996. [13]R. Brachman y T. Anand, «The process of knowledge discovery in databases,» de Advances in knowledge discovery and data mining, 1996. [14]K. Lakshminarayan, S. Harp, R. Goldman y T. Samad, «Imputation of Missing Data Using Machine Learning Techniques,» de KDD, 1996. [15]B. Nguyen , J. L. Rivero y C. Morell, «Aprendizaje supervisado de funciones de distancia: estado del arte,» Revista Cubana de Ciencias Informáticas, vol. 9, nº 2, pp. 14-28, 2015. [16]I. Monedero, F. Biscarri, J. Guerrero, M. Peña, M. Roldán y C. León, «Detection of water meter under-registration using statistical algorithms,» Journal of Water Resources Planning and Management, vol. 142, nº 1, p. 04015036, 2016. [17]I. Guyon y A. Elisseeff, «An introduction to variable and feature selection,» Journal of machine learning research, vol. 3, nº Mar, pp. 1157-1182, 2003. [18]K. Polat y S. Güneş, «A new feature selection method on classification of medical datasets: Kernel F-score feature selection,» Expert Systems with Applications, vol. 36, nº 7, pp. 10367-10373, 2009. [19]D. J. Matich, «Redes Neuronales. Conceptos Básicos y Aplicaciones,» de Cátedra: Informática Aplicada ala Ingeniería de Procesos- Orientación I, 2001. [20]E. Acevedo M., A. Serna A. y E. Serna M., «Principios y Características de las Redes Neuronales Artificiales, » de Desarrollo e Innovación en Ingeniería, Medellín, Editorial Instituto Antioqueño de Investigación, 2017, pp. Capítulo 10, 173-182. [21]M. Hofmann y R. Klinkenberg, RapidMiner: Data mining use cases and business analytics applications, CRC Press, 2016. [22]R. Pupale, «Towards Data Science,» 2018. [En línea]. Disponible: https://towardsdatascience.com/https-medium-com-pupalerushikesh-svm-f4b42800e989. [23]F. H. Troncoso Espinosa, «Prediction of recidivismin thefts and burglaries using machine learning,» Indian Journal of Science and Technology, vol. 13, nº 6, pp. 696-711, 2020. [24]L. Tashman, «Out-of-sample tests of forecasting accuracy: an analysis and review,» International journal of forecasting, vol. 16, nº 4, pp. 437-450, 2000. [25]S. Varma y R. Simon, «Bias in error estimation when using cross-validation for model selection,» BMC bioinformatics, vol. 7, nº 1, p. 91, 2006. [26]N. V. Chawla, K. W. Bowyer, L. O. Hall y W. Kegelmeyer, «SMOTE: Synthetic Minority Over-sampling Technique,» Journal of Artificial Inteligence Research16, pp. 321-357, 2002. [27]M. Sokolova y G. Lapalme, «A systematic analysis of performance measures for classification tasks,» Information processing & management, vol. 45, nº 4, pp. 427-437, 2009. [28]S. Narkhede, «Understanding AUC-ROC Curve,» Towards Data Science, vol. 26, 2018. [29]R. Westermann y W. Hager, «Error Probabilities in Educational and Psychological Research,» Journal of Educational Statistics, Vol 11, No 2, pp. 117-146, 1986.

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Ni, Pin, Yuming Li, and Victor Chang. "Recommendation and Sentiment Analysis Based on Consumer Review and Rating." International Journal of Business Intelligence Research 11, no. 2 (2020): 11–27. http://dx.doi.org/10.4018/ijbir.2020070102.

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Accurate analysis and recommendation on products based on online reviews and rating data play an important role in precisely targeting suitable consumer segmentations and therefore can promote merchandise sales. This study uses a recommendation and sentiment classification model for analyzing the data of beer product based on online beer reviews and rating dataset of beer products and uses them to improve the recommendation performance of the recommendation model for different customer needs. Among them, the beer recommendation is based on rating data; 10 classification models are compared in text sentiment analysis, including the conventional machine learning models and deep learning models. Combining the two analyses can increase the credibility of the recommended beer and help increase beer sales. The experiment proves that this method can filter the products with more negative reviews in the recommendation algorithm and improve user acceptance.

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Chechliński, Łukasz, Barbara Siemiątkowska, and Michał Majewski. "A System for Weeds and Crops Identification—Reaching over 10 FPS on Raspberry Pi with the Usage of MobileNets, DenseNet and Custom Modifications." Sensors 19, no. 17 (2019): 3787. http://dx.doi.org/10.3390/s19173787.

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Automated weeding is an important research area in agrorobotics. Weeds can be removed mechanically or with the precise usage of herbicides. Deep Learning techniques achieved state of the art results in many computer vision tasks, however their deployment on low-cost mobile computers is still challenging. The described system contains several novelties, compared both with its previous version and related work. It is a part of a project of the automatic weeding machine, developed by the Warsaw University of Technology and MCMS Warka Ltd. Obtained models reach satisfying accuracy (detecting 47–67% of weed area, misclasifing as weed 0.1–0.9% of crop area) at over 10 FPS on the Raspberry Pi 3B+ computer. It was tested for four different plant species at different growth stadiums and lighting conditions. The system performing semantic segmentation is based on Convolutional Neural Networks. Its custom architecture combines U-Net, MobileNets, DenseNet and ResNet concepts. Amount of needed manual ground truth labels was significantly decreased by the usage of the knowledge distillation process, learning final model which mimics an ensemble of complex models on a large database of unlabeled data. Further decrease of the inference time was obtained by two custom modifications: in the usage of separable convolutions in DenseNet block and in the number of channels in each layer. In the authors’ opinion, the described novelties can be easily transferred to other agrorobotics tasks.

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Volkenandt, Tobias, Stefanie Freitag, and Michael Rauscher. "Machine Learning Powered Image Segmentation." Microscopy and Microanalysis 24, S1 (2018): 520–21. http://dx.doi.org/10.1017/s1431927618003094.

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Roy, Mrs T. L. Deepika. "Customer Behavior Analysis using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 945–48. http://dx.doi.org/10.22214/ijraset.2021.35180.

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RFM (Recency, Frequency, Monetary) investigation is a demonstrated showcasing model for conduct based client division. It groups clients dependent on their exchange history – how as of late, how frequently and what amount they buy.RFM helps partition clients into different classes or groups to distinguish clients who will react to advancements and how. This RFM examination depends on a blend of three boundaries. For instance, we can say that individuals who spend the most on items are our best clients. A large portion of us coincide and think about something very similar. In any case, Imagine a scenario in which they were bought just a single time. Or on the other hand an extremely quiet past? Consider the possibility that they are done utilizing our item. would they be able to in any case be viewed as your best clients? Most likely not. Making a decision about client esteem from only one perspective will give you a mistaken report of your client base and their lifetime. That is the reason, the RFM model joins three diverse clients ascribed to rank clients. In the event that they purchased in the recent past, they get higher focus. On the off chance that they purchase ordinarily, they get a higher score. What's more, on the off chance that they spend greater, they get more focus. Thus, we Combine these three scores to make the RFM score. At long last we can portion the client data set into various gatherings dependent on this RFM score.

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Xiahou, Xiancheng, and Yoshio Harada. "B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM." Journal of Theoretical and Applied Electronic Commerce Research 17, no. 2 (2022): 458–75. http://dx.doi.org/10.3390/jtaer17020024.

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Customer churn prediction is very important for e-commerce enterprises to formulate effective customer retention measures and implement successful marketing strategies. According to the characteristics of longitudinal timelines and multidimensional data variables of B2C e-commerce customers’ shopping behaviors, this paper proposes a loss prediction model based on the combination of k-means customer segmentation and support vector machine (SVM) prediction. The method divides customers into three categories and determines the core customer groups. The support vector machine and logistic regression were compared to predict customer churn. The results show that each prediction index after customer segmentation was significantly improved, which proves that k-means clustering segmentation is necessary. The accuracy of the SVM prediction was higher than that of the logistic regression prediction. These research results have significance for customer relationship management of B2C e-commerce enterprises.

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Angle, Sachi, B. Ashwath Rao, and S. N. Muralikrishna. "Kannada morpheme segmentation using machine learning." International Journal of Engineering & Technology 7, no. 2.31 (2018): 45. http://dx.doi.org/10.14419/ijet.v7i2.31.13395.

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This paper addresses and targets morpheme segmentation of Kannada words using supervised classification. We have used manually annotated Kannada treebank corpus, which is recently developed by us. Kannada bears resemblance to other Dravidian languages in morphological structure. It is an agglutinative language, hence its words have complex morphological form with each word comprising of a root and an optional set of suffixes. These suffixes carry additional meaning, apart from the root word in a context. This paper discusses the extraction of morphemes of a word by using Support Vector Machines for Classification. Additional features representing the properties of the Kannada words were extracted and the different letters were classified into labels that result in the morphological segmentation of the word. Various methods for evaluation were considered and an accuracy of 85.97% was achieved.

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Florez-Lopez, Raquel, and Juan Manuel Ramon-Jeronimo. "Marketing Segmentation Through Machine Learning Models." Social Science Computer Review 27, no. 1 (2008): 96–117. http://dx.doi.org/10.1177/0894439308321592.

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Mundhe, Shivani. "Image Segmentation using Adaptive Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 5 (2021): 948–50. http://dx.doi.org/10.22214/ijraset.2021.34383.

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Kumar, S. Likhit. "Bank Customer Churn Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (2021): 727–32. http://dx.doi.org/10.22214/ijraset.2021.37467.

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Banking is one of the highly competitive sectors where customer relations is of utmost importance for any bank. Each customer is considered as a customer for life by the banks. The term “Customer Churn” refers to the state in which the customer or the subscriber stops involving in business transactions with a company or a service provider. To deal with this, the paper presents the work done towards predicting the customer churn rate, using machine learning models which will indicate whether a customer will leave the bank or not based on many factors, this in turn will help the bank in knowing which category of customers generally tend to leave the bank. Further the banks can bring in exciting offers so that it can retain its customers. In this predictive process popular models such as logistic regression, decision trees, random forest and other boosting techniques have been used to achieve a decent level of accuracy, for the banks to rely upon.

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SriLaxmi, K. "Credit Card Customer Predicting using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (2020): 2697–701. http://dx.doi.org/10.22214/ijraset.2020.5452.

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Kim, Ho Sung, and Young Ran Lee. "Segmentation of Brain CT Image Machine Learning." Journal of Korean Society of Medical Informatics 3, no. 2 (1997): 193. http://dx.doi.org/10.4258/jksmi.1997.3.2.193.

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Rueckert, D. "SP-0354: Machine learning for image segmentation." Radiotherapy and Oncology 127 (April 2018): S183. http://dx.doi.org/10.1016/s0167-8140(18)30664-9.

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Suresh. "Dermoscopic Image Segmentation using Machine Learning Algorithm." American Journal of Applied Sciences 8, no. 11 (2011): 1159–68. http://dx.doi.org/10.3844/ajassp.2011.1159.1168.

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Amnur, Hidra. "Customer Relationship Management and Machine Learning Technology for Identifying the Customer." JOIV : International Journal on Informatics Visualization 1, no. 1 (2017): 12. http://dx.doi.org/10.30630/joiv.1.1.10.

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Customer Relationship Management needed for the company to know their customer more closed, and make two-way communication between company and customer. in CRM solutions are multi-criteria decision-making analysis tools that do not require prior assumptions to explore the weights and performances among project risk, project management and organization performance, based on research framework of stimulus-organism response model. in this study, Machine learning with Support Vector Machine algorithm is currently for classification task due to its ability to model nonlinearities CRM Solutions. With Machine Learning and CRM, Bank X optimize their profit, with manage their more benefit customer or find a new customer or get their lost potential customer back.

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Mukherjee, Sudhanshu. "Predicting Malignant Cancer Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 1668–72. http://dx.doi.org/10.22214/ijraset.2021.39078.

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Abstract: One of the primary concerns that is also a demanding issue within the realm of medical specialism is the detection and removal of tumours. Because visualisation approaches had the drawback of being adversarial, doctors relied heavily on MRI images to provide a superior result. Pre-processing, tumour segmentation, and tumour operations are the three stages in which tumour image processing takes place. Following the acquisition of the source image, the original image is converted to grayscale. Additionally, a noise removal filter and a median filter for quality development are provided, followed by an exploration stage that yields hits orgasmic identical images. Finally, the watershed algorithm is used to complete the segmentation. This proposed methodology is useful in automatically organising reports in a short amount of time, and exploration has resulted in the removal of many less tumour parameters. Keywords: MRI Imaging, Segmentation, Watershed Algorithm.

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Pal, Subhabaha, and Sampa Pal. "Machine Learning Application in Analyzing Online Customer Journey." Indian Journal of Computer Science 4, no. 3 (2019): 11. http://dx.doi.org/10.17010/ijcs/2019/v4/i3/146161.

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Wong, Ann-Nee, and Booma Poolan Marikannan. "Optimising e-commerce customer satisfaction with machine learning." Journal of Physics: Conference Series 1712 (December 2020): 012044. http://dx.doi.org/10.1088/1742-6596/1712/1/012044.

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Lyutov, Alexey, Yilmaz Uygun, and Marc-Thorsten Hütt. "Managing workflow of customer requirements using machine learning." Computers in Industry 109 (August 2019): 215–25. http://dx.doi.org/10.1016/j.compind.2019.04.010.

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Noori, Behrooz. "Classification of Customer Reviews Using Machine Learning Algorithms." Applied Artificial Intelligence 35, no. 8 (2021): 567–88. http://dx.doi.org/10.1080/08839514.2021.1922843.

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Vohra, Sumit K., and Dimiter Prodanov. "The Active Segmentation Platform for Microscopic Image Classification and Segmentation." Brain Sciences 11, no. 12 (2021): 1645. http://dx.doi.org/10.3390/brainsci11121645.

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Image segmentation still represents an active area of research since no universal solution can be identified. Traditional image segmentation algorithms are problem-specific and limited in scope. On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. However, machine learning only can not address the question as to which features are appropriate for a certain classification problem. The article presents an automated image segmentation and classification platform, called Active Segmentation, which is based on ImageJ. The platform integrates expert domain knowledge, providing partial ground truth, with geometrical feature extraction based on multi-scale signal processing combined with machine learning. The approach in image segmentation is exemplified on the ISBI 2012 image segmentation challenge data set. As a second application we demonstrate whole image classification functionality based on the same principles. The approach is exemplified using the HeLa and HEp-2 data sets. Obtained results indicate that feature space enrichment properly balanced with feature selection functionality can achieve performance comparable to deep learning approaches. In summary, differential geometry can substantially improve the outcome of machine learning since it can enrich the underlying feature space with new geometrical invariant objects.

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Mikhailov, E. V., and S. V. Sai. "Machine learning for forest segmentation in satellite images." Proceedings of Tomsk State University of Control Systems and Radioelectronics 20, no. 1 (2017): 89–92. http://dx.doi.org/10.21293/1818-0442-2017-20-1-89-92.

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ElSebely, Randa, Bassem Abdullah, Ashraf A. Salem, and Ahmed Hassan Yousef. "Multiple Sclerosis Lesion Segmentation Using Ensemble Machine Learning." Saudi Journal of Engineering and Technology 05, no. 04 (2020): 134–43. http://dx.doi.org/10.36348/sjet.2020.v05i04.002.

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Xu, Yuan, Yuxin Wang, Jie Yuan, Qian Cheng, Xueding Wang, and Paul L. Carson. "Medical breast ultrasound image segmentation by machine learning." Ultrasonics 91 (January 2019): 1–9. http://dx.doi.org/10.1016/j.ultras.2018.07.006.

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