RFMC: a spending-category segmentation

Allegue, Sahar; Abdellatif, Takoua; Bannour, Khalil

doi:10.1109/wetice49692.2020.00040

Cited by 8 publications

(12 citation statements)

References 36 publications

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“…This work extends our previous work 4 where we introduced a first version of the categorization engine as well as a first version of the segmentation service using categorization. In the previous work, the SBM classified user transactions following a predefined bank list of categories while in this article's work, SBM opens the room to the users to introduce and update these categories with their personalized ones to have a personalized user experience.…”

Section: Introductionmentioning

confidence: 59%

“…For example, the RFMC segmentation (recency, frequency, monetary, category) technique is a new segmentation service that extends the classical RFM segmentation method by considering the category. 4 In this article, the segmentation service is based on the new version of SBM where categories are introduced by users and where there are subcategories. Visualization is another application that provides dashboards to bank agents showing clients' spending behaviors, trends, and so on.…”

Section: The Bank Applicationsmentioning

confidence: 99%

“…Therefore, the NLU component relies on NLU techniques. The bank applications : This layer contains banking smart services and uses cases. For example, the RFMC segmentation (recency, frequency, monetary, category) technique is a new segmentation service that extends the classical RFM segmentation method by considering the category 4 . In this article, the segmentation service is based on the new version of SBM where categories are introduced by users and where there are subcategories.…”

Section: Sbm Architecturementioning

confidence: 99%

“…Besides, the rise of open banking is ready, with open doors for adopting new applications and solutions for analyzing account data and converts it into information relevant for decision making since raw data on its own does not provide actionable insights. Data mining and analytics have been successfully applied to banking in various use‐cases such as analyzing customer attrition and retention, 1 loan and credit scoring, 2,3 customer segmentation, 4,5 and fraud detection 6 . Nevertheless, the absent ingredient enabling fintech solutions to improve raw account information is a transaction categorization engine because it is about far more than keeping banking transactions neat and tidy in the personalized platform.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU

Allegue

Abdellatif

Abed

2021

Concurrency and Computation

Self Cite

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New business models underpinned by new standards of openness, flexibility, and agility are arising in the banking sector. Therefore, banks have to establish new strategies to keep and to extend their client base, especially with the explosive growth of customers' data and interaction touch-points. Hence, banks and fintechs are in fierce competition to transform customers' data that incorporate their transactions into pertinent and significant knowledge for decision making. In this article, we present a novel system aimed toward solving a long-standing and challenging issue: obtaining classifiers to automatically categorize bank transactions for a Smart Budget Manager. We fit and test our system using real data. The strength of our system lies in the novel combination of incremental machine learning algorithms with a natural language understanding for a fine-grained categorization of bank transactions. Our system serves as a base layer for other advanced banking applications such as segmentation, next best offers and credit scoring. Our system is deployed in a real banking application of a Tunisian bank and has shown bank and customer's satisfaction.

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Section: Introductionmentioning

confidence: 59%

Section: The Bank Applicationsmentioning

confidence: 99%

Section: Sbm Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU

Allegue

Abdellatif

Abed

2021

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…RFM segmentation is an effective method to identify customer groups that are treated specially [19]. In this project, we conduct segmentation on customers with purchase experience.…”

Section: Customer Segmentationmentioning

confidence: 99%

Sales Prediction and Product Recommendation Model Through User Behavior Analytics

Zhao¹,

Keikhosrokiani²

2022

Computers, Materials &Amp; Continua

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The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down. The general public has responded to call of the government to stay at home. Offline retail stores have been severely affected. Therefore, in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience, this study aims to utilize historical sales data for exploring, building sales prediction and recommendation models. A novel data science life-cycle and process model with Recency, Frequency, and Monetary (RFM) analysis method with the combination of various analytics algorithms are utilized in this study for sales prediction and product recommendation through user behavior analytics. RFM analysis method is utilized for segmenting customer levels in the company to identify the importance of each level. For the purchase prediction model, XGBoost and Random Forest machine learning algorithms are used to build prediction models and 5-fold Cross-Validation method is utilized to evaluate their. For the product recommendation model, the association rules theory and Apriori algorithm are used to complete basket analysis and recommend products according to the outcomes. Moreover, some suggestions are proposed for the marketing department according to the outcomes. Overall, the XGBoost model achieved better performance and better accuracy with F1-score around 0.789. The proposed recommendation model provides good recommendation results and sales combinations for improving sales and market responsiveness. Furthermore, it recommend specific products to new customers. This study offered a very practical and useful business transformation case that assists companies in similar situations to transform their business models.

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Customer Segmentation using Machine Learning

Jaiswal

Supraja

et al. 2023

2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)

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Customer segmentation has seen major growth in all sectors in the last decade. Several techniques have been devised to analyze customer behavior through loyalty, purchases, recency, frequency and monetary to develop efficient marketing strategies that cater to each client individually. As the availability of products and services increases, so does the competition. With the spiraling of automation accompanied by its cost-effectiveness and ease of availability, all businesses equip themselves with the required workforce and machinery to conduct experiments such as customer segmentation on an industrial scale. In the proposed work, the datasets are manipulated by extracting features from existing attributes. A widespread approach is RFM that calculates the Recency, Frequency and Monetary values for each customer tuple. This paper aims at laying out a new approach at every step of customer segmentation from pre-processing, clustering, validation and suggesting marketing strategies for customer retention. Two datasets-Online Retail II Set and Mall Customer Segmentation are modelled and the results from analysis of both the datasets are presented and compared to reach a generalized opinion.

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RFMC: a spending-category segmentation

Cited by 8 publications

References 36 publications

SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU

SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU

Sales Prediction and Product Recommendation Model Through User Behavior Analytics

Customer Segmentation using Machine Learning

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