Using Data Analytics for Customers Segmentation: Experimental Study at a Financial Institution

Gončarovs, Pāvels

doi:10.1109/itms.2018.8552951

Cited by 5 publications

(2 citation statements)

References 1 publication

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“…The articles are collected mainly related to customer segmentation, which is a common customer analytics technique and a traditional concept in marketing [5]. Customer analytics models are frequently used in research publications to aid decision-making and are applied to typical case studies: in SMEs [6], in the retail industry [4,7,8], or in the financial institutions [9],… Lu et al [6] developed a process model for data-driven decision-making that will serve as an overarching framework for critical business analytics life cycle stages. Visual analytics has informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams.…”

Section: Customer Analyticsmentioning

confidence: 99%

Customer Analytic in Vietnamese e-Commerce Firms: Status Quo and Its Influences on Firm Performance

Ngoc¹,

Thi²

2023

Advances in Economics, Business and Management Research

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This study presents the current situation of customer analysis of ecommerce enterprises in Vietnam. In addition, this study investigates the quantitative relationship between customer analytics adoption, innovation, and firm performance. We built a research model based on the literature review that claims that consumer analytics adoption not only has a favorable association with firm performance but also influences innovation, which leads to firm performance. After that, the authors tested the model using survey data from 154 Vietnamese e-commerce companies. The results show positive relationships between (i) applying customer analytics and innovation and (ii) innovation and firm performance. Innovation plays a mediating role in the relationship between customer analytics and firm performance. The findings encourage businesses to have a more perspective on the importance of working with customer data in e-commerce, promoting infrastructure upgrades, professional skills to quickly harness the full benefits of data analysis for decision support.

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Section: Customer Analyticsmentioning

confidence: 99%

Customer Analytic in Vietnamese e-Commerce Firms: Status Quo and Its Influences on Firm Performance

Ngoc¹,

Thi²

2023

Advances in Economics, Business and Management Research

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“…Some studies have been conducted on customer clustering and segmentation based on customer behavioral perspectives, customer behavioral factors, demographic factors, and environmental objects [32][33][34][35][36][37][38]. Goncarovs [39] described a five-step customer segmentation method consisting of gathering quantitative information, creating specific microsegments, sorting microsegments, and creating final customer segments. Artificial intelligence and machine learning play an important role in identifying investment patterns for banking innovations, with the addition of risk capital and other emerging technologies also considered by scientists [40].…”

Section: Edmondson and Mcmanusmentioning

confidence: 99%

Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis

2021

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Identifying investment patterns as part of customer segmentation is one of the most important tasks in retail banking. Clustering customers effectively is an important element of improving marketing policy and strategic planning. There are several methods for identifying similar groups of customers and describing their characteristics to offer them appropriate products. However, using machine learning methods is rare, and the application is limited for certain types of data. The aim of this study is to investigate the benefits of using a two-stage clustering method using neural-network-based Kohonen self-organizing maps followed by hierarchical clustering for identifying the investment patterns of potential retail banking customers. The unique benefit of this method is the ability to use both categorical and numerical variables at the same time. This research examined 1,542 responses received for an online investment survey, focusing on the questions that are related to the respondents’ investment preferences and their current financial assets. The research utilizes descriptive statistics and multiple correspondence analysis (MCA) to understand the variables and Kohonen self-organizing maps (SOMs), in combination with hierarchical clustering, to identify customer groups and describe the characteristics of these clusters. The analysis was able to identify clusters of potential customers with similar preferences and gained insights into their investment patterns related to their investment portfolio and investment behavior, including their savings profile, attitude to risk-taking, and preferences for investment advice. These findings were supported by additional insights through the application of multiple correspondence analysis (MCA) describing patterns of financial instruments and portfolios. The main contribution of the research is the combined application of the machine learning methods Kohonen SOM, hierarchical clustering, and MCA for investment pattern analysis in the retail banking business.

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Behavior Segmentation based Micro-Segmentation Approach for Health Insurance Industry

Nandapala

Jayasena

Rathnayaka

2020

2020 2nd International Conference on Advancements in Computing (ICAC)

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Using Data Analytics for Customers Segmentation: Experimental Study at a Financial Institution

Cited by 5 publications

References 1 publication

Customer Analytic in Vietnamese e-Commerce Firms: Status Quo and Its Influences on Firm Performance

Customer Analytic in Vietnamese e-Commerce Firms: Status Quo and Its Influences on Firm Performance

Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis

Behavior Segmentation based Micro-Segmentation Approach for Health Insurance Industry

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