COMBINING VISUAL CUSTOMER SEGMENTATION AND RESPONSE MODELING

Customer Relationship Management (CRM) is a central part of Business Intelligence and sales campaigns are often used for improving customer relationships. This paper explores customer behavior during sales campaigns. We provide a visual, data-driven and efficient framework for customer segmentation and campaign-response modeling. First, the customers are grouped by purchasing behavior characteristics using a self-organizing map. To this behavioral segmentation model, we link segment migration patterns using feature plane representations. This enables visual monitoring of the customer base and tracking customer behavior before and during sales campaigns. In addition to the general segment migration patterns, this method provides the capability to drill down into each segment to visually explore the dynamics. The framework is applied to a department store chain with more than one million customers

Delineating the Business Value of Data-driven Initiatives in Organizations – Findings from a Systematic Review of the Information Systems Literature

A key objective of data-driven transformations is to utilize big data analytics (BDA) to create data-driven business value (DDBV). While prior research shows the potential of BDA to achieve DDBV, the concept remains blurry and an overview of realizable DDBVs is still lacking. To better understand the multidimensionality of the DDBV concept and to obtain insights into the bandwidth of achievable DDBVs, we conducted a systematic review of the information systems literature. Based on our results, we present a comprehensive overview of 34 DDBVs, which are classified according to their tangibility and locus of value realization. Furthermore, we describe three research deficiencies: (1) the missing operationalization of the DDBV concept, (2) the lack of explanatory mechanisms for DDBV realization, and (3) missing qualitative, in-depth insights into DDBV realization processes. Future research may build upon our systematization and help closing these research gaps, thereby increasing the success likelihood of data-driven initiatives

Adaptive Cooperative Learning Methodology for Oil Spillage Pattern Clustering and Prediction

The serious environmental, economic and social consequences of oil spillages could devastate any nation of the world. Notable aftermath of this effect include loss of (or serious threat to) lives, huge financial losses, and colossal damage to the ecosystem. Hence, understanding the pattern and  making precise predictions in real time is required (as opposed to existing rough and discrete prediction) to give decision makers a more realistic picture of environment. This paper seeks to address this problem by exploiting oil spillage features with sets of collected data of oil spillage scenarios. The proposed system integrates three state-of-the-art tools: self organizing maps, (SOM), ensembles of deep neural network (k-DNN) and adaptive neuro-fuzzy inference system (ANFIS). It begins with unsupervised learning using SOM, where four natural clusters were discovered and used in making the data suitable for classification and prediction (supervised learning) by ensembles of k-DNN and ANFIS. Results obtained showed the significant classification and prediction improvements, which is largely attributed to the hybrid learning approach, ensemble learning and cognitive reasoning capabilities. However, optimization of k-DNN structure and weights would be needed for speed enhancement. The system would provide a means of understanding the nature, type and severity of oil spillages thereby facilitating a rapid response to impending oils spillages. Keywords: SOM, ANFIS, Fuzzy Logic, Neural Network, Oil Spillage, Ensemble Learnin

How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation

What algorithm to choose for customer segmentation? Should you use one algorithm or many? How many customer segments should you create? How to evaluate the results? In this research, we carry out a systematic literature review to address such central questions in customer segmentation research and practice. The results from extracting information from 172 relevant articles show that algorithmic customer segmentation is the predominant approach for customer segmentation. We found researchers employing 46 different algorithms and 14 different evaluation metrics. For the algorithms, K-means clustering is the most employed. For the metrics, separation-focused metrics are slightly more prevalent than statistics-focused metrics. However, extant studies rarely use domain experts in evaluating the outcomes. Out of the 169 studies that provided details about hyperparameters, more than four out of five used segment size as their only hyperparameter. Typically, studies generate four segments, although the maximum number rarely exceeds twenty, and in most cases, is less than ten. Based on these findings, we propose seven key goals and three practical implications to enhance customer segmentation research and application.© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification

Maintaining healthy organization-customers relationship has positive influence on customers’ behavioral tendencies as regards preference to products and services, buying behavior, loyalty, satisfaction, and so on. To achieve this, an in-depth analysis of customers’ characteristics and purchasing behavioral trend is required. This paper proposes a hybrid unsupervised learning framework consisting of k-means algorithm and self-organizing maps (SOMs) for customer segmentation and behavior analysis. K-means algorithm was used to partition the entire input space of customers’ transaction dataset into 3 and 4 disjoint segments based on customers’ frequency (F) and monetary value (MV). SOM provided visualization of the underlying clusters and discovered customers’ relationships in the dataset. Interaction of F and MV clusters resulted in 12 sub-clusters. An in-depth analysis of each sub-cluster was also performed and appropriate customer relationship management (CRM) strategies established for each sub-cluster. Discovered knowledge will guide effective allocation of resources to each customer cluster and other organizational decision support functions much required by CRM systems. Keywords: customer relationship, data mining, k-means, pattern recognition, self organizing ma

Segmentação de clientes com base no seu valor e comportamento migracional : estágio PT Comunicações

Mestrado em Gestão de Sistemas de InformaçãoA indústria das telecomunicações é caracterizada pela rápida mudança de ofertas, existindo uma fácil mudança entre operadoras por parte dos clientes. Por conseguinte, é um mercado de forte concorrência onde existe uma forte guerra de preços. Assim, a retenção de clientes é muito importante para as empresas do mercado das telecomunicações, de modo a que estas mantenham os seus clientes e não os percam para as empresas concorrentes. O presente relatório apresenta a descrição das atividades desenvolvidas durante o estágio realizado na direção segmento de consumo residencial, na área de planeamento e projetos estratégicos da PT Comunicações, S.A., o qual durou 4 meses, de 21/04/2014 até 28/08/2014. As atividades desenvolvidas foram a segmentação dos clientes do mercado residencial com base no seu valor para a empresa e no seu comportamento migracional. Os principais objetivos foram pela criação e disponibilização de uma ferramenta de identificação e monitorização de todos os movimentos de clientes, de acordo com a sua subscrição de produtos e serviços. Para alcançar estes objetivos foram analisados dados de faturação de clientes, através da ferramenta SQL Server. De acordo com os objetivo traçados, o estágio permitiu ao estagiário desenvolver as suas competências nas temáticas do SQL, segmentação de clientes e CRM. Numa perspetiva de resultados, procurou-se identificar os principais movimentos de clientes, classificando-os e com isso poder tirar conclusões sobre relações entre a oferta e a procura. Estes resultados permitirão fazer ajustes a nível das ofertas da empresa de modo a responder à procura.The telecommunications industry is known for quick changes of offers and strong competition, making it easy to customers switch from one company to another. So, it´s a market of strong competition where there is a fierce war of prices. Thus, retaining the customers is very important to the companies in the telecommunications industry, in such a way that they retain their customers and don´t lose them to competitors. This report presents the description of the activities developed during the internship that took place in the direction segment of residential consuming, in the area of planning and strategically projects of "PT Comunicações, S.A." which lasted four months, from 21/04/2014 to 28/08/2014. The activities developed were focused on the customer segmentation of residential market based on their value to the company and their migration behavior. The main aims were creating and making available a tool to identify and monitor all customers´ movements, according to their subscription of products and services. To reach these objectives was analyzed data from the customers' bills, through the SQL Server tool. According to the established aims, the internship allowed the intern to develop his skills in the SQL, customer segmentation and CRM areas. As far as results are concerned, our effort was to identify the main movements of customers, classifying them and with that being able to reach conclusions over the connections between the supply and the demand. These results will allow an adjustment in what the company offers so that it can answer to the demand

A multi-attribute data mining model for rule extraction and service operations benchmarking

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose Customer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking. Design/methodology/approach The authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns. Findings As a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed. Originality/value The authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry