A model to improve management of banking customers

Purpose – The purpose of this study is to provide a model to assess and classify banking customers based on the concept of Customer Lifetime Value (CLV) in order to determine which kind of customers creates more value to the bank. Design/methodology/approach – The proposed model comprises two sub-models: (sub-model 1) modelling and prediction of CLV in a multiproduct context using Hierarchical Bayesian models as input to (sub-model 2) a value-based segmentation specially designed to manage customers and products using the Latent Class regression. The model is tested using real transaction data of 1,357 randomly-selected customers of a bank. Findings – This research demonstrates which drivers of customer value better predict the contribution margin and product usage for each of the products considered in order to get the CLV measure. Using this measure, the model implements a value-based segmentation, which helps banks to facilitate the process of customer management. Originality/value – Previous CLV models are mostly conceptual, generalization is one of their main concerns, are usually focused on single product categories, and they are not design with a special emphasis on their application as support for managerial decisions. In response to these drawbacks, the proposed model will enable decision-makers to improve the understanding of the value of each customer and their behaviour towards different financial products

Identificiranje relevantnih segmenata potencijalnih korisnika chatbota u bankarstvu na temelju ponašanja pri prihvaćanju tehnologije

Purpose – Chatbot technology is expected to revolutionize customer service in financial institutions. However, the adoption of customer service chatbots in banking remains low. Therefore, the aim of this paper is to identify relevant segments of potential banking chatbot users based on technology adoption behavior. Design/Methodology/Approach – Data for the research was collected through an online questionnaire in Romania using the non-probability sampling method. The 287 questionnaires were analyzed using hierarchical and k-means cluster analysis. Findings and implications – The analysis revealed three distinct segments: Innovators (26%), consisting of highly educated young women employed in the business sector; the Late Majority (55%), consisting of young women with higher education degrees who work in services-related fields; and Laggards (19%), consisting of educated middle-aged men employed in the business sector. New significant differences among demographic and banking behavior variables were observed across the profiles of potential banking chatbot user segments. Limitations – The study is based on a non-probability sample collected from only one country, with a rather small sample size. Originality – Technology acceptance variables (perceived usefulness, perceived ease of use), expanded to include constructs such as awareness of service, perceived privacy risk, and perceived compatibility, were found to be appropriate for customer segmentation purposes in the context of chatbot applications based on artificial intelligence. The study also revealed a new innovator demographic profile.Svrha – Očekuje se da će chatbot tehnologija revolucionirati usluge korisnicima u financijskim institucijama. Međutim prihvaćenost chatbota među korisnicima usluga banaka još je uvijek niska. Stoga je cilj ovog rada identificirati relevantne segmente potencijalnih korisnika bankovnih chatbotova na temelju ponašanja pri usvajanju tehnologije. Metodološki pristup – Podatci su prikupljeni u Rumunjskoj ne temelju neprobabilističke metode uzorkovanja putem online anketnog upitnika. Analizirano je 287 anketnih upitnika primjenom hijerarhijske i k-mean klasterske analize. Rezultati i implikacije – Analizom su otkrivena tri različita segmenta: Inovatori (26%) koji su visokoobrazovani, mlade žene zaposlene u području poslovne ekonomije; Kasna većina (55%) koju čine mlađe žene s višom stručnom spremom zaposlene u područjima povezanim s uslugama; Kolebljivci (19%) koji su obrazovani, muškarci srednjih godina zaposleni u području poslovne ekonomije. Otkrivene su nove značajne razlike među profilima segmenata potencijalnih korisnika chatbota u bankarstvu vezane uz demografske te varijable ponašanja korisnika usluga u bankarstvu. Ograničenja – Istraživanje se temelji na nepobabilističkom uzorku prikupljenom u samo jednoj zemlji, a veličina uzorka je prilično mala. Doprinos – Utvrđeno je da su varijable prihvaćanja tehnologije (percipirana korisnost, percipirana jednostavnost korištenja) proširene s konstruktima kao što su svjesnost o usluzi, percipirani rizik privatnosti i percipirana kompatibilnost, prikladne za potrebe segmentacije korisnika u kontekstu chatbot aplikacija temeljenih na umjetnoj inteligenciji. Istraživanje je otkrilo novi demografski profil inovatora

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

A model to improve management of banking customers

Purpose – The purpose of this study is to provide a model to assess and classify banking customers based on the concept of Customer Lifetime Value (CLV) in order to determine which kind of customers creates more value to the bank. Design/methodology/approach – The proposed model comprises two sub-models: (sub-model 1) modelling and prediction of CLV in a multiproduct context using Hierarchical Bayesian models as input to (sub-model 2) a value-based segmentation specially designed to manage customers and products using the Latent Class regression. The model is tested using real transaction data of 1,357 randomly-selected customers of a bank. Findings – This research demonstrates which drivers of customer value better predict the contribution margin and product usage for each of the products considered in order to get the CLV measure. Using this measure, the model implements a value-based segmentation, which helps banks to facilitate the process of customer management. Originality/value – Previous CLV models are mostly conceptual, generalization is one of their main concerns, are usually focused on single product categories, and they are not design with a special emphasis on their application as support for managerial decisions. In response to these drawbacks, the proposed model will enable decision-makers to improve the understanding of the value of each customer and their behaviour towards different financial products