1,003 research outputs found
Analytical customer relationship management in retailing supported by data mining techniques
Tese de doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201
Feature selection strategies for improving data-driven decision support in bank telemarketing
The usage of data mining techniques to unveil previously undiscovered knowledge has
been applied in past years to a wide number of domains, including banking and marketing. Raw
data is the basic ingredient for successfully detecting interesting patterns. A key aspect of raw
data manipulation is feature engineering and it is related with the correct characterization or
selection of relevant features (or variables) that conceal relations with the target goal.
This study is particularly focused on feature engineering, aiming at the unfolding
features that best characterize the problem of selling long-term bank deposits through
telemarketing campaigns. For the experimental setup, a case-study from a Portuguese bank,
ranging the 2008-2013 year period and encompassing the recent global financial crisis, was
addressed. To assess the relevance of such problem, a novel literature analysis using text
mining and the latent Dirichlet allocation algorithm was conducted, confirming the existence of a
research gap for bank telemarketing.
Starting from a dataset containing typical telemarketing contacts and client information,
research followed three different and complementary strategies: first, by enriching the dataset
with social and economic context features; then, by including customer lifetime value related
features; finally, by applying a divide and conquer strategy for splitting the problem in smaller
fractions, leading to optimized sub-problems. Each of the three approaches improved previous
results in terms of model metrics related to prediction performance. The relevance of the
proposed features was evaluated, confirming the obtained models as credible and valuable for
telemarketing campaign managers.A utilização de técnicas de data mining para a descoberta de conhecimento tem sido
aplicada nos últimos anos a uma grande variedade de domÃnios, incluindo banca e marketing.
Os dados no seu estado primitivo constituem o ingrediente básico para a deteção de padrões
de informação. Um aspeto chave da manipulação de dados em bruto consiste na "engenharia
de atributos", que compreende uma correta definição e seleção de atributos relevantes (ou
variáveis) que se relacionem com o alvo da descoberta de conhecimento.
Este trabalho foca-se numa abordagem de "engenharia de atributos" para definir as
variáveis que melhor caraterizam o problema de vender depósitos bancários a prazo através de
campanhas de telemarketing. Sendo um estudo empÃrico, foi utilizado um caso de estudo de
um banco português, abrangendo o perÃodo 2008-2013, que inclui os efeitos da crise financeira
internacional. Para aferir da importância deste problema, foi realizada uma inovadora análise
da literatura recorrendo a text mining e ao algoritmo latent Dirichlet allocation, confirmando a
existência de uma lacuna nesta matéria.
Utilizando como base um conjunto de dados de contactos de telemarketing e
informação sobre os clientes, três estratégias diferentes e complementares foram propostas:
primeiro, os dados foram enriquecidos com atributos socioeconómicos; posteriormente, foram
adicionadas caracterÃsticas associadas ao valor do cliente ao longo do seu tempo de vida;
finalmente, o problema foi dividido em problemas mais especÃficos, permitindo abordagens
otimizadas a cada subproblema. Cada abordagem melhorou as métricas associadas Ã
capacidade preditiva do modelo. Adicionalmente, a relevância dos atributos foi avaliada,
confirmando os modelos obtidos como credÃveis e valiosos para gestores de campanhas de telemarketing
A review of natural language processing in contact centre automation
Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco
Exploration of customer churn routes using machine learning probabilistic models
The ongoing processes of globalization and deregulation are changing the competitive framework in the majority of economic sectors. The appearance of new competitors and technologies entails a sharp increase in competition and a growing preoccupation among service providing companies with creating stronger bonds with customers. Many of these companies are shifting resources away from the goal of capturing new customers and are instead focusing on retaining existing ones. In this context, anticipating the customer¿s intention to abandon, a phenomenon also known as churn, and facilitating the launch of retention-focused actions represent clear elements of competitive advantage.
Data mining, as applied to market surveyed information, can provide assistance to churn management processes. In this thesis, we mine real market data for churn analysis, placing a strong emphasis on the applicability and interpretability of the results. Statistical Machine Learning models for simultaneous data clustering and visualization lay the foundations for the analyses, which yield an interpretable segmentation of the surveyed markets. To achieve interpretability, much attention is paid to the intuitive visualization of the experimental results. Given that the modelling techniques under consideration are nonlinear in nature, this represents a non-trivial challenge. Newly developed techniques for data visualization in nonlinear latent models are presented. They are inspired in geographical representation methods and suited to both static and dynamic data representation
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Agent based modelling and simulation: An examination of customer retention in the UK mobile market
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Customer retention is an important issue for any business, especially in mature markets such as the UK mobile market where new customers can only be acquired from competitors. Different methods and techniques have been used to investigate customer retention including statistical methods and data mining. However, due to the increasing complexity of the mobile market, the effectiveness of these techniques is questionable. This study proposes Agent-Based Modelling and Simulation (ABMS) as a novel approach to investigate customer retention. ABMS is an emerging means of simulating behaviour and examining behavioural consequences. In outline, agents represent customers and agent relationships represent processes of agent interaction. This study follows the design science paradigm to build and evaluate a generic, reusable, agent-based (CubSim) model to examine the factors affecting customer retention based on data extracted from a UK mobile operator. Based on these data, two data mining models are built to gain a better understanding of the problem domain and to identify the main limitations of data mining. This is followed by two interrelated development cycles: (1) Build the CubSim model, starting with modelling customer interaction with the market, including interaction with the service provider and other competing operators in the market; and (2) Extend the CubSim model by incorporating interaction among customers. The key contribution of this study lies in using ABMS to identify and model the key factors that affect customer retention simultaneously and jointly. In this manner, the CubSim model is better suited to account for the dynamics of customer churn behaviour in the UK mobile market than all other existing models. Another important contribution of this study is that it provides an empirical, actionable insight on customer retention. In particular, and most interestingly, the experimental results show that applying a mixed customer retention strategy targeting both high value customers and customers with a large personal network outperforms the traditional customer retention strategies, which focuses only on the customer‘s value.This work is funded by the Brunel Department of Information Systems and Computing (DISC
Automated Machine Learning implementation framework in the banking sector
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsAutomated Machine Learning is a subject in the Machine Learning field, designed to give the possibility
of Machine Learning use to non-expert users, it aroused from the lack of subject matter experts, trying
to remove humans from these topic implementations. The advantages behind automated machine
learning are leaning towards the removal of human implementation, fastening the machine learning
deployment speed. The organizations will benefit from effective solutions benchmarking and
validations. The use of an automated machine learning implementation framework can deeply
transform an organization adding value to the business by freeing the subject matter experts of the
low-level machine learning projects, letting them focus on high level projects. This will also help the
organization reach new competence, customization, and decision-making levels in a higher analytical
maturity level.
This work pretends, firstly to investigate the impact and benefits automated machine learning
implementation in the banking sector, and afterwards develop an implementation framework that
could be used by banking institutions as a guideline for the automated machine learning
implementation through their departments. The autoML advantages and benefits are evaluated
regarding business value and competitive advantage and it is presented the implementation in a
fictitious institution, considering all the need steps and the possible setbacks that could arise.
Banking institutions, in their business have different business processes, and since most of them are
old institutions, the main concerns are related with the automating their business process, improving
their analytical maturity and sensibilizing their workforce to the benefits of the implementation of new
forms of work. To proceed to a successful implementation plan should be known the institution
particularities, adapt to them and ensured the sensibilization of the workforce and management to
the investments that need to be made and the changes in all levels of their organizational work that
will come from that, that will lead to a lot of facilities in everyone’s daily work
Modelling partial customer churn in the Portuguese fixed telecommunications industry by using survival models
Considering that profits from customer relationships are the lifeblood of firms (Grant and
Schlesinger, 1995), an improvement on the customer management is essential to ensure the
competitivity and success of firms. For the last decade, Portuguese customers of fixed
telecommunications industry have easily switched the service provider, which has been
very damaging for the business performance and, therefore, for the economy.
The main objective of this study is to analyse the partial churn of residential customers in
the fixed-telecommunications industry (fixed-telephone and ADSL), by using survival
models. Additionally, we intend to test the assumption of constant customer retention rate
over time and across customers. Lastly, the effect of satisfaction on partial customer churn
is analysed. The models are developed by using large-scale data from an internal database
of a Portuguese fixed telecommunications company. The models are estimated with a large
number of covariates, which includes customer’s basic information, demographics, churn
flag, customer historical information about usage, billing, subscription, credit, and other.
Our results show that the variables that influence the partial customer churn are the service
usage, mean overall revenues, current debts, the number of overdue bills, payment method,
equipment renting, the existence of flat plans and the province of the customer. Portability
also affects the probability of churn in fixed-telephone contracts. The results also suggest
that the customer retention rate is neither constant over time nor across customers, for both
types of contracts. Lastly, it seems that satisfaction does not influence the cancellation of
both types of contracts.Considerando que os lucros gerados pelos clientes são vitais para as empresas (Grant e
Schlesinger, 1995), uma melhoria na gestão do cliente é fundamental para assegurar a
competitividade e o sucesso das empresas. Na última década, os clientes portugueses das
empresas de telecomunicações fixas têm mudado de operador com demasiada facilidade, o
que tem prejudicado o desempenho das empresas e, consequentemente, a economia.
O principal objectivo deste estudo é analisar o cancelamento de contratos de telefone fixo e
ADSL por clientes residenciais, através do uso de modelos de sobrevivência. Para além
disso, pretende-se testar o pressuposto de que a taxa de retenção de clientes é constante ao
longo do tempo e entre clientes. Por último, pretende-se analisar o efeito da satisfação do
cliente no cancelamento destes tipos de contratos. Os modelos são construÃdos com base
numa base de dados de larga escala fornecida por uma empresa portuguesa deste sector. Os
modelos são estimados com base num vasto número de variáveis, incluindo informação
básica sobre o cliente, dados demográficos, indicação sobre o cancelamento do contrato,
dados históricos sobre o uso dos serviços, facturação, contracto, crédito, etc..
Os resultados mostram que as variáveis que influenciam o cancelamento de ambos os tipos
de contratos são o uso do serviço, a facturação média, o valor em dÃvida, o número de
facturas em dÃvida, o método de pagamento, o método de pagamento do equipamento, a
existência de tarifas planas e o distrito do cliente. A portabilidade de número parece
influenciar o cancelamento de contratos de telefone fixo. Os resultados também mostram
que a taxa de retenção de clientes não é constante ao longo do tempo nem entre clientes em
ambos os tipos de contratos. Por último, parece que a satisfação não influencia o
cancelamento de ambos os tipos de contratos
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