Modelling Customer Churn Rate and Its Use for Customer Retention Planning

2022 ◽  
Author(s):  
Ashish Sinha ◽  
Sumesh Raizada
Keyword(s):  
Customer Retention ◽  
Customer Churn

Improving customer retention management through cost-sensitive learning

2014 ◽  
Vol 48 (3/4) ◽  
pp. 477-495 ◽  
Author(s):  
Kristof Coussement
Keyword(s):  
Real Life ◽  
Customer Retention ◽  
Success Factor ◽  
Consumer Decision Making ◽  
Churn Prediction ◽  
Misclassification Cost ◽  
Content Type ◽  
Cost Sensitive Learning ◽  
Customer Churn ◽  
Retention Management

Purpose – Retailers realize that customer churn detection is a critical success factor. However, no research study has taken into consideration that misclassifying a customer as a non-churner (i.e. predicting that (s)he will not leave the company, while in reality (s)he does) results in higher costs than predicting that a staying customer will churn. The aim of this paper is to examine the prediction performance of various cost-sensitive methodologies (direct minimum expected cost (DMECC), metacost, thresholding and weighting) that incorporate these different costs of misclassifying customers in predicting churn. Design/methodology/approach – Cost-sensitive methodologies are benchmarked on six real-life churn datasets from the retail industry. Findings – This article argues that total misclassification cost, as a churn prediction evaluation measure, is crucial as input for optimizing consumer decision making. The practical classification threshold of 0.5 for churn probabilities (i.e. when the churn probability is greater than 0.5, the customer is predicted as a churner, and otherwise as a non-churner) offers the worst performance. The provided managerial guidelines suggest when to use each cost-sensitive method, depending on churn levels and the cost level discrepancy between misclassifying churners versus non-churners. Practical implications – This research emphasizes the importance of cost-sensitive learning to improve customer retention management in the retail context. Originality/value – This article is the first to use the concept of misclassification costs in a churn prediction setting, and to offer recommendations about the circumstances in which marketing managers should use specific cost-sensitive methodologies.

scholarly journals Customer Retention Strategy Through Customer Satisfaction and Customer Loyalty: The Study on Traveloka Loyalty Program

2018 ◽  
Vol 2 (1) ◽  
pp. 69
Author(s):  
Fiona Poetri Komalasari ◽  
Surya Fajar Budiman
Keyword(s):  
Customer Satisfaction ◽  
Customer Loyalty ◽  
Customer Retention ◽  
Average Score ◽  
Loyalty Program ◽  
Retention Strategy ◽  
Customer Base ◽  
Customer Churn ◽  
Online Travel ◽  
Customer Satisfaction And Loyalty

Customer loyalty and customer retention is very closely related, customer loyalty stopped customer churn and strengthen the customer retention. The primary aim of retention strategy is to build a strong customer base and to prevent them from drifting towards other competitor. Traveloka as a travel supplier company, is the leading online travel agent in Indonesia. The customers satisfaction has an average score of 3,91. The result signify that Traveloka is successful in obtaining its customers’ satisfaction The customer loyalty has an average score of 4,04. Traveloka has managed to reach customer loyalty Traveloka customer satisfaction and loyalty are obtained as presented through chapter IV that the average is 3,97. In this research, Traveloka customer retention strategy using loyalty program is proven to be effective.

Customer retention management: A complementary use of data mining and soft systems methodology

2020 ◽  
pp. 1-20
Author(s):  
Mohammad Mehrabioun ◽  
Bibi Malihe Mahdizadeh
Keyword(s):  
Human Activity ◽  
Customer Retention ◽  
Soft Systems Methodology ◽  
Activity System ◽  
Systems Methodology ◽  
Soft Systems ◽  
Customer Churn ◽  
Activity Modeling ◽  
Purposeful Activity ◽  
Human Activity System

BACKGROUND: Customer retention and management of customer churn are deemed as among the most significant issues for businesses. Given the fact that customer churn is not typically predictable easily, identifying and analyzing customer churn is necessary for businesses. OBJECTIVE: Therefore, the current research was conducted to employ a complementary approach to identify the reasons influencing customer churn. METHODS: To do so, initially, customers’ data were clustered by recruiting the K-means method. Each cluster represented customers who held similar values and the probability of churn behavior. In the next step, stakeholder groups are identified based on the K- means algorithm. Then, Soft Systems Methodology (SSM) was employed to encapsulate each of the identified interested groups’ world-view to better understand logical reasons for churned customers. Purposeful activity modeling (human activity system) was adopted for each interested group utilizing SSM techniques. RESULTS: Using SSM techniques, purposeful activity modeling (human activity system) for each interested group adopted. Utilizing human activity systems for structuring debate sessions about change actions, short-term and long-term plans have been proposed to maintain and improve customer retention programs. CONCLUSIONS: SSM can be considered as an overarching approach that can afford a better understanding of the processes derived from data mining.

scholarly journals A Hybrid Data Mining Method for Customer Churn Prediction

2018 ◽  
Vol 8 (3) ◽  
pp. 2991-2997
Author(s):  
E. Jamalian ◽  
R. Foukerdi
Keyword(s):  
Customer Retention ◽  
Real Data ◽  
Data Preparation ◽  
Churn Prediction ◽  
Weighted Voting ◽  
Customer Churn ◽  
Hybrid Data ◽  
Using Data ◽  
The Individual ◽  
Churn Management

The expenses for attracting new customers are much higher compared to the ones needed to maintain old customers due to the increasing competition and business saturation. So customer retention is one of the leading factors in companies’ marketing. Customer retention requires a churn management, and an effective management requires an exact and effective model for churn prediction. A variety of techniques and methodologies have been used for churn prediction, such as logistic regression, neural networks, genetic algorithm, decision tree etc.. In this article, a hybrid method is presented that predicts customers churn more accurately, using data fusion and feature extraction techniques. After data preparation and feature selection, two algorithms, LOLIMOT and C5.0, were trained with different size of features and performed on test data. Then the outputs of the individual classifiers were combined with weighted voting. The results of applying this method on real data of a telecommunication company proved the effectiveness of the method.

scholarly journals Data-driven agent-based exploration of customer behavior

SIMULATION ◽  
2017 ◽  
Vol 94 (3) ◽  
pp. 195-212 ◽  
Author(s):  
David Bell ◽  
Chidozie Mgbemena
Keyword(s):  
Decision Tree ◽  
Customer Retention ◽  
Mobile Network ◽  
Mobile Services ◽  
Customer Behavior ◽  
Data Driven ◽  
Agent Based ◽  
Services Sector ◽  
Customer Churn ◽  
Mobile Network Operators

Customer retention is a critical concern for mobile network operators because of the increasing competition in the mobile services sector. Such unease has driven companies to exploit data as an avenue to better understand changing customer behavior. Data-mining techniques such as clustering and classification have been widely adopted in the mobile services sector to better understand customer retention. However, the effectiveness of these techniques is debatable due to the constant change and increasing complexity of the mobile market itself. This design study proposes an application of agent-based modeling and simulation (ABMS) as a novel approach to understanding customer behavior through the combination of market and social factors that emerge from data. External forces at play and possible company interventions can then be added to data-derived models. A dataset provided by a mobile network operator is utilized to automate decision-tree analysis and subsequent building of agent-based models. Popular churn modeling techniques were adopted in order to automate the development of models, from decision trees, and subsequently explore possible customer churn scenarios. ABMS is used to understand the behavior of customers and detect reasons why customers churned or stayed with their respective mobile network operators. A CART decision-tree method is presented that identifies agents, selects important attributes, and uncovers customer behavior – easily identifying tenure, location, and choice of mobile devices as determinants for the churn-or-stay decision. Word of mouth between customers is also explored as a possible influence factor. Importantly, methods for automating data-driven agent-based simulation model generation will support faster exploration and experimentation – including with those determinants from a wider market or social context.

Managing Service Quality, Food Quality, Price Fairness and Customer Retention: A Holistic Perspective

2020 ◽  
Vol 2 (1) ◽  
pp. 1-5
Author(s):  
Ammar Ahmed ◽  
Rafat Naseer ◽  
Muhammad Asadullah ◽  
Hadia Khan
Keyword(s):  
Service Quality ◽  
Food Quality ◽  
Service Sector ◽  
Customer Retention ◽  
Price Fairness ◽  
Policy Makers ◽  
Restaurant Management ◽  
The Subject ◽  
Research Propositions ◽  
The Impact

In this competitive environment, organizations strive to satisfy their customer by providing best quality service at affordable and fair prices with a view to enhance their revenues. To achieve the objective of revenue maximization, organizations strive to identify the factors that help them in retaining their customers. Drawing from the signalling theory of marketing, the current study proposes a novel conceptual model representing the impact of service quality with food quality and price fairness on customer retention in restaurant sector of Pakistan. The paper underlines an important arena of knowledge for academicians as well as organizational scientists on the subject. On the basis of literature available on the variables understudy, the present study forwards eight research propositions worthy of urgent scholarly attention. The conceptualized model of the present article can also be viewed significant in unleashing further avenues for the restaurant management entities, policy makers and future researchers in the domain of managing in the service sector businesses.

A study on relationship between relational benefits and customer value, switching barrier, customer retention in hotel service

2009 ◽  
Vol 10 (3) ◽  
pp. 131-150
Author(s):  
김재홍 ◽  
Dongchoon Choi ◽  
Sungjoon Yoon
Keyword(s):  
Customer Value ◽  
Customer Retention ◽  
Relational Benefits ◽  
Hotel Service
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