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Article
Publication date: 8 April 2014

Kristof Coussement

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…

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Abstract

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.

Details

European Journal of Marketing, vol. 48 no. 3/4
Type: Research Article
ISSN: 0309-0566

Keywords

Article
Publication date: 13 March 2017

Samira Khodabandehlou and Mahmoud Zivari Rahman

This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business.

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Abstract

Purpose

This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business.

Design/methodology/approach

The six stages are as follows: first, collection of customer behavioral data and preparation of the data; second, the formation of derived variables and selection of influential variables, using a method of discriminant analysis; third, selection of training and testing data and reviewing their proportion; fourth, the development of prediction models using simple, bagging and boosting versions of supervised machine learning; fifth, comparison of churn prediction models based on different versions of machine-learning methods and selected variables; and sixth, providing appropriate strategies based on the proposed model.

Findings

According to the results, five variables, the number of items, reception of returned items, the discount, the distribution time and the prize beside the recency, frequency and monetary (RFM) variables (RFMITSDP), were chosen as the best predictor variables. The proposed model with accuracy of 97.92 per cent, in comparison to RFM, had much better performance in churn prediction and among the supervised machine learning methods, artificial neural network (ANN) had the highest accuracy, and decision trees (DT) was the least accurate one. The results show the substantially superiority of boosting versions in prediction compared with simple and bagging models.

Research limitations/implications

The period of the available data was limited to two years. The research data were limited to only one grocery store whereby it may not be applicable to other industries; therefore, generalizing the results to other business centers should be used with caution.

Practical implications

Business owners must try to enforce a clear rule to provide a prize for a certain number of purchased items. Of course, the prize can be something other than the purchased item. Business owners must accept the items returned by the customers for any reasons, and the conditions for accepting returned items and the deadline for accepting the returned items must be clearly communicated to the customers. Store owners must consider a discount for a certain amount of purchase from the store. They have to use an exponential rule to increase the discount when the amount of purchase is increased to encourage customers for more purchase. The managers of large stores must try to quickly deliver the ordered items, and they should use equipped and new transporting vehicles and skilled and friendly workforce for delivering the items. It is recommended that the types of services, the rules for prizes, the discount, the rules for accepting the returned items and the method of distributing the items must be prepared and shown in the store for all the customers to see. The special services and reward rules of the store must be communicated to the customers using new media such as social networks. To predict the customer behaviors based on the data, the future researchers should use the boosting method because it increases efficiency and accuracy of prediction. It is recommended that for predicting the customer behaviors, particularly their churning status, the ANN method be used. To extract and select the important and effective variables influencing customer behaviors, the discriminant analysis method can be used which is a very accurate and powerful method for predicting the classes of the customers.

Originality/value

The current study tries to fill this gap by considering five basic and important variables besides RFM in stores, i.e. prize, discount, accepting returns, delay in distribution and the number of items, so that the business owners can understand the role services such as prizes, discount, distribution and accepting returns play in retraining the customers and preventing them from churning. Another innovation of the current study is the comparison of machine-learning methods with their boosting and bagging versions, especially considering the fact that previous studies do not consider the bagging method. The other reason for the study is the conflicting results regarding the superiority of machine-learning methods in a more accurate prediction of customer behaviors, including churning. For example, some studies introduce ANN (Huang et al., 2010; Hung and Wang, 2004; Keramati et al., 2014; Runge et al., 2014), some introduce support vector machine ( Guo-en and Wei-dong, 2008; Vafeiadis et al., 2015; Yu et al., 2011) and some introduce DT (Freund and Schapire, 1996; Qureshi et al., 2013; Umayaparvathi and Iyakutti, 2012) as the best predictor, confusing the users of the results of these studies regarding the best prediction method. The current study identifies the best prediction method specifically in the field of store businesses for researchers and the owners. Moreover, another innovation of the current study is using discriminant analysis for selecting and filtering variables which are important and effective in predicting churners and non-churners, which is not used in previous studies. Therefore, the current study is unique considering the used variables, the method of comparing their accuracy and the method of selecting effective variables.

Details

Journal of Systems and Information Technology, vol. 19 no. 1/2
Type: Research Article
ISSN: 1328-7265

Keywords

Abstract

Details

Machine Learning and Artificial Intelligence in Marketing and Sales
Type: Book
ISBN: 978-1-80043-881-1

Case study
Publication date: 20 January 2017

Anton S. Ovchinnikov

This case exposes students to predictive analytics as applied to discrete events with logistic regression. The VP of customer services for a successful start-up wants to…

Abstract

This case exposes students to predictive analytics as applied to discrete events with logistic regression. The VP of customer services for a successful start-up wants to proactively identify customers most likely to cancel services or “churn.” He assigns the task to one of his associates and provides him with data on customer behavior and his intuition about what drives churn. The associate must generate a list of the customers most likely to churn and the top three reasons for that likelihood.

Article
Publication date: 5 April 2011

Z. Zare‐Hoseini, M.J. Tarokh and H. Jabbari Nooghabi

Acquiring and retaining profitable customers are major concerns of a business. In this paper, the customers of the Restoration and Beauty Clinic of Iran University are segmented…

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Abstract

Purpose

Acquiring and retaining profitable customers are major concerns of a business. In this paper, the customers of the Restoration and Beauty Clinic of Iran University are segmented using three value types: current value, expected value, and loyalty in a case study to predict the probability of customer churn and future purchase services in the clinic.

Design/methodology/approach

This study utilized customers' data records with nine data fields (socio‐demographic and transactional) from three year's transactions of the clinic. Logistic regression as a data mining technique is then used to predict the future behavior of the customers. In addition, the verification and the validation of the models are done using lift charts.

Findings

This research segments the customers of the clinic into four categories based on three values (current value, expected value, and loyalty). Then simple marketing strategies that might be adopted are suggested. These strategies might help the shareholders and experts of the clinic to promote relationships with patients and deliver better services to attract and retain their customers.

Originality/value

The results of this research enable public health agencies to evaluate the effectiveness of their policies and detect their shortcomings in order to better serve patients. Also, it will help to increase their profits from the clinics and raise customer satisfaction.

Details

International Journal of Pharmaceutical and Healthcare Marketing, vol. 5 no. 1
Type: Research Article
ISSN: 1750-6123

Keywords

Article
Publication date: 15 February 2008

Maria Mavri and George Ioannou

This paper aims to examine customer switching behaviour in Greek banking services. More specifically it aims to investigate predictors of churn behaviour as part of customer…

3591

Abstract

Purpose

This paper aims to examine customer switching behaviour in Greek banking services. More specifically it aims to investigate predictors of churn behaviour as part of customer relationship management (CRM).

Design/methodology/approach

The enhancement of existing relationships is of pivotal importance to banks, since attracting new customers is known to be more expensive. The paper discusses survival analysis based on data collected from customers of a leading financial services company. It examines a number of variables, which represent characteristics of the customers and of the offered services and products. By using life tables, it estimates the contribution of each separate factor in customers' switching behaviour in different periods of time.

Findings

A hazard proportional model is built to determine the risk of churn behaviour, which is the end‐result of all the examined factors.

Practical implications

Bank's management team could use the findings of our study, in order to determine specific attributes in designing financial services and products, which would add in customers' satisfaction.

Originality/value

The approach and results have significant implications for enlarging the duration of the relationship among customer and bank.

Details

Managerial Finance, vol. 34 no. 3
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 26 July 2013

Gunnvald B. Svendsen and Nina K. Prebensen

The present paper aims to investigate the effect of network provider, customer demographics, customer satisfaction and perceived switch costs on churn in the mobile…

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Abstract

Purpose

The present paper aims to investigate the effect of network provider, customer demographics, customer satisfaction and perceived switch costs on churn in the mobile telecommunications market.

Design/methodology/approach

The study is carried out as a longitudinal, two-wave study of mobile telecommunications customers in Norway: n=1,499 (wave 1) and n=976 (wave 2). Churn is measured as change in the mobile network provider between the two waves. The data are analysed as a logistic regression with the independent variables provider, gender, satisfaction, switch costs and age.

Findings

The analysis shows significant effects of provider, satisfaction, switch costs and age and of the interaction between satisfaction and provider. Gender has no significant effect on churn. Provider effects are interpreted as effects of brand image since other known influences on churn (satisfaction, switch costs and demographics) have been controlled for in the design.

Research limitations/implications

Further research is necessary in order to single out which brand aspects are responsible for the effects of brand ownership and to ensure the generality of the findings outside Scandinavia.

Practical implications

The findings indicate that a strong brand image makes a company less susceptible to customer churn caused by low satisfaction.

Originality/value

The relation between brand ownership and churn in the mobile telecommunications sector has not been reported previously.

Details

European Journal of Marketing, vol. 47 no. 8
Type: Research Article
ISSN: 0309-0566

Keywords

Open Access
Article
Publication date: 26 May 2022

James Lappeman, Michaela Franco, Victoria Warner and Lara Sierra-Rubia

This study aims to investigate the factors that influence South African customers to potentially switch from one bank to another. Instead of using established models and survey…

2434

Abstract

Purpose

This study aims to investigate the factors that influence South African customers to potentially switch from one bank to another. Instead of using established models and survey techniques, the research measured social media sentiment to measure threats to switch.

Design/methodology/approach

The research involved a 12-month analysis of social media sentiment, specifically customer threats to switch banks (churn). These threats were then analysed for co-occurring themes to provide data on the reasons customers were making these threats. The study used over 1.7 million social media posts and focused on all five major South African retail banks (essentially the entire sector).

Findings

This study concluded that seven factors are most significant in understanding the underlying causes of churn. These are turnaround time, accusations of unethical behaviour, billing or payments, telephonic interactions, branches or stores, fraud or scams and unresponsiveness.

Originality/value

This study is unique in its measurement of unsolicited social media sentiment as opposed to most churn-related research that uses survey- or customer-data-based methods. In addition, this study observed the sentiment of customers from all major retail banks across 12 months. To date, no studies on retail bank churn theory have provided such an extensive perspective. The findings contribute to Susan Keaveney’s churn theory and provide a new measurement of switching threat through social media sentiment analysis.

Details

Journal of Consumer Marketing, vol. 39 no. 5
Type: Research Article
ISSN: 0736-3761

Keywords

Article
Publication date: 15 February 2016

Shweta Singh and Sumit Singh

The Purpose of this study is to provide an alternative way to create customer valuation metric while accounting for customer riskiness. Customer relationship management (CRM…

Abstract

Purpose

The Purpose of this study is to provide an alternative way to create customer valuation metric while accounting for customer riskiness. Customer relationship management (CRM) emphasizes the importance of measuring customer value. Analytics has paved the way for innovation by providing companies valuable insights into the behavior of customers. Earlier models used to measure customer value do not take into account the types and level of risk posed by customers, such as probability of churn, regularity of purchases, etc. The authors put forth a new and innovative approach to measuring customer value while, at the same time, adjusting for customer riskiness.

Design/methodology/approach

Using a non-parametric approach used in the operations research area, the authors create a risk-adjusted regency, frequency, monetary value (RARFM) score for each customer. These scores are used to segment the customers into two groups – customers with high and low RARFM scores. The authors then identify the underlying demographics and behavioral characteristics that separate the two groups.

Findings

Findings of this paper indicate that customers who perform the best on the RARFM metric tend to be more experienced, and are more likely to exhibit behavioral tendencies that help them perform well in their jobs, such as purchasing promotional goods that act as sales aid and enhance their performance.

Originality/value

The paper is innovative in its approach in terms of creating a new metric for calculating customer value. Few papers have proposed ways to handle and adjust for customer riskiness. Here, the authors propose three kinds of customer risk. Current paper provides a twist to traditional RFM analysis by creating a RARFM score for each customer, and provides a scientific way of assigning weights to RFM.

Details

Management Research Review, vol. 39 no. 2
Type: Research Article
ISSN: 2040-8269

Keywords

Article
Publication date: 20 August 2019

Sandhya N., Philip Samuel and Mariamma Chacko

Telecommunication has a decisive role in the development of technology in the current era. The number of mobile users with multiple SIM cards is increasing every second. Hence…

Abstract

Purpose

Telecommunication has a decisive role in the development of technology in the current era. The number of mobile users with multiple SIM cards is increasing every second. Hence, telecommunication is a significant area in which big data technologies are needed. Competition among the telecommunication companies is high due to customer churn. Customer retention in telecom companies is one of the major problems. The paper aims to discuss this issue.

Design/methodology/approach

The authors recommend an Intersection-Randomized Algorithm (IRA) using MapReduce functions to avoid data duplication in the mobile user call data of telecommunication service providers. The authors use the agent-based model (ABM) to predict the complex mobile user behaviour to prevent customer churn with a particular telecommunication service provider.

Findings

The agent-based model increases the prediction accuracy due to the dynamic nature of agents. ABM suggests rules based on mobile user variable features using multiple agents.

Research limitations/implications

The authors have not considered the microscopic behaviour of the customer churn based on complex user behaviour.

Practical implications

This paper shows the effectiveness of the IRA along with the agent-based model to predict the mobile user churn behaviour. The advantage of this proposed model is as follows: the user churn prediction system is straightforward, cost-effective, flexible and distributed with good business profit.

Originality/value

This paper shows the customer churn prediction of complex human behaviour in an effective and flexible manner in a distributed environment using Intersection-Randomized MapReduce Algorithm using agent-based model.

Details

Data Technologies and Applications, vol. 53 no. 3
Type: Research Article
ISSN: 2514-9288

Keywords

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