- Open Access
Developing a prediction model for customer churn from electronic banking services using data mining
© The Author(s). 2016
- Received: 29 January 2016
- Accepted: 13 August 2016
- Published: 22 August 2016
Given the importance of customers as the most valuable assets of organizations, customer retention seems to be an essential, basic requirement for any organization. Banks are no exception to this rule. The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention.
Being based on existing information technologies which allow one to collect data from organizations’ databases, data mining introduces a powerful tool for the extraction of knowledge from huge amounts of data. In this research, the decision tree technique was applied to build a model incorporating this knowledge.
The results represent the characteristics of churned customers.
Bank managers can identify churners in future using the results of decision tree. They should be provide some strategies for customers whose features are getting more likely to churner’s features.
- Customer churn
- Data mining
- Electronic banking services
- Decision tree
Emphasizing the higher costs associated with attracting new customers compared with retaining existing customers, and the fact that long-term customers tend to produce more profits, Verbeke et al. (2011) assert that customer retention increases profitability. Many competitive organizations have realized that a key strategy for survival within the industry is to retain existing customers. Tsai and Chen (2010) argued that “this leads to the importance of churn management.”
Customer churn represents a basic problem within the competitive atmosphere of banking industry.
According to Nie et al. (2011), a bank can increase its profits by up to 85 % by improving the retention rate by up to 5 %. In addition, customer retention is seen as more important than in the past. This survey seeks to identify common characteristics of churned customers in order to build a customer churn prediction model.
According to Sharma and Panigrahi (2011), churning refers to a customer who leaves one company to go to another company.
Customer churn introduces not only some loss in income but also other negative effects on the operation of companies (Chen et al. 2014). As Hadden et al. (2005) stipulated, “Churn management is the concept of identifying those customers who are intending to move their custom to a competing service provider.”
Risselada et al. (2010) stated that churn management is becoming part of customer relationship management. It is important for companies to consider it as they try to establish long-term relationships with customers and maximize the value of their customer base.
Data mining refers to the discovery of knowledge from a huge amount of data (Nie et al. 2011). Tsai and Lu (2009) described data mining as discovering interesting patterns within the data and predicting or classifying the behavior exhibited by the model. Seng and Chen (2010) suggested that the basic challenge is how to convert seemingly meaningless data into useful information and competitive intelligence.
Data mining in customer churn
Tsai and Lu (2009) stipulated that “in literature, statistical and data mining techniques have been used to create the prediction models.” Classification tools are often used to model and predict customer churn. Some of the techniques commonly used to achieve this are neural networks, decision trees (DT), random forests, support vector machines (SVM) and logistic regression (Miguéis et al. 2012).
Liébana-Cabanillas et al. (2013) recognized electronic banking portals as initial alternative channels to the traditional bank branches. They mentioned many advantages of electronic banking; these include convenient and global access, availability, time- and cost-saving, wider choices of services, information transparency, customization, and financial innovation.
Researcher and survey year
Guo-en and Wei-dong (2008)
Support vector machine, decision tree c4.5, logistic regression, naïve Bayes
Anil Kumar and Ravi (2008)
Multilayer perceptron, logistic regression, decision tree, random forest, radial basis function network, support vector machine techniques
Nie et al. (2011)
Logistic regression, decision tree
Lin et al. (2011)
Rough set theory, rule-based decision-making technique, flow network graph
Huang et al. (2012)
Logistic regression, linear classifier, naïve Bayes, decision tree, multilayer perceptron neural networks, support vector machines
Sharma and Panigrahi (2011)
Yu et al. (2011)
Neural networks, support vector machine, decision tree, extended support vector machine
Farquad et al. (2014)
Support vector machine, naïve Bayes, tree rules
Keramati et al. (2014)
Decision tree, neural networks, support vector machine, k-nearest neighbors
Keramati and Ardabili (2011) defined customer satisfaction as “an experience-based assessment that stems from the degree to which customer expectations about characteristics of the service have been fulfilled.” As elements of satisfaction within the scope of electronic banking, Kumbhar (2011) referred to “perceived value, brand perception, cost effectiveness, ease of use, convenience, problem handling, security/assurance, responsiveness, contact facilities, system availability, fulfillment, efficiency and compensation.” In their study, Keramati and Ardabili (2011) analyzed customer churn across an Iranian mobile network operator. They used service failure rate, length of customer association, and customer complaints to evaluate the level of dissatisfaction across the operator’s database.
Accordingly, considering the limitations in the available data in the bank’s database, in this research, length of customer association and customer complaints were used to evaluate level of customer’s dissatisfaction.
Level of service usage
In this research, the number and value amount of transactions undertaken via electronic banking portals such as internet bank, unstructured supplementary service data (USSD) commands, telephone bank, mobile bank, and ATM were extracted from bank’s database.
Customer demographic variables
Clemes et al. (2010) listed customer-related demographic variables (e.g., income, age, education, culture, and nationality). They further suggested that the customer’s occupation may affect his or her use of electronic banking channels. Buckinx and Van den Poel (2005) investigated the effect of gender as a customer demographic variable.
Considering the limitations in the available data in the bank’s database, in this research, age, gender, level of education, and career were used to evaluate customer demographic variables.
To identify the characteristics of churned customers, we used the DT method in the modeling phase of CRISP-DM.
Data mining techniques
According to Han et al. (2012), in data mining, the predictive analysis task is undertaken via regression and classification techniques. They introduced classification as a process of finding a model that explains and recognizes data classes or concepts. This model is derived from the training dataset. The training data refer to the data objects whose class labels are known. The model can then be used to predict class labels of objects with unknown labels.
When an instance is classified by a DT model, the DT sorts it through the tree to the suitable leaf node. Each leaf node shows a classification (Tsai and Chen 2010). Nie et al. (2011) suggested that the DT not only produces results which are easy to understand, but that it also has the ability to build models using numerical and categorical datasets.
In the present research, DT techniques were applied to build a prediction model for customer churn from electronic banking services for two reasons.
One reason relates to our goal of finding the features of churners and our need to understand if-then rules for this goal. Due to DT provides easy understanding rules, DT technique was selected for modeling phase. The other reason is the type of our data. Our data include numerical and categorical types and DT was suitable for these types of data. Therefor DT was applied for the modeling phase.
Business understanding phase
Zan et al. (2007) demonstrated that business understanding can be established via understanding the goals and data mining requirements.
Commercial objectives of this study include the discovery of common characteristics of churned customers from electronic banking services.
In the present survey, financial, human, and scientific resources are used. In order to accomplish this commercial objective, DT was used in the modeling phase. The results of the model represent the features of the churners.
Data understanding phase
Zan et al. (2007) stated that for this phase it is necessary to “determine what data is available to solve your business needs.” In the present survey, we randomly sampled 4383 customers of electronic banking services from the bank’s database. The extracted data covered the time interval between March 21st, 2013, and March 20th, 2015.In this research, career is treated as a nominal variable, while gender, complaint, and churn are taken as binomial variables. Gender accepts either of two statuses: male or female, with 0 referring to a female customer and 1 to a male customer. A customer that had no transactions through electronic banking portals for at least the two years prior to the end of the research time period is considered to be a churner. For the customer churn variable, 1 refers to a churner and 0 to a non-churner. Education level was parameterized as an ordinal variable while the remaining variables were treated as discrete numerical variables. The dataset is shown in Tables 2, 3, 4 and 5.Table 2
Descriptions of customer demographic data
Minimum: 19 years
Maximum: 76 years
Average: 39 years
Female: 4.61 %
Male: 95.39 %
Level of education
Illiterate: 0.59 %
Reading and writing ability: 0.8 %
Elementary education: 2.51 %
High school diploma: 12.39 %
Diploma: 42.12 %
Associate degree: 18.94 %
B. Sc.: 19.44 %
M. Sc.: 1.76 %
Ph. D: 1.46 %
Manufacturing: 0.9 %
Services: 94.6 %
Housing and construction: 0.5 % Commercial business: 3.7 %
Agriculture: 0.3 %Table 3
Descriptions of level of service usage data
Number and amount of transactions through ATM
Missing data: 3 (in number of transactions)
Number and amount of transactions through USSD-based mobile banking
Missing data: 0
Number and amount of transactions through mobile bank
Missing data: 0
Number and amount of transactions through telephone bank
Missing data: 0
Number and amount of transactions through internet bank
Missing data: 0Table 4
Descriptions of customer dissatisfaction data
Zero for all customers
Length of customer association
Minimum: 0 year
Maximum: 12 years
Average: 7 yearsTable 5
Descriptions of customer churn
Churner: 1.44 %
Non-churner: 98.56 %
Data preprocessing phase
According to Chen and Huang (2011), raw data should be transformed into useful information in this phase. Larose (2005) described this phase as the one in which data selection and data cleaning tasks are undertaken. Han et al. (2012) mentioned that sampling and feature subset selection are done in the data preprocessing phase. The feature subset selection process omits the redundant or irrelevant features.
The variables (except complaint) were used in this phase. As highlighted by Han et al. (2012), “data cleaning routines attempt to fill in missing values, smooth out noise, identify outliers, and correct inconsistencies within the data.” In this research, we detected and eliminated outliers. Furthermore, we used two methods to fill in missing values: 1) replacing missing values with the average value of the corresponding variable; and 2) using k-nearest neighbor (k = 5). Considering the numbers of churners and non-churners (63 and 4320 customers, respectively), this is an imbalanced data problem. To solve it we used a bootstrap sampling module in the RapidMiner data mining software. In this method, random sampling with replacement is performed to take samples of customer records. In order to select the best method for data cleaning, we followed a DT approach to evaluate the results. The depth of the DT was set to 20; also, we used a gini index in the DT setting.
Results of evaluating after preprocessing phase
Noise & Outliers were detected and eliminated
Impute (k = 5)
As indicated by similar results shown in Table 6, we could equally have chosen either of the compared methods. We chose to replace missing values with the average value of the corresponding variable.
Han et al. (2012) introduced forward selection and backward elimination methods for feature subset selection. They defined forward selection as a procedure that starts with an empty set as the reduced set. At each step, the best of the remaining features are determined and added into the reduced set. They defined backward elimination as a procedure that starts with a full attributes set. At each step, the worst remaining attributes are removed from the set.
Results of evaluating feature selection methods
Feature selection method
The backward elimination method was selected based on the results that showed in Table 7. The output of the backward elimination method indicated that career was redundant, so this feature was omitted from the dataset.
This model categorized the characteristics of churned customers into five groups. We can use this model to predict customer churn from electronic banking services based on their common characteristics.
True negative (TN) refers to the number of negative tuples that were labeled correctly by the classifier.
False positive (FP) refers to the number of negative tuples that were incorrectly labeled as positive.
False negative (FN) refers to the number of positive tuples that were incorrectly labeled as negative.
True positive (TP) refers to the positive tuples that were labeled correctly as positive.
Maratea et al. (2014) defined Accuracy as “the probability of success in recognizing the right class of an instance.”
They also defined Precision as “the probability that a predicted positive class instance is a true positive” and explained Recall as “the probability of success in recognizing a positive class instance.” They further introduced F-measure, which is “the harmonic mean of precision and recall and tends towards the lower of the two.”
One of the useful statistical tools for describing the classifier performance is the receiver operating characteristic (ROC) curve. Furthermore, one of the most popular measures for evaluating the power of a predictive model is the area under the curve (AUC). Gigliarano et al. (2014) defined AUC as “the integrated true positive rate over all false positive rate values.” AUC takes a value between 0 and 1.
We use k-fold cross validation to estimate the model’s accuracy or compare performances of two classification algorithms. This method divides a dataset into k folds of nearly equal sizes. Each fold is in turn used to test the model that provided with other k-1 folds by a classification algorithm. The average of the k accuracies obtained from k-fold cross validation is taken as the performance of the corresponding classification algorithm (Wong 2015).
In this research, in order to enhance the model evaluation, we use a cross-validation method with k = 10. All of the tuples in the dataset are used for training and testing the model in this method.
Results of evaluation
We presented the final report to the bank and the bank’s experts are now implementing the report.
We implemented the CRISP methodology for predicting customer churn in electronic banking services. The aim of the present study is to identify the features of churners from electronic banking services. Demographic variables (e.g., age, gender, career, and level of education), transaction data through electronic banking portals (e.g., ATM, mobile bank, telephone bank, internet bank, and USSD-based mobile banking), the length of the customer association, and customer complaints were extracted from the bank’s database.
Forward selection and backward elimination methods were applied for feature subset selection after data cleaning.
The backward elimination method performed better. This method showed that the career variable was redundant and so it was omitted from the dataset. The DT method was applied for the modeling of this dataset.
If number of transactions through USSD-based mobile banking ≤0.5 and length of customer association ≤6.5 and number of transactions through internet bank ≤1.5 and number of transactions through mobile bank ≤0.5 and number of transactions through telephone bank ≤1 and gender = 1 → Churn
If number of transactions through USSD-based mobile banking ≤0.5 and length of customer association ≤6.5 and number of transactions through internet bank ≤1.5 and number of transactions through mobile bank ≤0.5 and number of transactions through telephone bank ≤1 and gender = 0 and age ≤35 → Churn
If number of transactions through USSD-based mobile banking ≤0.5 and length of customer association ≤6.5 and number of transactions through internet bank ≤1.5 and number of transactions through mobile bank ≤0.5 and number of transactions through telephone bank ≤1 and gender = 0 and 35< age ≤41 → Churn
If number of transactions through USSD-based mobile banking ≤0.5 and length of customer association >6.5 and education level = high school diploma and age ≤29.5 → Churn
If number of transactions through USSD-based mobile banking ≤0.5 and length of customer association >6.5 and education level = Ph. D and age >53 → Churn
From our literature review, use of data mining techniques for predicting customer churn is new in the electronic banking context. Data collection and feature selection for predicting customer churn in the electronic banking services context is one of the novel aspects of the present research.
It is expected that, with a better understanding of the features of churners, bank managers can consider some strategies to prevent churn. These strategies should be used for customers whose features are growing more similar to the churner groups identified above. These strategies can include providing required facilities, improving the quality of services, identifying the needs of different groups, and increasing customer responsiveness.
Limitations and future research
The use of the bank’s database imposed some limitations on the present study. For example, we could examine only the factors that were recorded in the bank’s database. In addition, due to the large volume of data stored in the database and the associated privacy issues, it was time-consuming to extract all the data. Future research will further investigate the implementation results and will also identify customer requirements using different techniques and propose some methods to prevent them from churning. We will perform qualitative research to find the reasons for churn in the churner groups.
Acknowledgement in Cover letter (double blind review)
We would like to thank bank’s experts that extracted data from bank’s database.
Authors’ contributions in Cover letter (double blind review)
AK reviewed the manuscript and gave recommendation for improvements. HG carried out the data analysis and wrote the manuscript. SMM reviewed the manuscript. All authors have read and approved the final manuscript.
Competing interests in Cover letter (double blind review)
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Anil Kumar D, Ravi V (2008) Predicting credit card customer churn in banks using data mining. Int J Data Anal Tech Strateg 1(1):4–28View ArticleGoogle Scholar
- Buckinx W, Van den Poel D (2005) Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. Eur J Oper Res 164:252–268View ArticleGoogle Scholar
- Chen SC, Huang MY (2011) Constructing credit auditing and control & management model with data mining technique. Expert Syst Appl 38:5359–5365View ArticleGoogle Scholar
- Chen K, Hu Y-H, Hsieh Y-C (2014) Predicting customer churn from valuable B2B customers in the logistics industry: a case study. IseB 13:475–494. doi:10.1007/s10257-014-0264-1 View ArticleGoogle Scholar
- Clemes MD, Gan C, Zhang D (2010) Customer switching behaviour in the chinese retail banking industry. Int J Bank Mark 28(7):519–546View ArticleGoogle Scholar
- Deng X, Liu Q, Deng Y, Mahadevan S (2016) An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Inf Sci 340–341:250–161View ArticleGoogle Scholar
- Farquad MAH, Ravi V, Raju SB (2014) Churn prediction using comprehensible support vector machine:An analytical CRM application. Appl Soft Comput 19:31–40View ArticleGoogle Scholar
- Gigliarano C, Figini S, Muliere P (2014) Making classifier performance comparisons when ROC curves intersect. Comput Stat Data Anal 77:300–312View ArticleGoogle Scholar
- Guo-en X, Wei-dong J (2008) Model of customer churn prediction on support vector machine. Syst Eng Theory Pract 28(1):71–77View ArticleGoogle Scholar
- Hadden J, Tiwaria A, Roy R, Ruta D (2005) Computer assisted customer churn management: State-of-the-art and future trends. Comput Oper Res 34:2902–2917View ArticleGoogle Scholar
- Han J, Kamber M, Pei J (2012) DATA MINING Concepts and Techniques, 3rd edn. Morgan Kaufmann, USAGoogle Scholar
- Huang B, Kechadi MT, Buckley B (2012) Customer churn prediction in telecommunications. Expert Syst Appl 39:1414–1425View ArticleGoogle Scholar
- Keramati A, Ardabili SMS (2011) Churn analysis for an Iranian mobile operator. Telecommun Policy 35:344–356View ArticleGoogle Scholar
- Keramati A, Jafari-Marandi R, Aliannejadi M, Ahmadian I, Mozzafari M, Abbasi U (2014) Improved churn prediction in telecommunication industry using data mining techniques. Appl Soft Comput 24:994–1012View ArticleGoogle Scholar
- Kumbhar VM (2011) Factors affecting the customer satisfaction in e-banking: some evidences from Indian banks. Manag Res Pract 3(4):1–14Google Scholar
- Larose DT (2005) Discovering knowledge in data: An introduction to data mining. John Wiely & Sons, HobokenGoogle Scholar
- Liébana-Cabanillas F, Nogueras R, Herrera LJ, Guillén A (2013) Analysing user trust in electronic banking using data mining methods. Expert Syst Appl 40:5439–5447View ArticleGoogle Scholar
- Lin C-S, Tzeng G-H, Chin Y-C (2011) Combined rough set theory and flow network graph to predict customer churn in credit card accounts. Expert Syst Appl 38:8–15View ArticleGoogle Scholar
- Maratea A, Petrosino A, Manzo M (2014) Adjusted F-measure and kernel scaling for imbalanced data learning. Inf Sci 257:331–341View ArticleGoogle Scholar
- Miguéis VL, Van den Poel D, Camanho AS, Falcão e Cunha J (2012) Modeling partial customer churn: On the value of first product-category purchase sequences. Expert Syst Appl 39:11250–11256View ArticleGoogle Scholar
- Nie G, Rowe W, Zhang L, Tian Y, Shi Y (2011) Credit card churn forecasting by logistic regression and decision tree. Expert Syst Appl 38:15273–15285View ArticleGoogle Scholar
- Risselada H, Verhoef PC, Bijmolt THA (2010) Staying power of churn prediction models. J Interact Mark 24:198–208View ArticleGoogle Scholar
- Saradhi VV, Palshikar GK (2011) Employee churn prediction. Expert Syst Appl 38:1999–2006View ArticleGoogle Scholar
- Seng J-L, Chen TC (2010) An analytic approach to select data mining for business decision. Expert Syst Appl 37:8042–8057View ArticleGoogle Scholar
- Sharma A, Panigrahi PK (2011) A neural network based approach for predicting customer churn in cellular network services. Int J Comput Appl 27(11):26–31Google Scholar
- Tsai C-F, Chen M-Y (2010) Variable selection by association rules for customer churn prediction of multimedia on demand. Expert Syst Appl 37:2006–2015View ArticleGoogle Scholar
- Tsai C-F, Lu Y-H (2009) Customer churn prediction by hybrid neural networks. Expert Syst Appl 36:12547–12553View ArticleGoogle Scholar
- Verbeke W, Martens D, Mues C, Baesens B (2011) Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Syst Appl 38:2354–2364View ArticleGoogle Scholar
- Wong T-T (2015) Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recog 48:2839–2846View ArticleGoogle Scholar
- Yu X, Guo S, Guo J, Huang X (2011) An extended support vector machine forecasting framework for customer churn in e-commerce. Expert Syst Appl 38:1425–1430View ArticleGoogle Scholar
- Zan M, Shan Z, Li L, Ai-jun L (2007) A predictive model of churn in telecommunications based on data mining, IEEE International Conference on Control and Automation ThAl-2 Guangzhou., pp 809–813Google Scholar