Customer Lifetime Value and Defection Possibility Prediction Model Using Machine Learning: An Application to a Cloud-Based Software Company

Prasasti, Niken; Okada, Masato; Kanamori, Katsutoshi; Ohwada, Hayato

doi:10.1007/978-3-319-05458-2_7

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…We use precision, recall, and F1-scores as the evaluation metrics. These metrics are standard and widely used to evaluate the effectiveness of a prediction technique, c.f., [25], [36]. Thus, we believe there is little threat to construct validity.…”

Section: Threats To Validitymentioning

confidence: 91%

EFSPredictor: Predicting Configuration Bugs with Ensemble Feature Selection

Xia

et al. 2015

2015 Asia-Pacific Software Engineering Conference (APSEC)

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The configuration of a system determines the system behavior and wrong configuration settings can adversely impact system's availability, performance, and correctness. We refer to these wrong configuration settings as configuration bugs. The importance of configuration bugs has prompted many researchers to study it, and past studies can be grouped into three categories: detection, localization, and fixing of configuration bugs. In the work, we focus on the detection of configuration bugs, in particular, we follow the line-of-work that tries to predict if a bug report is caused by a wrong configuration setting. Automatically prediction of whether a bug is a configuration bug can help developers reduce debugging effort. We propose a novel approach named EFSPredictor which applies ensemble feature selection on the natural-language description of a bug report. It uses different feature selection approaches (e.g., ChiSquare, GainRatio and Relief) which output different ranked lists of textual features. Next, to obtain a set of representative textual features, EFSPredictor first assigns different scores to the features outputted by these feature selection approaches. Next, for each feature, EFSPredictor sums up the scores outputted by the multiple ranked lists, and outputs the top features (e.g., 25% of the total number of features) as the selected features. Finally, EFSPredictor builds a prediction model based on the selected features. We conduct experiments on 5 bug report datasets (i.e., accumulo, activemq, camel, flume, and wicket) containing a total of 3,203 bugs. The experiment results show that, on average across the 5 projects, EFSPredictor achieves an F1-score to 0.57, which improves the state-of-the-art approach proposed by Xia et al. by 14%.

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Section: Threats To Validitymentioning

confidence: 91%

EFSPredictor: Predicting Configuration Bugs with Ensemble Feature Selection

Xia

et al. 2015

2015 Asia-Pacific Software Engineering Conference (APSEC)

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“…The data used in this study are secondary data that has been used and pre-processed by Prasasti et al (2013;Martono, 2014;Kuswanto et al, 2015). The negative class is the larger class and the positive class is the least class.…”

Section: Data Sourcementioning

confidence: 99%

“…Prasasti et al (2013) predicted customer defection of company 'X' using C4.5 Decision Tree and SVM. Using the same dataset, Martono (2014) used J48 Decision Tree classification method (J48), Random Forest (RF), Neural Network with Multi Layer Perception (MLP), as well as SVM with SMO algorithm.…”

Section: Introductionmentioning

confidence: 99%

Comparison Between Selective Sampling and Random Undersampling for Classification of Customer Defection Using Support Vector Machine

Kuswanto

Sarumaha

Ohwada³

2017

Journal of Computer Science

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Quality of a product determines the customer loyalty and it can be measured by conducting a survey. Company 'X' that sells three kinds of product (low, medium and high price) collected very large dataset through an online survey and recorded customer defection and their characteristic. The measured variables are Update Accumulation, Product Price, Customer Type, Delivery Status and Customer Defection. The data has an imbalanced response that could mislead the accuracy of classification if it is analyzed using standard approaches. Selective Sampling (SS) and Random Undersampling (RU) have been applied to draw a sample from imbalance response in order to obtain more balance data. Furthermore, Support Vector Machine (SVM) has been applied to classify the sampled data. The performance of the SS-SVM and SS-RU to classify sampled data has been evaluated and compared with the result of classifying the raw dataset. The RU yields on exact balance (50%:50%) response class, while SS reduce the imbalance proportion significantly (around 52%:48%). Nevertheless, the SS-SVM outperforms RU-SVM in the sense that it is capable to run the process effectively, where the SS-SVM reduces the duration of classification process 3 to 20 h shorter than using RU-SVM, with slightly different accuracy rate. Moreover, the SS-SVM maintains the basic characteristics of raw data better than RU-SVM.

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“…Previous techniques in predicting customer defection include logistic regression [2], decision trees [3], support-vector machines (SVMs) [4], neural artificial networks [5], and random forests (RFs) [6]. In our previous study [7], we investigated predicting customer defection using the SVM and the J48 Decision Tree, both of which perform well for the prediction model.…”

Section: Introductionmentioning

confidence: 99%

Applicability of machine-learning techniques in predicting customer defection

Prasasti

Ohwada

2014

2014 International Symposium on Technology Management and Emerging Technologies

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Machine learning is an established method of predicting customer defection from a contractual business. However, no systematic comparison or evaluation of the different machine-learning techniques has been performed. In this study, we provide a comprehensive comparison of different machine-learning techniques with three different data sets of a software company to predict customer defection. The evaluation criteria of the techniques are understandability of the model, convenience of using the model, time efficiency in running the learning model, and performance of predicting customer defection.

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Customer Lifetime Value and Defection Possibility Prediction Model Using Machine Learning: An Application to a Cloud-Based Software Company

Cited by 6 publications

References 4 publications

EFSPredictor: Predicting Configuration Bugs with Ensemble Feature Selection

EFSPredictor: Predicting Configuration Bugs with Ensemble Feature Selection

Comparison Between Selective Sampling and Random Undersampling for Classification of Customer Defection Using Support Vector Machine

Applicability of machine-learning techniques in predicting customer defection

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