Churn in the mobile gaming field: Establishing churn definitions and measuring classification similarities

Perišić, Ana; Jung, Dubravka Šišak; Pahor, Marko

doi:10.1016/j.eswa.2021.116277

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…Particularly well-explored are the contractual business settings in the B2C domain, such as those commonly encountered in the telecommunications [19][20][21], banking [22,23], and insurance [24,25] sectors, where customers at risk of terminating or not renewing their contracts are identified and targeted with retention campaigns in efforts to persuade them otherwise. Non-contractual settings have also often been studied, where efforts have been put towards predicting which retail customers are least likely to make a purchase in the future [26,27], which users are at most risk to stop playing mobile games [6], or which passengers are not planning to use a particular airline for their future flights [28]. The B2B domain, on the other hand, has received less attention so far.…”

Section: Literature Reviewmentioning

confidence: 99%

“…− DF num }, where DF num is the length of the observation period for DF in months. We use DF num values of [1,3,6,9,12], which correspond to time periods usually used for comparisons in business settings (i.e., last month, last quarter, two previous quarters, three previous quarters, and last year). This way, we are able to explicitly quantify changes in purchasing patterns of customers between the latest observation period and previous periods, as well as to capture seasonality in their behavior.…”

Section: Features Training and Test Setsmentioning

confidence: 99%

“…Companies operating in non-contractual environments (such as retail or wholesale) have an even more difficult task, since there is no way to explicitly observe churn due to the fact that customer purchasing frequencies or payments are not known in advance and they are free to transact with the company whenever they wish. This implies that one of the biggest challenges faced by organizations relying on this business model face is to determine a meaningful time period to use for defining a customer as lost (e.g., if no purchases are made in three consecutive months, then a customer is considered a churner), as this definition will affect all further modeling efforts and classification results [6]. It is also one of the main reasons for the disproportion that can be observed when the number of studies focusing on contractual business settings is compared to the number of those exploring cases where formal contracts between a company and their customers do not exist (i.e., non-contractual business settings) [7].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

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Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data

et al. 2022

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Customer churn is a problem virtually all companies face, and the ability to predict it reliably can be a cornerstone for successful retention campaigns. In this study, we propose an approach to customer churn prediction in non-contractual B2B settings that relies exclusively on invoice-level data for feature engineering and uses multi-slicing to maximally utilize available data. We cast churn as a binary classification problem and assess the ability of three established classifiers to predict it when using different churn definitions. We also compare classifier performance when different amounts of historical data are used for feature engineering. The results indicate that robust models for different churn definitions can be derived by using invoice-level data alone and that using more historical data for creating some of the features tends to lead to better performing models for some classifiers. We also confirm that the multi-slicing approach to dataset creation yields better performing models compared to the traditionally used single-slicing approach.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Features Training and Test Setsmentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%

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Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data

et al. 2022

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“…Customer churn is a common problem across businesses in numerous industries, including finance [ 1 ], news [ 2 ], insurance [ 3 ], online mobile gaming [ 4 ], telecommunication [ 5 ], and online gambling [ 6 ]. According to [ 7 ], churn management is the concept of identifying those customers who intend to move their custom to a competing service provider .…”

Section: Introductionmentioning

confidence: 99%

Customer retention and churn prediction in the telecommunication industry: a case study on a Danish university

Saleh

Saha

2023

SN Appl. Sci.

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In this study, we explore the possible factors affecting churn in the Danish telecommunication industry and how those factors connect with retention strategies. The Danish telecommunication industry is experiencing a saturated market regarding the number of customers, but the number of service providers has increased significantly in recent years. Due to the high costs of acquiring new customers, the telecommunication industry put great emphasis on retaining customers in such an intensely competitive industry. We employ five machine learning algorithms: random forest, AdaBoost, logistic regression, extreme gradient boosting classifier, and decision tree classifier on four datasets from two geographical regions, Denmark and the USA. The first three datasets are from online repositories, and the last one contains responses from 311 students from Aalborg University collected through a survey. We identify key features extracted by the best-performing algorithms based on five performance measures. Based on that, we aggregate all the features that appear important for each dataset. The results demonstrate that customers’ preferences are not aligned. Among the prominent drivers, we find that service quality, customer satisfaction, offering subscription plan upgrades, and network coverage are unique to the Danish student population. Telecommunication companies need to integrate the sociohistoric milieu of the Nordic countries to tailor their retention policies to different consumer cultures.

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Two-Group k-Adic Similarity Coefficients for Binary Classifiers

Ana,

Sophie

2024

J Classif

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Churn in the mobile gaming field: Establishing churn definitions and measuring classification similarities

Cited by 8 publications

References 23 publications

Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data

Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data

Customer retention and churn prediction in the telecommunication industry: a case study on a Danish university

Two-Group k-Adic Similarity Coefficients for Binary Classifiers

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