# Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data
## 2 Literature Review  
Customer churn prediction modeling has often been the focus of researchers, as evidenced by numerous studies published on this topic. 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. Within the contractual settings in this domain, approaches have been proposed to identify business clients who are likely to close all contracts with a financial service provider [29], business customers who are least likely to renew a subscription to a software service [30; 31; 32], or the probability of corporate users switching to a different B2B telecommunications service provider given a set of incentives [33]. Non-contractual B2B settings have started receiving more interest fairly recently, where efforts are being made to help companies identify customers at risk of leaving. However, even though some general guidelines in terms of the most promising approaches to the problem can be inferred from relevant studies, it may be difficult for practitioners to decide which approach (or combination of approaches) to use, as there is significant variability in methods used to create models (distinct algorithms and hyperparameter values used), leveraged data sources (spanning transactional, CRM, quality-of-service, and E-commerce systems), characteristics of raw datasets (in terms of the time span they cover, number of customers, and churn rates), and approaches to deriving features. This is best illustrated within Table 1, where we provide an overview of relevant studies with respect to:* Raw data characteristics (domain they come from, time period they span, number of customers included, and churn rates);
* Source systems the data were extracted from (transactional, quality-of-service (QoS), Customer Relationship Management (CRM), and web data);
* Churn definitions used (single or multiple);
* Types of features extracted (L--length, R--recency, F--frequency, M--monetary, P--profit);
* Type of feature extraction window considered (fixed or variable);
* Approach to creating the training dataset (single-slicing or multi-slicing). In the remainder of this section. we describe each of the studies in more detail and put them in the context of gaps we aim to address within present study. [PERSON] et al. [34] examined the importance of length, recency, frequency, monetary, and profit (LRFMP) variables for predicting churn in the case of one of the largest logistics companies in Taiwan. The company defines lost business customers (i.e., churners) as those who did not engage in any transactions in the past month. The dataset (after applying business-domain knowledge and relevant filtering) comprised 69,170 business customers, among which 1321 were churners. The authors applied common binary classification techniques for the domain--Decision Tree (DT), feed-forward Multi-Layer Perceptron neural network (MLP), Support Vector Machines (SVM) and Logistic Regression (LR)--to assess their effectiveness in predicting churn. Their experiment showed that the DT model is able to achieve superior results compared to other models on all reported measures (accuracy, precision, recall, and F1) and they report that the top three most influential predictors were recency of purchase, length of the relationship (i.e., tenure), and monetary indicator (i.e., amount spent). [PERSON] et al.