# Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data
## 2 Literature Review
They calculated the potential profit to be made at a customer level, provided that they respond favorably to an offer within the retention campaign and maintain average spending levels in the prediction period, which they then use as a sorting criterion for creating lists of customers to offer incentives to. An integral part of that calculation is the probability of a customer to become a churner, which is obtained via predictive churn models devised using DT and LR classifiers (in case of DT, they consider simple, cost-sensitive, and boosted variants). Two other important components of the calculation are the probability that a customer accepts the offer (which is kept constant across entire customer base at 30%) and the magnitude of incentive (the authors operate within a scenario where a 5% discount is offered). They then proceeded to test the proposed approach on a real-world dataset of 11,021 B2B customers of a major Australian online fast moving consumer goods retailer who made transactions within the span of one calendar year. Churn is defined as inactivity (no purchases made) in 6 consecutive months, with a reported churn rate of 28%. The authors reported that the boosting approach outperforms LR and simple and cost-sensitive DT, and that using this method for sorting and selecting potential churners can lead to significant business effects. They also identified recency and frequency as the most important predictors of churn. Most recently, [PERSON] et al. [36] proposed a novel measure that can be used to increase the profitability of retention campaigns called EMPB (Expected Maximum Profit measure for B2B customer churn). Unlike in [12], where all customers are treated as equals, the authors of this study took into account the variability in customer base (i.e., high-value vs. low-value customers), which they proceeded to show can be leveraged to create retention campaigns that maximize expected profits. They compared the performance of customer churn prediction models devised with respect to the proposed measure and concluded that it can yield considerable and measurable business gains compared to traditionally-used metrics such as AUC. They used a dataset obtained from a large North American beverage retailer comprising purchases of 41,739 B2B customers spanning 12 months, out of which roughly 4% are churners, to create predictive models using algorithms such as XGBoost, ProffLogit, Proffree, RF, and LASSO regression that leverage this measure to recommend customers to be included in retention campaigns to maximize profits. The most important features that the authors identified were monetary value and recency, as well as purchase quantity and the average difference in days with respect to the due date for handling reported issues (QoS). As shown in this section, relevant studies consider a range of different case data that exhibit distinct characteristics in terms of the domain that they come from, the number of customers and time frame that they span, the churn definitions used, and churn rates reported. Source systems vary from transactional, CRM, and QoS, to web platform usage, and they are leveraged to construct pertinent informative features using a mostly-fixed window width and a single-slicing approach to dataset creation. The exception to this are [35], where variable window widths are used, and [18], where multi-slicing is assessed. What differentiates this study from all studies previously mentioned is the fact that it is, to the best of our knowledge, the first study to explore the effects of using different churn definitions and variable window widths for feature extraction and a multi-slicing approach to dataset creation on predictive model performance in one place, thus providing valuable insights on the most promising approaches to practitioners.