# Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data
## 1 Introduction  
### Background  
Companies across virtually all industry branches have long since recognized the importance of keeping their customers engaged and active, as that directly translates into more revenue and reduces the overall costs, especially given the fact that it can be several times more expensive to attract a new customer than to retain an existing one [1]. However, since customers tend to explore different offers and options on the market and are always on the lookout for better deals and opportunities, understanding when they are about to terminate further transactions with a company is paramount for formulating effective and efficient strategies to try and persuade them otherwise. The phenomenon when a customer stops making purchases from a company (that is, when they stop buying products or paying for the services a company offers) is known as customer churn and the ability to predict it accurately can have significant implications on different processes across the organization (e.g., marketing, sales, procurement), as well as on the overall profitability [2]. However, even though identifying customers who are at risk of leaving is recognized as one of the key prerequisites for devising retention activities [3], there are many complexities pertinent to just defining churn, which stem from the fact that numerous contexts and business models exist when it comes to organizations operating in distinct domains and environments [4]. For example, companies leveraging contractual business models (such as those offering subscriptions to services or products) might be able to directly observe customer churn (when a subscription expires and is not renewed or is terminated by acustomer), but need to decide whether to take into account all subscriptions a customer might have (total or complete churn) or just those pertinent to particular groups of services or products (partial churn) [5]. 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]. These complexities are further augmented by the fact that customer characteristics and behavior can vary quite substantially depending on whether a company is operating in a business-to-business (B2B) or a business-to-consumer (B2C) domain [8], which needs to be taken into account when devising churn prediction models and retention strategies. B2B companies usually have fewer customers that make larger and more frequent purchases compared to their B2C counterparts [9], so retaining even a single customer in this context can make a significant difference to the financial bottom line of a company [10]. This is at odds with findings that B2B companies have traditionally struggled with data gathering and analysis [11] and that they have exhibited inertness when it comes to utilizing modern customer relationship analytics that leverage 'big data' [12].