# Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data
## 2 Literature Review
[35] considered the case of a Mexican company that sells parcel-delivery as a prepaid service to business clients. Clients are able to purchase the desired number \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]-** & **[PERSON]** & **[PERSON]** & **This Study** \\  & **et al.**[34] & **et al.**[35] & **et al.**[9] & **et al.**[18] & **et al.**[12] & **et al.**[12] & **et al.**[36] & **This Study** \\ \hline \multirow{3}{*}{Domain} & \multirow{3}{*}{Logistics} & \multirow{3}{*}{Logistics} & Wholesale & Wholesale & Retailer (fast moving & \multirow{3}{*}{Retailer (beverages)} & \multirow{3}{*}{\begin{tabular}{c} Wholesale \\ (agricultural \\ goods) \\ \end{tabular} } \\  & & Logistics & \begin{tabular}{c} Wholesale \\ (fast moving \\ consumer \\ goods) \\ \end{tabular} & \begin{tabular}{c} Retailer (fast moving \\ consumer \\ goods) \\ \end{tabular} & \begin{tabular}{c} Retailer (fast \\ moving \\ consumer \\ goods) \\ \end{tabular} & \begin{tabular}{c} Wholesale \\ (agricultural \\ goods) \\ \end{tabular} \\ \hline Dataset span & 29 months & 40 months & 12 months & 30 months & 12 months & 31 months & 38 months \\ \hline \# of Customers & 69,170 & 1968 & 80,000 & 5000 & 11,021 & 41,739 & 3470 \\ \hline \begin{tabular}{c} Churn definitions \\ \end{tabular} & 1 month & 3, 7 months & 12 months & 3 months & 6 months & 12 months & 1, 2, 3 months \\ \hline Churn rates & 2\% & 4–19\% & 10\% & 7–15\% & 28\% & 4\% & 5–38\% \\ \hline \multirow{3}{*}{Data sources} & \multirow{3}{*}{Transactions, QoS} & \multirow{3}{*}{Transactions} & Transactions, & Transactions, & \multirow{3}{*}{Transactions, QoS, CRM} & \multirow{3}{*}{Transactions} & \multirow{3}{*}{Transactions} \\  & & & data & & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Features extracted \\ \end{tabular} } & \multirow{3}{*}{LRFMP} & \multirow{3}{*}{F} & \begin{tabular}{c} LRFM, QoS, \\ platform \\ usage \\ \end{tabular} & \multirow{3}{*}{LRFM, QoS} & \multirow{3}{*}{RFM} & \multirow{3}{*}{LRFM} & \multirow{3}{*}{LRFM} & \multirow{3}{*}{LRFM} \\  & & & & & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Feature window \\ \end{tabular} } & \multirow{3}{*}{Fixed} & \multirow{3}{*}{Variable} & \multirow{3}{*}{Fixed} & \multirow{3}{*}{Fixed} & \multirow{3}{*}{Fixed} & \multirow{3}{*}{Variable} \\ \hline
\begin{tabular}{c} Training set creation \\ \end{tabular} & \multirow{3}{*}{Single-slicing} & \multirow{3}{*}{Single-slicing} & \multirow{3}{*}{Single-slicing} & \multirow{3}{*}{Multi-slicing} & \multirow{3}{*}{Single-slicing} & \multirow{3}{*}{Multi-slicing} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relevant studies overview. of delivery units from the company at any point in time and then consume them at their discretion, thus making this a non-contractual B2B scenario. The authors experimented with different definitions of churn (i.e., inactivity of customers in consecutive future time periods) and used inventory level-based (i.e., amount of services available) time series of varying lengths to derive features that are fed to selected machine learning algorithms in order to predict whether a client will be active or not. In particular, the authors extracted trend and level, magnitude, auto-correlations, and Fourier coefficients (as derived by fast Fourier transform) and used them as features. The dataset comprised transactions made by 1968 clients who ordered and spent services in a period of just over three years (between January 2014 and April 2017), among which, depending on the churn definition used, there were between 56 and 346 churners. The authors reported that Random Forest (RF) outperforms SVM, AdaBoost, and k-Nearest Neighbors (kNN) classifiers for the majority of time series lengths and churn definitions used when evaluated on specificity, but that SVM also performs acceptably over the majority of combinations when balanced accuracy is considered. [PERSON] et al.