Customer churn prediction models using machine learning classification have been developed predominantly by training and testing on one time slice of data. We train models on multiple time slices of data and refer to this approach as multi-slicing. Our results show that given the same time frame of data, multi-slicing significantly improves churn prediction performance compared to training on the entire data set as one time slice. We demonstrate that besides an increased training set size, the improvement is driven by training on samples from different time slices. For data from a convenience wholesaler, we show that multi-slicing addresses the rarity of churn samples and the risk of overfitting to the distinctive situation in a single training time slice. Multi-slicing makes a model more generalizable, which is particularly relevant whenever conditions change or fluctuate over time. We also discuss how to choose the number of time slices.

使用机器学习分类的客户流失预测模型主要是通过对一个时间片的数据进行训练和测试来开发的。我们在数据的多个时间切片上训练模型，并将这种方法称为多重切片。我们的结果表明，在数据的相同时间范围内，与将整个数据集作为一个时间片进行训练相比，多切片显着提高了流失预测性能。我们证明，除了增加训练集大小外，改进是由对来自不同时间片的样本进行训练驱动的。对于来自便利批发商的数据，我们表明多重切片解决了流失样本的稀有性以及在单个训练时间片中过度拟合特殊情况的风险。多重切片使模型更具泛化性，这在条件随时间变化或波动时尤其重要。我们还讨论了如何选择时间片的数量。