# Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data
## 6 Conclusions
### Limitations and Future Research Directions  
To conclude this paper, we would like to highlight some of the limitations pertinent to our study, as well as to propose several future research directions.  
In terms of data, we were limited by the fact that our data comes from a single company operating in the Balkans region dealing with agricultural goods and equipment. While the results we obtained seem to be in line with previous findings, it may be the case that they would not entirely hold for companies operating in different domains or geographical regions. Hence, even though this research is a step forward in terms of evidence that the multi-slicing approach generalizes well, additional studies covering more domains and business environments would certainly be even more beneficial to strengthening this conclusion.  
We explored how different amounts of historical data for constructing _DF_ affect the predictive performance of models, but did not account for the possible effects of having fewer overall observable months of data (i.e., shorter _CF_ period). While the contemporary attitude in the era of big data appears to be \"the more the better\", some studies suggest that beyond a point, using more historical data does not lead to better performing models [42] and, depending on the design and resource requirements of the feature construction and model training processes, might in fact only mean more complexity while yielding only marginal gains in terms of model performance. Investigation into whether this holds for the dataset we have on hand is an avenue for future research.  
Seasonality and changing purchase patterns are phenomena many companies experience in their operations. In this study, we used only the most recent data to derive churn labels (for all considered definitions), which is in line with the established practice in the domain. However, even though the multi-slicing approach implicitly addresses the issue of models devised at different prediction points in time (by creating'snapshots' of customers at different possible prediction points), it would be interesting to check whether and to what extent this holds by devising models for high- and low-churn rate periods and assessing their performance.  
The final limitation to this research pertains to the algorithms chosen for devising predictive churn models. We have opted for three established classifiers in this domain, as they have been demonstrated repeatedly to produce models applicable in real-world settings and they allowed us to easily compare our results to previous studies. However as deep-learning approaches gain more traction, it would be interesting to see whether and to what extent they could be leveraged when applied in this domain as a part of future research efforts. It would also be useful to gain more detailed insights into the reasons behind customer churn by employing some of the recent methods for explainable machine learning.  
Conceptualization, M.M.; Methodology, M.M., D.G., and T.L.; Software, M.M. and A.A.; Formal analysis, M.M. and A.A.; Data curation, T.L. and D.G.; Writing--original draft preparation, M.M., T.L., and A.A.; Writing--review and editing, D.S., M.M., and D.G.; Visualization, M.M.; Project administration, D.G.; Funding acquisition, D.S. and M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research has been supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, through project no. 451-03-68/2022-14/200156 \"Innovative scientific and artistic research from the FTS (activity) domain\".  
The authors declare no conflict of interest.