## 6 Discussion  
This research focuses on elucidating the primary contributions and limitations, utilizing a deep learning architecture to predict individual-level behavior across datasets with diverse characteristics. We achieved the best results in terms of accuracy with the GRU model, which is a type of recurrent neural network that can capture temporal dependencies. This model allows us to make predictions for each week during the calibration period, which reveals the overall trends of user and customer behavior. These predictions can help enterprises make decisions based on future expectations rather than past patterns. This can enhance the understanding of individual-level behavior and enable better strategies for engagement and retention. For instance, the aggregated results can help identify trends at the managerial level, while the individual-level predictions can enable customer segmentation and churn detection at a more personalized level. Thus, the deep learning approach has great potential for optimizing business strategies and improving customer experiences. However, we also note that scalability issues may arise when dealing with large data and many individuals. In comparison, the widely used BTYD models, which are statistical models typically based on some statistical distributions, do not learn any patterns. In this study, we observed that the BTYD models provided better MAE and RMSE values. However, the bias results indicated potential misleading tendencies; and these values may still be considered successful. If we apply the proposed methodology on data with less zero values at individual level (e.g., less inactivity of users), we might have better MAE and RMSE values. Also, as [PERSON] et al. [19] mentioned in their study, if we encounter more frequent cases, employing LSTM could lead to greater benefits. On the other hand, when the model predicts logins or transactions more than zero for the users or customers who have zero logins or transactions, it might determine the users or customers who are more likely to make logins or transactions. These individuals might be treated differently by decision makers. Considering the nature of the datasets, where a significant portion of users had zero logins during most of the calibration period, predicting user activity as active or not may hold more value than calculating the difference between login counts in various cases. In addition to BTYD, several methods have been proposed for individual-level predictions. [PERSON] and [PERSON] [17] model the temporal evolution of RFM values, [PERSON] et al. [12] use binary outcomes as targets, and [PERSON] and [PERSON] [23] forecast single-point values. These studies address important aspects such as handcrafted features, RFM interdependencies, and target diversity. The framework proposed in this study can be adapted to various target settings and benefit from incorporating handcrafted features. However, integrating such features might be challenging for companies with large datasets and real-time needs. In these cases, invariant features may be more practical than time-varying features. Additionally, the impact of these covariates may vary across different types of datasets (e.g., e-commerce data, mobile application usage data). Future research could explore how BTYD and deep learning models might complement each other. Investigating hybrid or ensemble models might enhance both individual-level and aggregate results. Furthermore, future studies should focus on effectively integrating covariates across diverse datasets. Incorporating additional features (e.g., variables that determine economic trends, socioeconomic variables) that may influence individual behavior could also be beneficial. Moreover, addressing scalability challenges is crucial; therefore, future research might investigate various segmentation strategies to manage large datasets more effectively.