###### Abstract
Many studies have demonstrated that ML approaches outperform logit models in terms of predictive power [35; 36; 37; 38]. The cited studies have made substantial contributions to the use of ML in discrete choice issues. [PERSON] et al. [39] used an SVM model to study the behavior of public transportation choice. In a large-scale problem, [PERSON] et al. [40], used SVM to estimate the household travel choice mode. [PERSON] et al. [41] simulated travel mode decisions using the classification tree (CT) technique under dynamic behavioral processes. [PERSON] et al. [42] estimated mode choice based on the location and socioeconomic characteristics using CT and other approaches. The random forest (RF) ensemble method has been employed by other scholars such as [PERSON] et al. [43], who looked at the mode choice behavior of travelers in Delhi. [PERSON] et al. [36], also investigated the interpretation and predictive power of an RF to simulate travel mode decision behavior. The extreme gradient boosting (XGBoost) model is another ML method that has been applied in travel demand prediction. [PERSON] and [PERSON] [44] employed XGBoost to analyze data from transport mode choice research carried out in the Delaware Valley region. Neural networks (NNs) have also been applied to predict modal choice [41]. All these studies demonstrate the predictive power of ML techniques, but most of them also critique the black-box system that underlies the predictions they make. Hence, a key benefit of logit models which makes them more attractive than ML, is its interpretability ascertained from the regression coefficients. However, in recent times, a number of travel behavior modeling studies [35; 36; 44], have investigated the possibility of using model-agnostic methods, like permutation feature importance, to extract interpretable information from ML models. Despite the wide application of discrete choice modeling, as can be seen in Table 1, there is sparse application of DCE in telecom and scarcely any using deep-learning models. Hence, the objective of this study is to employ two deep-learning models (artificial neural network (ANN) and the extreme gradient boosting (XGBoost)) rather than the traditional econometric models (such as MNL), to analyze discrete choice data. The discrete choice experiment (DCE) investigated customers' preferences for mobile plan using a South African university as case study. It builds upon a previous study in [24], that utilized blocked fractional factorial designs to construct locally optimal DCE for the choice sets. Root mean square error (RMSE) and mean absolute error (MAE) were used to assess the models' fitness, while accuracy, precision, recall and F1 score were used to compare the models' performance. Having applied the deep-learning models to the discrete choice analysis of mobile plan preferences of customers, it is easier for the applicability of these deep-learning models in other areas and cases, hence, overcoming the constraints of traditional approach in DCE and achieving higher model accuracies. This study is envisaged to have decision-making implications for telecommunication companies in enhancing customers satisfaction. The remaining part of this article is organized as follows: the study's methodology is presented in Section 2. Section 3 presents results and discussion, while Section 4 presents concluding thoughts.