###### Abstract
In addition, a locally optimal DCE was constructed for our choice sets using principles from blocked fractional factorial designs (BFFD) readily available, as opposed to alternative approaches that might be more technically challenging, especially when the number of characteristics is large. In a typical DCE, respondents are shown samples of hypothetical scenarios that were selected a priori from all feasible scenarios using statistical design approaches. In each scenario, each respondent must choose only one answer. Options for each scenario include a number of attributes with at least two levels. It is anticipated that the respondent would select the choice that would benefit them the most, or the option with the highest utility. The degree to which each feature influences the decision maker's choice is measured by ensuring a specific amount of variety in the scenarios [9]. Numerous study areas, such as marketing science [10], energy prices [11; 12] infection contact tracing in healthcare [13], healthcare drug administration [14; 15], sustainable agriculture [16; 17], tourism development [18; 19], sustainable investment from private investors [19], food research [20], human resource policy intervention [21], and transportation infrastructure management [8], have found use for discrete choice experiments. More particularly in this study, the use of DCE is centered on telecoms, with a summary of studies presented in Table 1 below. \begin{table}
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\begin{tabular}{c} **\%** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related studies on the application of DCE. From Table 1, discrete choice analysis has been applied in the context of telecom to elicit customers' preferences but scarcely using ML models. The multinomial logit (MNL) model proposed by [PERSON] [26] is the most popular model for discrete choice analysis. With the MNL model, choice probabilities have a closed form and are easily interpretable [27]; however, strict statistical assumptions imply that the model capture all sources of variability and correlation between alternatives (and individuals) using a linear combination of covariates [28]. Furthermore, according to [PERSON] et al. [29], the MNL model formulation requires domain knowledge to incorporate sources of heterogeneity and to introduce non-linearity on the attributes of the alternatives. Different versions of the MNL model have been proposed in a bid to add more flexibility to its underlying assumptions. The most common approach to capture that response heterogeneity in mode choice is the mixed multinomial logit (MMNL) models [30; 31; 32]. However, compared to classic statistical models, ML approaches are a viable alternative for choice modeling. They provide an alternate approach to standard prediction modeling that may overcome existing shortcomings. By using data-driven learning to represent complex relationships between variables, machine-learning techniques avoid making strong prior assumptions [33]. ML models are more flexible and have better prediction accuracy because they learn from the data [34]. They are able to represent  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Refs** & **Summary of Studies** & **Attributes Used** & **DCE** & **DCE with Other Models** \\ \hline \multirow{4}{*}{[22]} & A sample of Polish people’s subscription choices between standardised packages of fixed-line telecommunications services. Willingness-to-pay estimates were found for the attributes.