## 3 Results
### Validation Results
#### 3.2.2 Evaluating the Practicability of Data Augmentation
Although data generated by GAN-based synthesizers led to better results concerning performance indices, the TVAE results showed a closeness between the synthesized and original data. This suggests that TVAE might be a better synthesizer because of its closeness to the original data. The drawbacks identified using these results need to be investigated in future research. The concordance analysis results revealed a discrepancy between the original data and other datasets, despite the improved predictive performances observed in augmented and synthesized data compared with the real data (seed and original data). This implies that achieving proximity to the values and orders of feature importance in the original data through data augmentation and synthesis is challenging. Consequently, there are concerns regarding the divergence in marketing decisions based on feature importance calculated using augmented and synthesized data compared with those based on the original data. Considering that the degree of concordance of feature importance between the original and other datasets may fall at approximately 85% or lower, marketers should exercise caution when making marketing decisions based on driver analysis, considering these observed discrepancies. While our findings suggest that reproducing the feature importance observed in the original data presents challenges, augmented and synthesized data related to CopulaGAN exhibited correlation coefficients higher than the average, surpassing 0.7, unlike other DGNNs, which yielded coefficients below 0.7. This indicates that CopulaGAN, with the incorporation of copula function during processing, tends to perform better in achieving concordance with feature importance observed in the original data. This observation aligns with previous studies [28; 32], suggesting that CopulaGAN might demonstrate superior performance in concordance analysis compared with other DGNNs, particularly owing to its effectiveness in handling correlated variables commonly found in consumer survey data. Therefore, the synthesized data generated using CopulaGAN might effectively augment data using dependencies between variables in table data other than financial data. In general, the usage of data augmentation and synthesis techniques can mitigate the reliance on low-credibility data and bolster the implementation of more effective marketing strategies by improving predictive performance. However, a cautious approach may be necessary when assessing the alignment of feature importance through these methods. Implementing the findings of this study into marketing research practices could alleviate the financial burden associated with acquiring large sample sizes and soliciting sensitive information in consumer surveys. In addition, the synthesized data hold promise for establishing a highly accessible and secure consumer survey database. These findings may offer valuable insights for small- and medium-sized organizations with limited marketing budgets seeking to conduct accurate marketing analyses. Our study might present useful findings to practitioners and academicians, but we have several limitations and cautions. In this work, we assessed the usefulness of the data augmentation approach in terms of a classification task alone. Although the classification task is a major approach in marketing analysis, many conventional marketing analysis methods are available (e.g., regression task, factor analysis, and the structural equation model) [56]. Our results are insufficient for application to various marketing issues; thus, further validations regarding the applicability of the data augmentation approach in marketing analysis are needed. The data in this study were well balanced. However, in several marketing research cases, imbalanced data have been observed. Because effective approaches to overcoming imbalanced data issues have been developed to improve the accuracy of deep learning algorithms [57], these approaches need to be applied to handle imbalanced survey data in future studies.