## 1 Introduction  
Questionnaire survey research is one of the most significant approaches utilized in marketing research. Survey research methods are generally carried out online or offline. Respondents rate questionnaires with five- or seven-point Likert scales for products, advertisements, and their demographic and psychographic information. According to European Society for Opinion and Marketing Research (ESOMAR) [1], quantitative methods, including the questionnaire survey research, are major marketing research approaches that account for more than 60% of the marketing research methods used for sales volume. Although survey research methods play a crucial role in marketing management and represent the prevailing approach toward gathering consumer opinions, several significant issues related to these methods must be resolved. The first problem is the cost issue. The larger the sample size, the closer the acquired data from the survey are to the population. However, the cost of executing survey research methods increases according to the sample size. This suggests that enterprises with a small marketing research budget cannot obtain accurate results from survey research. In many cases, small-size enterprises (e.g., start-up companies and those placed in local areas) cannot afford to invest sufficient budget for executing survey research with a large sample size [2]. As a result, these companies struggle to achieve consumer insights when developing new products and services. They are eventually obliged to execute marketing initiatives restricted to their closed knowledge. Previous studies on small business firms have pointed out that lacking a marketing attitude leads to business failure [3; 4]. This suggests that the marketing initiatives of these businesses might be equal to gambling without considering the consumer demands. This marketing research issue in small business firms has remained unsolved for quite a long time now. The second issue concerns rare populations [5; 6]. Many cases have targeted limited markets in marketing strategies. These include specialists in particular industry areas (i.e., surgeons, artists, and craftsmen), class groups (i.e., businesspeople with high salaries), and people with attributes (i.e., intractable disease patients and luxurious brand customers). Executing survey research for these rare populations is challenging. This issue is hard to solve in terms of budgeting alone and remains unresolved in the marketing research area. The third issue is the inconvenience of analyzing small-sample-sized data using statistical models, such as predictive models and some statistical testing methods. When predicting outcomes using customer data, several predictive models with small sample sizes could yield low-accuracy results. When performing structural equation model analysis with small-sample-sized data, several parameters in the model might not be calculated. Driver analysis is a useful analytical approach for making marketing decisions based on consumer survey research. Machine learning models, such as linear discriminant model analysis and regression models, can be employed for driver analysis to obtain effective variables for purchasing and preferring products and brands. The results of using small-sample-sized data for driver analysis may be inconsistent with those for a large-sample size. Therefore, the results of analyses using small-sample-sized data are irrelevant when making marketing decisions. Several techniques for augmenting small-sample-sized data have been presented to overcome the small sample size issue in the image-processing field. The data augmentation techniques are classified into two categories [7]. The first category comprises traditional data augmentation techniques such as cropping, flipping, noise transformation, and random erasing. This approach creates various patterns of seed data using parts and edits them according to certain rules. The Mixup is a more sophisticated technique than traditional techniques [8].