Open-ended survey responses provide valuable insights in marketing research, but low-quality responses not only burden researchers with manual filtering but also risk leading to misleading conclusions, underscoring the need for effective evaluation. Existing automatic evaluation methods target LLM-generated text and inadequately assess human-written responses with their distinct characteristics. We propose a two-stage evaluation framework specifically designed for human survey responses. First, gibberish filtering filters out nonsensical responses, then three dimensions—effort, relevance, and completeness—are evaluated using LLM capabilities, grounded in empirical analysis of real-world survey data. Validation on English and Korean datasets shows that our framework outperforms existing metrics and demonstrates high practical applicability for real-world applications across multilingual setting, showing strong correlations with expert assessment.