Case Study: What Singaporeans Were Saying During GE2025

Earlier this month, we ran a short public survey (https://qumo.ai/s/cma3np3e70000 k304uxpdmbz4) to understand how people in Singapore were feeling during the GE2025 period. While the sample was small, the exercise gave us a great opportunity to showcase how Qumo makes it easy to analyse qualitative data at scale.

🧩 The Challenge

Election periods are emotionally charged, with a lot of nuanced opinions that don't always show up in quantitative data. We wanted to explore how Qumo’s conversational forms and built-in AI analytics could help uncover themes in public sentiment — quickly, and with minimal manual effort.

📋 The Approach

We created a short, open-ended survey using Qumo and shared it publicly on social media. The goal wasn’t statistical accuracy — it was to demonstrate how even a few rich, text-based responses can be meaningfully analyzed using Qumo.

Once the responses came in, we used Qumo’s automatic thematic coding feature to extract patterns and group answers into clear themes.

🔍 Key Insights

Despite the small sample size, a few consistent themes emerged:

🗳️ Thoughts on Political Candidates

• Skepticism toward newer or less-experienced candidates
• Concerns around diversity and representation
• A desire for more accountability and transparency

💬 Top 2 Concerns on People’s Minds

• Cost of Living
• The Economy

✨ Hopes for the Future

• More affordable housing and targeted social support
• Long-term planning for economic resilience
• Greater diversity of voices in the political landscape
• Stronger governance and accountability from leadership

⚙️ How Qumo Helped

This case study shows how even a simple, conversational form can produce valuable insights when paired with the right analysis tools. With Qumo, we were able to:

• Automatically tag and group open-text answers by theme
• Highlight common patterns across individual voices
• Flag irrelevant or off-topic responses that don’t contribute to analysis
• Scale qualitative research without needing a team of analysts

Qumo makes it easier for lean teams or time-sensitive projects to uncover what people are really thinking — especially when those thoughts are too complex for multiple-choice questions.

🎯 The Takeaway

You don’t need a massive dataset to start learning from qualitative feedback. With Qumo, even a few open-ended responses can unlock rich themes, emotional nuance, and actionable insights.

Disclaimer: This independent survey was conducted solely to demonstrate Qumo’s product capabilities in analyzing qualitative data. Qumo is not affiliated with any political party or the Singapore government. The views expressed reflect individual respondents and do not represent the views of the company.

📝 For more details and the full breakdown of responses, check out our LinkedIn post here.