We recently analysed 600 open-ended responses to a simple but powerful question: "What one change would most improve everyday life where you live?"
Using Redge Survey Analyser, we processed the responses in minutes — identifying themes, measuring sentiment, and uncovering patterns that would take days of manual coding.
What we found:
The responses clustered around three core domains that together represent the lived experience of community wellbeing:
🔹 Safety & security — Concerns about feeling safe in public spaces and neighbourhoods
🔹 Physical infrastructure — The tangible environment people navigate daily, from road quality to transport access
🔹 Economic pressure — Household financial stress, from grocery bills to housing costs
What makes this analysis powerful isn't just the speed — it's the granularity. We can see how concerns vary across demographics, geographies, and life stages. We identified 15+ distinct themes, tracked sentiment across the entire dataset, and flagged the 7% who reported being completely satisfied (an equally valuable insight for understanding what's working).
The qual-quant advantage:
Traditional surveys force respondents into predetermined categories. Open-ended questions let people tell you what actually matters to them — in their own words.
AI thematic analysis makes this scalable. You get the depth of qualitative research with the statistical rigour of quantitative data. Every theme is quantified. Every insight is evidence-based.
Why this matters for research:
- Whether you're working in community consultation, customer experience, employee engagement, or policy development — the combination of open-ended questions and AI analysis unlocks insights that closed questions simply can't reach.
- The verbatims tell you what people think. The thematic analysis tells you how many think it. The cross-tabulations tell you who thinks it. Together, they give you the full picture.
This analysis was conducted using Redge Survey Analyser, which combines natural language processing, sentiment analysis, and statistical cross-tabulation to help us understand both the 'what' and the 'why' in survey data.
Interested in bringing AI-powered analysis to your next research project? Let's talk.