Customer needs / insights report

Decision Run AI-moderated qualitative interviews to bridge the gap between what quantitative surveys reveal (patterns) and what they cannot explain (emotional drivers, decision nuances, unmet expectations). Why qualitative (after a survey) Part 1 of this research established quantitative patterns via a national survey. But surveys don't fully explain why customers behave the way they do. To move from "what" to "why," we needed structured conversations that uncover emotional drivers, decision-making nuances, and unmet expectations. Research framework A research guide serves as the blueprint for qualitative interviews — ensuring structure while allowing flexibility, and optimizing AI's ability to refine questions in real time based on participant responses. Step 1 — Define research objectives - Understand pricing transparency: what builds trust with brides-to-be? - Examine digital trust: what online signals influence venue credibility? - Explore booking barriers: what drives decision fatigue? Step 2 — Establish methodology - Format: AI-moderated 1:1 interviews. - Participant criteria: anchored to an Ideal Customer Profile (ICP). - AI vs human moderation: an AI moderator conducts + adapts; researcher synthesizes. Step 3 — Data collection & analysis - Real-time AI adaptation: questions adjust based on participant responses. - Thematic categorization: AI groups responses into pre-defined research themes. - Highlight reels: shareable video clips of frustrations, surprises, and hypothesis validations. - Sentiment analysis: positive / neutral / negative tagging on every response. Step 4 — Ethics & guidelines - Informed consent. - Privacy + data-handling protocols. - Researcher checkpoints between waves. Example: when a participant expresses frustration with hidden pricing, the AI moderator flags "pricing transparency" as a recurring theme, adapts follow-up questions, and surfaces patterns across participants. Outcomes (what made it into the product vision) - 13 AI-moderated bride interviews completed. - Emotional drivers behind Gen Z venue selection mapped. - Decision-fatigue triggers identified (hidden pricing was #1). - Aesthetic-discovery behaviors documented (Pinterest + TikTok dominate inspiration). - Mixed-funding-model financial behavior surfaced (couple + parents + savings). Why this matters This research methodology is reusable. The same framework can be applied to any product where quant data outlines patterns but emotional drivers remain opaque. Pair a survey with AI- moderated interviews; you get the breadth of one and the depth of the other.