Bubble SaaS User Research
Customer interviews, session recordings, churn interviews, and in-app micro-surveys — five research methods ranked by signal quality, with full Bubble implementation for contextual surveys that surface the real reasons users do not convert.
Every Hour of User Research Saves Ten Hours of Wrong Building
The most common reason Bubble SaaS products fail is not technical — it is building the wrong thing for the wrong customer at the wrong price. User research is the practice of systematically finding out what customers actually want, need, and will pay for before building it. In 2026, with AI tools that can synthesise interview transcripts and session recordings that reveal behaviour rather than stated preferences, user research is faster and more actionable than it has ever been.
Five Research Methods Ranked by Signal Quality
Customer Interviews
30-minute structured conversations with current and potential customers. The highest-quality signal available. Ask about problems, not solutions. “Walk me through the last time you faced [problem].” Record and transcribe. Patterns across 10 interviews reveal your true product priorities.
Session Recordings
Hotjar or FullStory shows you what users actually do, not what they say they do. These are different. A user who says “the app is great” but never clicks the feature you most want them to use is giving you more valuable information through their behaviour than through their words.
Support Ticket Analysis
Your support tickets are categorised user pain. Every ticket represents a problem significant enough that the customer reached out. Categorise every ticket from the past 90 days by problem type. The most frequent categories are your highest-priority product investments.
Churn Interviews
Call every customer who cancels within 48 hours of cancellation. Ask “what would have made you stay?” and “what did you switch to?” This is the most uncomfortable research and the most valuable. Patterns across churn interviews reveal the single biggest product gap preventing retention.
Feature Usage Data
Which features are used daily? Which are never touched? Mixpanel or Amplitude shows you feature adoption rates across your user base. Features with high adoption deserve deepening. Features with low adoption despite being heavily invested in deserve a conversation: why did users not adopt this?
In-App Surveys
Single-question surveys triggered by specific events: “How did you hear about us?” at signup, “Why didn’t you invite your team?” at day 7 without an invitation, “What almost stopped you from upgrading?” immediately after a plan upgrade. Specific questions at specific moments yield dramatically better data than generic feedback forms.
Building In-App Research Tools in Bubble
UserSurveyResponse:
user → User
workspace → Workspace
survey_key → text (identifies which survey)
question → text
response → text
triggered_by → text (what event triggered the survey)
created_at → date
// Show survey 7 days in, no invitation sent
Survey modal visible when:
Workspace’s created_date < 7 days ago
AND Workspace’s seats_used = 1
AND User’s survey_invite_shown = no
Survey question: “What’s holding you back from inviting your team?”
Options:
A: “I’m still evaluating the product”
B: “My team isn’t ready yet”
C: “I want to set it up first”
D: “We work solo, I don’t have a team”
// On response: store and update User flag
On response submitted:
Create UserSurveyResponse with selected option
Update User: survey_invite_shown = yes
// Route D respondents away from team-invite prompts permanently
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