Two products, the same question: what does someone need to believe is true before they'll behave honestly?
SuperJ 2.0 has to earn trust from people who've never done research and have every reason to rush or fake a task. SuperJ Studio has to earn it from clients paying to watch real people think out loud, live. Different audiences, same design problem.
Active or recently shipped workstreams, not a completed, measured case study. Real usage data isn't in yet, so nothing below claims a validated before/after number.
Earning trust from people who've never done research before.
A ground-up trust and engagement pass on the consumer app, anchored on three questions: can incoming data be trusted, does verification feel safe enough to finish, and is the platform honest enough about money that people stay?
Respondents often rush, randomly select options, or provide dishonest inputs, which pollutes research data.
The callIn-survey checkpoints, and strikes that coach before they ban.
Behavioral checkpoints catch speeding and attention lapses in real time. A fair strike-and-penalty system corrects before it escalates to restrictions: most bad data is a coaching problem, not a ban-first offense.
Clients need verified demographics (location, age, identity), but users abandon onboarding when asked to upload personal documents.
The callPrivacy-preserving verification with zero-knowledge data retention.
Location matching, receipt and document validation, and clear recovery states for failed uploads. The bet: people hand over proof of who they are only if they trust it disappears afterward.
Ambiguity around payouts, penalties, and available tasks leads to high drop-off and poor platform trust.
The call“Quick Win” task cards and a wallet ledger in plain view.
Active earnings, redemption progress, and any penalty deductions visible on the home feed, replacing “trust us” with “see for yourself.”
Earning trust from clients watching people think out loud, live.
Video focus groups where moderators, respondents, and client observers share a room in real time but need almost none of the same controls. That mismatch shaped most of the decisions.
Running live qualitative research requires managing three completely different user needs (moderators leading, participants sharing, and clients observing) without operational friction.
The callSeparate end-to-end flows for moderators, respondents, and observers.
Each role sees only the controls and visibility relevant to them, instead of one generic meeting UI stretched three ways.
Live qualitative sessions fail when participants hit audio, video, or permission issues after they've already joined.
The callA pre-flight check for mic, camera, and network.
Failure discovery moves earlier, where it's cheap to fix, instead of live, where it derails the session.
Respondents need to stay completely anonymous to speak honestly, while moderators still need full control over the session.
The callAvatar anonymity, with host controls built into the room.
Hand-raising queues, muting, and screen-sharing. Anonymity and control stop being in tension once the moderator's authority comes from the room's structure, not from knowing who's speaking.
Where Things Stand
SuperJ 2.0 is rolling out across the consumer app; SuperJ Studio is running real client sessions. Neither has enough time in market for meaningful before/after numbers, so none are claimed here. This page folds into a full case study once there's real usage data.