Why Not Yet
Every 'not yet' sorted into a belief to answer or a barrier to remove.
The job
During a vaccination or screening campaign, know why each district and language community is not coming yet; separate beliefs (safety fears, ingredient worries, fertility rumours, 'not needed') from barriers (no slots, clinic hours, distance, a failing booking app, no one who speaks their language); route barriers to the people who can remove them and beliefs to clinician-approved answers in the community's language; prove bookings moved in that district.
The moment
Week two of the flu campaign and one industrial district's vial is stuck at 18%. The comms team has a myth-busting message ready in the community's own language. The vial's label is amber: 71% of not yet is a barrier (the clinic closes at four, my shift ends at six, the booking app wants a digital-ID login I don't have). The director drags an evening mobile clinic onto the vial and it rises to a projected 31% from three past fixes of that type. She approves it; a message goes to the 640 people who asked. The next week the real level is 36%, bookings double against a matched district that got nothing, and closes before my shift falls from 44% to 9%.
What it does
- Route a costed barrier fix to its owner
- Project a fix onto a vial
- Send a clinician-signed belief answer to the KB owner
- Tell the people who raised a barrier, one to one
- Pre-register a district verdict with a matched comparison
- Voice-agent sample of 'no slot' reporters
What you see
A map in which each district is a vaccine vial filling with uptake, labelled belief or barrier, with drag-a-fix projections and a drill into each language community.
What it moves
Uptake gap closed per district against its matched comparison, and share of 'not yet' that is a barrier still unremoved after two weeks.
Built for
- Operations
- Contact-centre operations
- Marketing, brand & comms
