Methodology
How BoothCue scores a booth setup
BoothCue uses a weighted heuristic model. It does not predict exact revenue. Instead, it estimates how much avoidable friction is likely sitting between foot traffic and a clean conversion moment.
Main factors
- Traffic fit: fast traffic punishes unclear displays and slow checkout more heavily than slow browsing events.
- Space fit: tighter footprints need clearer front-facing hierarchy and fewer competing focal points.
- Checkout readiness: weak payment flow hurts impulse purchases and line confidence.
- Display clarity: if the booth needs a long explanation before the category or price makes sense, stop-rate drops.
- Event conditions: wind, heat, and low light create extra operational friction.
- Stock depth: too little stock can make the table feel sparse; too much can overload discovery unless the display is very clear.
Status bands
- 78–100: Ready to convert — the setup looks commercially coherent with only minor tune-ups needed.
- 60–77: Needs one focused tune-up — the booth can work, but one or two weak links are likely suppressing conversion.
- 0–59: High friction risk — stop-rate, clarity, or checkout flow are likely too weak for the conditions described.
Why the output is practical
Instead of only scoring, BoothCue turns the setup into action blocks: fix-first moves, layout priorities, sales cues, signage lines, and a prep list. That makes it useful during the real setup window.