This page is for park operators. Looking for the guest app? Visit thoosie.app →
Crowd Intelligence + Crowd Action · For Park Operators

Know what your park will need an hour before it happens.Act on live data within minutes.

Crowd level within one bucket on at least 70% of days. Day-of wait times off by 8.9 minutes on average. Measured on 106,248 real ride-hours over the trailing 14 days across 60+ US theme parks, and re-scored every night against what actually happened. We own no hardware and install none: every reading comes from the ride-status feeds parks already publish. No guest app required.

70%Crowd within 1 level
8.9 minAvg wait forecast miss
60+US parks tracked
NightlyBacktest cadence
LIVE
Crowd Flow
 
Loading…
Loading live data…
Live wait times. Switch park above to explore.
See a hot zone? Score the risk → Risk Tower
Operations Control Tower
Loading…
Loading risk scores…
Risk Score: 0 = healthy, 100 = walk now. Ranked by operational urgency.
Zone flagged? Deploy a challenge → Crowd Movement
Live Anomaly Watch
Loading…
Checking for anomalies…
Detected from wait-time deviation vs per-ride baseline. Recommendation surfaces are configurable per park during pilot.
Pattern breaking? Deploy a challenge → Crowd Movement
Predicted Load · Next 90 Minutes
Hourly forecast
Loading forecast…
Hourly predicted avg wait across the park. Park-specific staffing playbooks layer on top during pilot.
Peak coming? Pre-route guests → Crowd Movement
Competitive Benchmarking
Regional compare
Loading competitive data…
Side-by-side wait time and crowd load vs. regional peers. Thoosie data only, no park system access required.
Benchmarked? Reset tomorrow's plan → Daily Brief
Operator Daily Brief
AI-generated
Loading daily brief…
Peak and shoulder windows surfaced from predicted hourly loads. Recommended staffing and crowd-shaping actions for the day ahead.
Playbook set? Watch it live → Crowd Flow
Crowd Movement
Design a crowd-steering challenge on real park data, then watch it play out
⚙ Advanced settings Loading…
🗺️ Challenge Simulator
The park's operating day, replayed. Click a hot ride (source), then a cool ride (target), pick a reward, and hit Simulate to watch the challenge re-route guests.
Loading movement map…
Click a hot ride on the map to start designing a challenge.
📊 Baseline vs Challenge
Simulate a challenge to see the comparison.
🎯 Movement Opportunities
Derived from today's wait-time data. Tap one to pre-fill the map and challenge designer, then Simulate is one click away.
Loading opportunities…
↔️ Observed Guest Flow
Rides gaining or shedding queue pressure vs their expected baseline. Big pressure gains at one ride paired with big drops at a nearby ride is evidence of natural guest flow, the same routes Thoosie can amplify with challenges.
Loading flow analysis…
To run this challenge live at your park, book a pilot walkthrough →
The problem

Three numbers parks already know, and have no tool to fix.

Weather, food waste, and early walkouts drain revenue from every operating day. Thoosie turns each one into a problem you can act on before the day is over.

🌧
$120M
Lost to weather events
Disney's own disclosure for the 2024 hurricane season. That is one operator's number for one season, not an industry figure, and your exposure will be different. Crowd-steering challenges let you capture F&B and in-park spend before guests cut the day short.
🍔
50K lbs
Food wasted per month
Disney donates ~50K lbs of surplus food monthly. At scale, that’s tens of thousands in margin walking out the door. Surplus-to-deal routing converts waste into incremental revenue.
🚪
Early exits
Guests who leave before close
Crowds, long waits, and heat all push families toward the gate early, and every guest who leaves early stops spending. We have no published figure for how many, so we do not quote one. A targeted challenge at hour 6 is built to keep them for hour 7.
How it works

Detect. Decide. Deploy. Done.

The intelligence layer spots the problem. The crowd-steering engine solves it. Here’s a Saturday afternoon at your park, start to finish.

1:38pm
Risk Tower
Zone 4 hits 82/100. Waits climbing, one anomaly open, early-departure risk rising.
Scores every zone 0 to 100 from public wait data. Live waits refresh every minute; the zone risk scores are recomputed on their own 15-minute cycle. No sensors. No installation. We own no hardware.
1:39pm
Daily Brief
This window was predicted this morning. Country Fair: 142 open capacity, shoulder period starting now.
Each morning the Brief surfaces today’s peak windows, shoulder gaps, and the moves worth pre-staging, before the park opens.
1:40pm
Crowd Movement
Operator deploys a zone challenge. One tap. Geofenced push goes live for guests on the Thoosie or park app.
Pick a deal type, zone bonus, closing push, surplus F&B discount. Set the dials, hit deploy. 60 seconds from flag to live challenge.
1:42pm
Daily Brief
23 guests scan in at Country Fair. Zone 4 drops to 61. F&B surplus converts.
Redemptions land in tonight’s Brief. Tomorrow’s forecast starts sharper. The same tool that predicted the problem records that it’s solved.
Problem solved in 4 minutes
Why it works

Validated by published research and proven categories.

We’re not inventing new guest behavior. Geofenced offers, surplus-to-deal routing, and gamified loyalty are all proven at scale in other categories. What Thoosie does is bring them together for the theme park gate.

Geofencing industry
18%
Redemption rate on geofenced mobile offers, 9× lift over traditional coupons. Source: Mobile Marketing Association.
Too Good To Go
90M
Users on the same surplus-as-deal model. 400M meals saved. Proven at global scale, applied to in-park F&B.
Gamified loyalty
Proven
Starbucks Rewards is the best-known consumer example of gamified progress driving repeat visits. That is the behavioral mechanic Thoosie applies to zone routing. We have no retention figure we can source, so we do not quote one.
Mobile ordering
Untested here
Pre-committed mobile orders tend to run larger than walk-up counter orders in food service generally. We have not measured that inside a theme park, so we treat it as something a pilot tests rather than a number we quote.
🎯

Move guests, don't just watch them.

Nine behavioral-incentive types (zone auction, closing-hour push, chain, flash, credit proximity) deployed against the loss-aversion, endowed-progress, and variable-reward literature. Operator-tuned, cohort-routed.

📈

Forecasts scored every night against what actually happened.

75.4% of crowd-level calls land within one bucket, and day-of wait forecasts miss by 8.9 minutes on average, measured across 106,248 ride-hours in the trailing 14 days. Multi-day horizon accuracy is not independently scored yet, so we quote day-of numbers only. We will not put a 14-day figure on this page until we have measured one.

Every challenge measured. Every result fed back.

Crowd level within one bucket on at least 70% of days · day-of wait error at or under 12 minutes · live waits refreshed every minute. Measured on your park, with your data. Miss those targets over the 60-day pilot and you don't pay for it.

🔗

Your app or ours.

Thoosie ships as a drop-in SDK. Parks can embed live wait data, crowd-shaping challenges, and guest-flow intelligence directly into their own branded app. With enough combined users across Thoosie and your platform, the crowd-shaping engine scales to move real volume, nudging guests toward low-density zones, thinning queues, and reducing close-time stacking. You control the levers; we run the targeting.

The honest split

What Thoosie ships today. What your pilot unlocks.

No dollar projections. No modeled ROI. Every capability below is either live in the product or in active development for pilot delivery.

Live in production

Shipping today

70%
Crowd within 1 level
8.9 min
Avg wait forecast miss
60+
US parks tracked
  • Nightly backtesting of every prediction against real park data
  • This dashboard: crowd flow, crowd movement, risk tower, anomalies, forecast, competitive benchmark and daily brief, all reading live APIs
  • Per-park operator login with email-code auth: live park rides, crowd-challenge stats, A/B test setup and results, and your own dashboard config
  • 13 operator API endpoints behind that login, so a park can pull its own data rather than read a screen
  • Read-only demo access to any of the 60+ tracked parks
  • Every-minute refresh for live wait times across the fleet
Rolling out with your pilot

Your pilot unlocks

  • Push-based crowd steering: APNs push + in-app so guests see nudges whether the app is open or closed
  • Server-verified arrival: HMAC-signed challenges + haversine geofence proof against your park’s ride coordinates
  • Deal redemption at your POS: QR check-out flow with cashier scanner and per-deal attribution
  • Drop-in SDK: embed Thoosie’s crowd-steering primitives into your existing guest app in development
  • iOS + Android: iOS shipped, Android in development
  • A/B policy testing: operator-controlled challenge policy variants with real-time reward attribution

Thoosie is pre-pilot. We’re looking for 2 to 3 launch partners in 2026 to deploy the “pilot unlocks” column in a real park. No dollar promises, just the receipts.

The ask

Give us 20 funnel cakes.

One Saturday. We prove the crowd movement.

You watch the loop run once, see, know, act, measure, on your own park's data.

Pull a real day from your last 30. Walk through 4 moments where Thoosie would have changed the call. Talk pilot scope. 30 minutes, direct from Ben.

Thoosie runs entirely on the wait-time signals parks already publish. Any data your park chooses to share on top of that is optional, sent over an encrypted connection, and always visible to you.

One business day to reply. Direct from Ben, parks@thoosie.app
Built by Ben Brooks · Kings Dominion regular since 1993.
ThoosieThoosie