1. Total Operating Expenses (year-to-date): Payroll, fuel, vehicle, insurance, software, rent. Pulled from accounting (QuickBooks, Xero, etc.) or uploaded manually.
2. Total Stops Completed (year-to-date): Number of customer service stops actually performed. Pulled from field-service software (GorillaDesk, Jobber, etc.) or uploaded.
3. Revenue per Stop (by day & route): Total revenue collected at each stop. Pulled from payment processor (Square, Stripe, etc.) or uploaded.
4. Route Schedule (day-by-day): Which routes run which days, how many trucks, how many runs per cycle. Pulled from field-service schedule.
5. Cycle-Week Rotation: If you use a 4-week rotating route schedule, the system needs to know which customers fall in which week.

For the active tenant, these data sources are connected:

• Payment processor: Status pending — will show: Connected / Disconnected once wired
• Accounting: Status pending
• Field-service software: Status pending

If a tenant doesn't have API-connected sources, upload the four required inputs as CSVs. Each tile accepts one file and shows status. Click Download Template to grab a starter file with the right column headers.

What this will show when the research dataset is loaded:

• Comparable-market pricing range per plan/can-count combination
• Your position: under market / at market / over market — by percentage
• Estimated annual revenue opportunity if catalog rates align with market for new signups

Research methodology:

• Match comparable markets by: city/town size, residential density, median household income, population
• Pull actual competitor pricing per plan/can-count from those markets (not approximated, not guessed)
• Filter to markets with similar income profile (avoid comparing high-income suburb to low-income market)

Status: Research project on the build queue. UI shell ready to receive the dataset.

Locked rules (cannot be overridden by recommendations):

• Catalog price applies to NEW signups only. Existing grandfathered customers stay at their original rate.
• Bulk price increases on existing customers require explicit timing approval — never automated.

Specific price-action recommendations populate here once both the internal data is connected and the comparable-market research is loaded.

Trigger: Customer cancelled and has been gone for 6 months.

Touch: One check-in email. Three response buttons: Sign Back Up / Not Interested / Opt Out.

Dismissal: Manager reviews responses and dismisses each one manually. Not auto-processed.

Not automated: Howard fires this manually when ready. No drip sequence, no multi-touch, no discount escalation.

Trigger: Truck route enters a customer’s neighborhood. Email/SMS fires 7 days before the truck day.

Message: “We’re in your neighborhood [date], want an add-on?”

Throttle: 90-day suppression — each customer receives at most one touch per 90 days.

Backend: occ-command-center → nt_lapsed_send

Charge timing: Authorize at crew check-in, capture in the evening batch.

Missed-cleaning / fuel fee: Tracking and messaging run automatically. Actual dollar charge requires Howard’s approval per incident — never auto-charged.

• Day 3 — Email touch 1
• Day 21 — Email touch 2
• Day 80 — Email touch 3 (final)

Worker: occ-ot-followup · Cron: daily 9 AM ET

Trigger: Customer completes their 3rd service.

Action: Worker sends their unique referral code and explains the referral program.

Worker: occ-referral-pitch · Cron: daily 10 AM ET