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.

Three pre-built scenarios let you stress-test the forecast against likely changes:

Scenarios become editable once tenant data is connected — tune churn, win-back, acquisition, pricing assumptions independently.

Each candidate market is scored on four factors (0–25 each, 100 max):

• Route adjacency: Distance from your existing routes — closer means lower marginal cost to serve.
• Demographic similarity: Match to your top-performing ZIPs on home value + household income.
• Population density: Stops-per-mile potential — denser is more profitable per route mile.
• Competitor presence: Lower score where established competitors already saturate the area.

Demographic data ingested from the tenant\'s comparable-market dataset (research project — not yet built).

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 status = dormant (no completed job in last 60 days) AND not on marketing-exclusions list AND has paid at least one invoice.

Frequency cap: Each customer can enter the win-back sequence once every 180 days. After completing the sequence (success or fail), they are locked out for 6 months.

Suppression: Customers in active dispute, with open support tickets, or flagged on the marketing-exclusions table are skipped automatically.

Conversion tracking: When a dormant customer schedules a new cleaning during the 21-day window or up to 14 days after the final touch, the sequence is marked converted and the credited touch logged for attribution.

Trigger: A truck route runs through a customer's neighborhood AND that customer matches a targeting tier AND has not received an upsell touch in the last 60 days.

NOT targeted: Quarterly customers. They picked quarterly for the lowest cost — pushing them to monthly is the wrong target.

Channels: Email (primary) and in-crew-app prompt during the cleaning visit.

Frequency cap: One upsell touch per customer per 60 days, regardless of which tier they qualify for.

Pricing rule: The published catalog price applies only to NEW signups. Existing grandfathered customers stay at their original rate even if they accept an upgrade.

Decline retry: Three attempts (T+0, T+3 days, T+7 days) before flagging for manual review.

Dunning sequence: Email at 7 / 14 / 21 / 28 days overdue, text at 14 days, phone-call task at 21 days.

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

Suppression: Skip if the customer is in dispute or has an open support ticket.

Missed-cleaning / fuel fee charge: Auto-tracking and messaging run automatically, but the actual dollar charge requires owner approval per incident.

• Day 1: Thank-you / "Did everything go well?" email
• Day 7: Recurring-plan benefits email — soft pitch
• Day 21: Discount offer — "10% off your first 3 cleanings if you go recurring"
• Day 45: Social-proof email — neighborhood testimonials
• Day 80: Final offer — last chance for the discount before the customer exits the funnel

Schedule, copy, and discount amounts are draft — tenant configures their own.

Trigger: A new customer whose lead source is "word of mouth" and who names a referring customer.

Reward: Credit automatically applied to the referrer's next invoice (amount configurable).

Two-sided bonus: The referred customer gets a discount on their first cleaning.

Top referrers: Tracked for VIP recognition (potential annual gift or extra perk).

Reward amounts and bonus structure are tenant-configurable.