The scenario
The demo converter is fictional but built from the standard pattern: pressure-sensitive labels, 270 active accounts, four CSRs, an ERP that knows everything about orders and a CRM that knows almost nothing. Repeat business is the backbone of revenue; reorder timing is tracked by memory.
Everything in this walkthrough runs on synthetic sample data.
The before-state
The demo’s order history hides what real order histories hide:
- 18 accounts whose current order gap exceeds 1.5× their own typical cycle — invisible in any weekly report, obvious the moment cycles are modeled.
- One top-30 account at 2× its cycle. In the demo’s backstory, its longtime buyer left four months ago. Nobody connected the events.
- A cluster of small accounts that lapsed the same quarter the converter raised prices — a pattern no individual CSR could see from their slice.
- A “seasonal” account that isn’t actually seasonal; it just got slow service once, in August.
What the system does
Four stages on the live demo platform:
- Cycle modeling. 24 months of order history in; a per-account rhythm profile out. No generic 90-day rules — each account measured against itself.
- The overdue board. 18 flagged accounts ranked by revenue at risk, each with its history, usual order size, and gap visualized.
- The drafted touches. Service-shaped check-ins generated per account — referencing the actual product and usual timing — queued for CSR approval. The big account gets a recommended phone call with talking points instead.
- The weekly rhythm. The digest view a sales manager opens on Monday: new arrivals to the overdue list, what last week’s touches returned, the attrition trend line.
The math of the outcome
Illustrative example on the demo numbers: the 18 flagged accounts carry roughly $310,000 in trailing-12-month revenue. If early contact saves three mid-size accounts from quiet churn, the retained revenue dwarfs the cost of watching. The demo shows the watching; the saving is a phone call only your team can make.