Referral and Repeat Business Template
Three documents and three sheets that rank reactivation outreach by missed cycles and value at risk from each customer's own service interval.
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Customer Register
Thistlewood Lawn & Landscape. Each customer's typical interval is the median days between their own past visits.
| Customer | Service | Typical interval | Days since last visit | Status |
|---|---|---|---|---|
| Alvarez, D. | Biweekly mowing | 14 | 41 | Overdue |
| Boryczka, M. | Monthly cleanup | 30 | 75 | Overdue |
| Castellano, R. | Quarterly fertilization | 90 | 95 | On schedule |
| Farrow, T. | Biweekly mowing | 14 | 63 | Overdue |
Castellano is five raw days past their last visit's 90-day mark and reads as barely overdue. Farrow is 22 raw days behind Castellano and is actually the more urgent call, which the raw day count alone does not show.
Reactivation List, ranked by value at risk
| Customer | Days overdue | Missed cycles | Avg ticket | Value at risk | Action |
|---|---|---|---|---|---|
| Farrow, T. | 49 | 3.50 | 65 | 227.50 | Phone call first |
| Boryczka, M. | 45 | 1.50 | 140 | 210.00 | Message plus call in 5 days |
| Alvarez, D. | 27 | 1.93 | 65 | 125.36 | Message plus call in 5 days |
| Dunmore, K. | 8 | 0.57 | 65 | 37.14 | One message |
Missed cycles, not days overdue, sets the order. Boryczka has fewer raw days overdue than Alvarez but a higher typical ticket and a shorter interval, so their value at risk still ranks above Alvarez's.
Referral Source Analysis
Recurring customers only; a single one-time install is tracked in total revenue but excluded here so it cannot swing a two-customer average on its own.
| Source | Recurring customers | Avg ticket | On schedule |
|---|---|---|---|
| Referred | 2 | 125.00 | 50% |
| Online search | 2 | 102.50 | 100% |
| Repeat, unprompted | 2 | 102.50 | 100% |
| Paid ad | 1 | 65.00 | 0% |
Referred customers average 125.00 against 102.50 for search and repeat-unprompted, about 22 percent higher, in the same direction as a published bank study's 16 percent lifetime-value finding, on a sample too small to treat as proof by itself.
Search "referral program template small business" and almost every result is the incentive structure: what to offer, when to ask, how to word the request. None of them address the customer who quietly stopped booking six weeks ago and will never see the referral ask at all. None of them question whether "90 days since last visit" means the same thing to a biweekly customer as it does to a quarterly one.
Thistlewood Lawn & Landscape runs both problems through the same customer register. A biweekly mowing customer 41 days since their last visit is 1.93 cycles overdue against their own 14-day interval, a clear reactivation priority. A quarterly fertilization customer 95 days out is only 0.06 cycles overdue against their own 90-day interval, barely past due at all. A flat day-count rule would flag the second customer as three months lapsed and miss how urgent the first one actually is.
The referral side runs on the same register rather than a separate spreadsheet. Thistlewood's own referred customers average $125.00 per job against $102.50 for customers acquired through search or unprompted repeat business, roughly 22 percent higher. That is pointed the same direction as a bank study finding referred customers carry 16 percent higher lifetime value over six years. The scheduling pack and the clinic scheduling pack read the same kind of history for capacity and no-shows instead of retention.
What's in the pack
Referral Programme
The incentive, who gets asked and when, and why a customer already flagged for reactivation gets the win-back message first, not a referral request.
Request Templates
The referral ask sent right after a completed job, and two reactivation messages worded from the customer's own typical interval rather than a raw day count.
Follow-up Cadence
Exactly one referral reminder if the first ask does not land, and a three-band phone-versus-message rule for reactivation by how many cycles a customer has missed.
Customer Register
Every customer with their service history, typical interval, acquisition source and current status, the sheet every other sheet in the pack reads from.
Referral Source Analysis
Average ticket and retention grouped by acquisition source, so a referred customer's actual value shows up in this business's own numbers.
Reactivation List
Every overdue customer ranked by value at risk, which is missed cycles times average ticket, not by raw days since their last visit.
How to use it
- 1
Open in River, or download it
Take the blank Word and CSV files with no account, or install the pack in River and hand it your customer job history.
- 2
Compute each customer's own typical interval
Use the median days between their own past visits on that service line, not a single number applied to every customer regardless of service type.
- 3
Flag and rank by missed cycles, not raw days
Flag anyone at 0.5 missed cycles or more, then rank the flagged list by value at risk so the highest-value overdue customer gets called first.
- 4
Run the referral ask on everyone else
Send the referral request after a satisfactory job for customers who are not on the reactivation list, and log every accepted referral against both customers.
Frequently asked questions
Is this template free?
Yes. Three documents and three sheets download as Word and CSV files with no signup and no credit card. Edit with AI is the optional path where the agent computes the intervals from your own customer history. The rest of the library is at the template index.
What format are the downloaded files?
Word documents (.docx) for the Referral Programme, Request Templates and Follow-up Cadence, and CSV (.csv) for the Customer Register, Referral Source Analysis and Reactivation List. They open natively in Word, Pages, Google Docs, Excel, Numbers and Sheets.
How many past visits does a customer need before this works?
At least two completed jobs on the same recurring service line, so a median interval exists to measure against. A customer with only one job, or a one-time service like a single installation, is excluded from reactivation and treated as a referral-request candidate instead.
Why not just flag everyone who has not booked in 90 days?
Because 90 days means something different to every service line. At Thistlewood, a biweekly customer 41 days out is already 1.93 cycles overdue and urgent, while a quarterly customer 95 days out is barely past due. A flat 90-day rule gets both of those backward.
Does the referral incentive apply to every referral?
Only once the referred customer's first job is completed and paid. A name passed along that never converts to a booking earns no credit, which keeps the incentive tied to revenue rather than to a lead count that never turns into a job.
Can this replace a CRM's built-in win-back automation?
Most CRM win-back triggers fire on a single flat day count for every contact. This pack computes the interval per customer per service line first, so if your CRM can be configured with a per-segment trigger, the numbers here are what to configure it with.
Call back the customer actually worth calling back first
Download the blank pack as Word and CSV files, or open it in River and have it rank your own customers by value at risk.
Edit with AI