# Quiet customers and win-backs

Most customers do not complain before they leave. They just stop. Finding them
is a data job, not a sentiment job.

---

## Set the rhythm per customer, not per business

A customer is quiet when their gap has stretched well past *their own* normal
interval. Comparing everyone to a single company-wide average produces a list
full of annual customers who are perfectly happy.

For each customer with three or more orders or jobs:

```
their normal interval = median gap between their own orders
quiet                 = current gap > 2× their normal interval
gone                  = current gap > 3× their normal interval
```

With fewer than three orders there is no rhythm to measure. Compare those to
the business's median instead, and label them as an estimate in the report.

| Customer | Normal interval | Current gap | Read |
|---|---|---|---|
| Dana R. | 3 months | 7 months | Quiet — worth a call |
| Kirby St. LLC | 12 months | 13 months | Normal — leave alone |
| M. Alvarez | 6 weeks | 5 months | Gone — win-back |

---

## Rank by what the relationship was worth

Sort the quiet list by past value, not by length of silence. The owner has
time for fifteen conversations, not two hundred, and the fifteen should be the
ones that mattered.

Value in order of usefulness: lifetime spend, then order frequency, then most
recent order size. Use whatever the connected data actually supports and say
which one you used.

**The strongest signal is a pairing:** a customer who left a negative review
and then stopped ordering. Flag those at the top regardless of value. They
told the business exactly what was wrong and then left anyway, and they are
the most winnable because the fix is already known.

---

## Shopify order patterns as sentiment

Behavior leads reviews by weeks. Reading it early is the only chance to fix
something before it becomes public.

Worth surfacing:

- **Fulfillment time drifting up** across a period. Compare the window to the
  prior one and report both numbers.
- **Repeat refunds against one product** or one date range. Three refunds on
  the same SKU is a product problem, not three unhappy people.
- **Partial or split shipments** rising. These generate "where is the rest of
  my order" contacts a week or two later.
- **First-order customers who never came back,** as a share of the window.

Report what the data shows and nothing beyond it. If refund reasons are not
recorded in the data, say they are unavailable rather than inferring why
people asked for money back.

---

## Designing the win-back

One message per customer, referencing something real: what they bought, what
was fixed, the season their equipment gets serviced. Generic "we miss you"
mail is exactly what they already delete.

Confirm what the owner can actually honor before drafting. Never invent a
discount, credit, free service, or priority slot. Ask:

```
Before I draft these — what's actually on the table for a win-back?

  Something like a discount or a free check-up
  A straight personal note with no offer
  A call from you rather than an email

For the four highest-value names I'd suggest the call. An email to someone
who spent USD 9,000 with you reads as a mailing list.
```

**Offer strength by segment:**

| Segment | What usually works |
|---|---|
| High value, left a bad review | A personal call. No offer up front — the apology is the offer |
| High value, no complaint | A note from the owner, naming the last job |
| Mid value, seasonal business | A timely reminder, no discount needed |
| Low value, one-time buyer | A single low-cost offer, then stop |

**Stop after one.** A second and third win-back attempt to someone who has not
replied damages the brand and, for email, damages deliverability for everyone
else. One good message, then leave them alone.

---

## Approval

Every win-back is drafted for approval and sent one at a time. The owner
approves the message and the offer inside it. An offer that goes out before
the owner has agreed to honor it turns a churn problem into a refund problem.