repeat-purchase-rate
Purpose
Calculates the repeat purchase rate — the percentage of customers who return to place at least one more order within a defined window — and segments it by first-purchase product or collection. Identifies which products drive the highest repeat purchase behavior. Read-only — no mutations.
Prerequisites
- Authenticated Shopify CLI session:
shopify store auth --store <domain> --scopes read_customers,read_orders - API scopes:
read_customers,read_orders
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| store | string | yes | — | Store domain (e.g., mystore.myshopify.com) |
| days_back | integer | no | 90 | Acquisition window — customers first purchased in this period |
| repeat_window | integer | no | 90 | Days after first purchase to look for a repeat order |
| segment_by | string | no | none | Segment repeat rate by: product, none |
| format | string | no | human | Output format: human or json |
Safety
ℹ️ Read-only skill — no mutations are executed. Safe to run at any time.
Workflow Steps
-
OPERATION:
customers— query Inputs:query: "created_at:>='<NOW - days_back days>'",first: 250, selectid,numberOfOrders,createdAtExpected output: Customers acquired in window -
OPERATION:
orders— query Inputs:query: "created_at:>='<NOW - days_back + repeat_window days>'",first: 250, selectcustomer { id },createdAt,lineItems { product { id, title } }, pagination cursor Expected output: Orders to build per-customer purchase history and first-product mapping -
For each acquired customer: if they have ≥ 2 orders within
repeat_windowdays → repeat purchaser -
Calculate overall rate; if
segment_by: product, group by first-purchased product
GraphQL Operations
# customers:query — validated against api_version 2025-01
query AcquiredCustomers($query: String!, $after: String) {
customers(first: 250, after: $after, query: $query) {
edges {
node {
id
createdAt
numberOfOrders
defaultEmailAddress {
emailAddress
}
}
}
pageInfo {
hasNextPage
endCursor
}
}
}
# orders:query — validated against api_version 2025-01
query CustomerOrderHistory($query: String!, $after: String) {
orders(first: 250, after: $after, query: $query) {
edges {
node {
id
createdAt
customer {
id
}
lineItems(first: 5) {
edges {
node {
product {
id
title
}
}
}
}
}
}
pageInfo {
hasNextPage
endCursor
}
}
}
Session Tracking
Claude MUST emit the following output at each stage. This is mandatory.
On start, emit:
╔══════════════════════════════════════════════╗
║ SKILL: Repeat Purchase Rate ║
║ Store: <store domain> ║
║ Started: <YYYY-MM-DD HH:MM UTC> ║
╚══════════════════════════════════════════════╝
After each step, emit:
[N/TOTAL] <QUERY|MUTATION> <OperationName>
→ Params: <brief summary of key inputs>
→ Result: <count or outcome>
On completion, emit:
For format: human (default):
══════════════════════════════════════════════
REPEAT PURCHASE RATE
Acquisition window: <days_back> days
Repeat window: <repeat_window> days
Customers acquired: <n>
Repeat purchasers: <n>
Repeat rate: <pct>%
By First Product:
"<product>" Acquired: <n> Repeat: <pct>%
Output: repeat_purchase_<date>.csv
══════════════════════════════════════════════
For format: json, emit:
{
"skill": "repeat-purchase-rate",
"store": "<domain>",
"acquisition_days": 90,
"repeat_window_days": 90,
"customers_acquired": 0,
"repeat_purchasers": 0,
"repeat_rate_pct": 0,
"by_product": [],
"output_file": "repeat_purchase_<date>.csv"
}
Output Format
CSV file repeat_purchase_<YYYY-MM-DD>.csv with columns:
customer_id, first_order_date, first_product, total_orders, is_repeat, days_to_repeat, total_spent
Error Handling
| Error | Cause | Recovery |
|---|---|---|
THROTTLED |
API rate limit exceeded | Wait 2 seconds, retry up to 3 times |
| Guest checkout customers | No customer record to link orders | Exclude from analysis |
| Insufficient history | Store newer than window | Analyze available period |
Best Practices
- A repeat rate of 25–35% within 90 days is a healthy baseline for most non-subscription ecommerce stores.
- Products with high repeat rates are your "gateway" products — prioritize them in acquisition campaigns.
- Use
segment_by: productto identify which products create loyal customers vs. one-time buyers. - Pair with
customer-cohort-analysisfor a deeper view of long-term retention trends.
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