shopify-admin-rfm-customer-segmentation

Name: shopify-admin-rfm-customer-segmentation
Author: 40RTY-ai

168stars17forks

How to install shopify-admin-rfm-customer-segmentation?

40rty-ai/shopify-admin-skills/shopify-admin-rfm-customer-segmentation

$npx -y skills add 40RTY-ai/shopify-admin-skills --skill shopify-admin-rfm-customer-segmentation

Installs into the current project.

Summary

Read-only: scores every customer on Recency, Frequency, and Monetary value to segment them into actionable groups (Champions, Loyal, At-Risk, Lost).

Files · 1

View on GitHub

SKILL.md

1	## Purpose
2	Performs full RFM (Recency, Frequency, Monetary) analysis across the entire customer base. Each customer is scored 1-5 on three dimensions — how recently they purchased, how often they purchase, and how much they spend — then classified into actionable segments: Champions, Loyal Customers, Potential Loyalists, At-Risk, Hibernating, and Lost. Read-only — no mutations.
3
4	## Prerequisites
5	- Authenticated Shopify CLI session: `shopify store auth --store <domain> --scopes read_orders,read_customers`
6	- API scopes: `read_orders`, `read_customers`
7
8	## Parameters
9
10	\| Parameter \| Type \| Required \| Default \| Description \|
11	\|-----------\|------\|----------\|---------\|-------------\|
12	\| store \| string \| yes \| — \| Store domain (e.g., mystore.myshopify.com) \|
13	\| days_back \| integer \| no \| 365 \| Lookback window for order history \|
14	\| segments \| integer \| no \| 5 \| Number of quintile buckets per dimension (3 or 5) \|
15	\| min_orders \| integer \| no \| 1 \| Minimum orders for a customer to be scored \|
16	\| tag_customers \| boolean \| no \| false \| If true, add RFM segment tag to customer (requires write_customers scope) \|
17	\| format \| string \| no \| human \| Output format: `human` or `json` \|
18
19	## Safety
20
21	> ℹ️ Read-only by default. If `tag_customers: true`, will add tags via customerUpdate mutation — use `dry_run: true` first.
22
23	## RFM Segment Definitions
24
25	\| Segment \| R Score \| F Score \| M Score \| Description \|
26	\|---------\|---------\|---------\|---------\|-------------\|
27	\| Champions \| 5 \| 5 \| 5 \| Best customers — recent, frequent, high spend \|
28	\| Loyal Customers \| 3-5 \| 4-5 \| 4-5 \| Consistent buyers with strong spend \|
29	\| Potential Loyalists \| 4-5 \| 2-3 \| 2-3 \| Recent buyers who could become loyal \|
30	\| New Customers \| 5 \| 1 \| 1-2 \| Just made first purchase \|
31	\| Promising \| 4 \| 1-2 \| 1-2 \| Recent but low frequency — nurture them \|
32	\| Need Attention \| 3 \| 3 \| 3 \| Average across all dimensions — slipping \|
33	\| About to Sleep \| 2-3 \| 2 \| 2 \| Below average recency and frequency \|
34	\| At Risk \| 1-2 \| 4-5 \| 4-5 \| Were great customers, haven't bought recently \|
35	\| Hibernating \| 1-2 \| 1-2 \| 1-3 \| Low on all dimensions — nearly lost \|
36	\| Lost \| 1 \| 1-2 \| 1-5 \| Haven't bought in a very long time \|
37
38	## Workflow Steps
39
40	1. OPERATION: `orders` — query
41	Inputs: `query: "created_at:>='<NOW - days_back days>'"`, `first: 250`, select `createdAt`, `totalPriceSet`, `customer { id, email, firstName, lastName, numberOfOrders }`, pagination cursor
42	Expected output: All orders in window with customer linkage; paginate until complete
43
44	2. Aggregate per customer:
45	- Recency = days since last order
46	- Frequency = total number of orders in window
47	- Monetary = total spend in window
48
49	3. Score each dimension 1-5 using quintile bucketing:
50	- Sort all customers by each metric
51	- Divide into N equal-sized groups (quintiles)
52	- Assign scores (5 = best for recency [most recent], frequency [most frequent], monetary [highest spend])
53
54	4. Map (R, F, M) score combination to named segment using the definitions above
55
56	5. OPERATION: `customers` — query (enrichment)
57	Inputs: Customer IDs from each segment for contact details
58	Expected output: Email, name, tags for top customers in each segment
59
60	## GraphQL Operations
61
62	```graphql
63	# orders:query — validated against api_version 2025-01
64	query OrdersForRFM($query: String!, $after: String) {
65	orders(first: 250, after: $after, query: $query) {
66	edges {
67	node {
68	id
69	createdAt
70	totalPriceSet { shopMoney { amount currencyCode } }
71	customer {
72	id
73	email
74	firstName
75	lastName
76	numberOfOrders
77	}
78	}
79	}
80	pageInfo { hasNextPage endCursor }
81	}
82	}
83	```
84
85	```graphql
86	# customers:query — validated against api_version 2025-01
87	query CustomerDetails($query: String, $after: String) {
88	customers(first: 250, after: $after, query: $query) {
89	edges {
90	node {
91	id
92	email
93	firstName
94	lastName
95	numberOfOrders
96	totalSpentV2 { amount currencyCode }
97	tags
98	createdAt
99	}
100	}
101	pageInfo { hasNextPage endCursor }
102	}
103	}
104	```
105
106	## Session Tracking
107
108	Claude MUST emit the following output at each stage. This is mandatory.
109
110	On start, emit:
111	```
112	╔══════════════════════════════════════════════╗
113	║ SKILL: RFM Customer Segmentation ║
114	║ Store: <store domain> ║
115	║ Started: <YYYY-MM-DD HH:MM UTC> ║
116	╚══════════════════════════════════════════════╝
117	```
118
119	After each step, emit:
120	```
121	[N/TOTAL] <QUERY\|MUTATION>

People who liked this also viewed

6 picks

Type

shopify-admin-rfm-customer-segmentation

168stars17forks

View on GitHub

How to install shopify-admin-rfm-customer-segmentation?

40rty-ai/shopify-admin-skills/shopify-admin-rfm-customer-segmentation

$npx -y skills add 40RTY-ai/shopify-admin-skills --skill shopify-admin-rfm-customer-segmentation

Installs into the current project.

Summary

Read-only: scores every customer on Recency, Frequency, and Monetary value to segment them into actionable groups (Champions, Loyal, At-Risk, Lost).

Files · 1

View on GitHub

SKILL.md

1	## Purpose
2	Performs full RFM (Recency, Frequency, Monetary) analysis across the entire customer base. Each customer is scored 1-5 on three dimensions — how recently they purchased, how often they purchase, and how much they spend — then classified into actionable segments: Champions, Loyal Customers, Potential Loyalists, At-Risk, Hibernating, and Lost. Read-only — no mutations.
3
4	## Prerequisites
5	- Authenticated Shopify CLI session: `shopify store auth --store <domain> --scopes read_orders,read_customers`
6	- API scopes: `read_orders`, `read_customers`
7
8	## Parameters
9
10	\| Parameter \| Type \| Required \| Default \| Description \|
11	\|-----------\|------\|----------\|---------\|-------------\|
12	\| store \| string \| yes \| — \| Store domain (e.g., mystore.myshopify.com) \|
13	\| days_back \| integer \| no \| 365 \| Lookback window for order history \|
14	\| segments \| integer \| no \| 5 \| Number of quintile buckets per dimension (3 or 5) \|
15	\| min_orders \| integer \| no \| 1 \| Minimum orders for a customer to be scored \|
16	\| tag_customers \| boolean \| no \| false \| If true, add RFM segment tag to customer (requires write_customers scope) \|
17	\| format \| string \| no \| human \| Output format: `human` or `json` \|
18
19	## Safety
20
21	> ℹ️ Read-only by default. If `tag_customers: true`, will add tags via customerUpdate mutation — use `dry_run: true` first.
22
23	## RFM Segment Definitions
24
25	\| Segment \| R Score \| F Score \| M Score \| Description \|
26	\|---------\|---------\|---------\|---------\|-------------\|
27	\| Champions \| 5 \| 5 \| 5 \| Best customers — recent, frequent, high spend \|
28	\| Loyal Customers \| 3-5 \| 4-5 \| 4-5 \| Consistent buyers with strong spend \|
29	\| Potential Loyalists \| 4-5 \| 2-3 \| 2-3 \| Recent buyers who could become loyal \|
30	\| New Customers \| 5 \| 1 \| 1-2 \| Just made first purchase \|
31	\| Promising \| 4 \| 1-2 \| 1-2 \| Recent but low frequency — nurture them \|
32	\| Need Attention \| 3 \| 3 \| 3 \| Average across all dimensions — slipping \|
33	\| About to Sleep \| 2-3 \| 2 \| 2 \| Below average recency and frequency \|
34	\| At Risk \| 1-2 \| 4-5 \| 4-5 \| Were great customers, haven't bought recently \|
35	\| Hibernating \| 1-2 \| 1-2 \| 1-3 \| Low on all dimensions — nearly lost \|
36	\| Lost \| 1 \| 1-2 \| 1-5 \| Haven't bought in a very long time \|
37
38	## Workflow Steps
39
40	1. OPERATION: `orders` — query
41	Inputs: `query: "created_at:>='<NOW - days_back days>'"`, `first: 250`, select `createdAt`, `totalPriceSet`, `customer { id, email, firstName, lastName, numberOfOrders }`, pagination cursor
42	Expected output: All orders in window with customer linkage; paginate until complete
43
44	2. Aggregate per customer:
45	- Recency = days since last order
46	- Frequency = total number of orders in window
47	- Monetary = total spend in window
48
49	3. Score each dimension 1-5 using quintile bucketing:
50	- Sort all customers by each metric
51	- Divide into N equal-sized groups (quintiles)
52	- Assign scores (5 = best for recency [most recent], frequency [most frequent], monetary [highest spend])
53
54	4. Map (R, F, M) score combination to named segment using the definitions above
55
56	5. OPERATION: `customers` — query (enrichment)
57	Inputs: Customer IDs from each segment for contact details
58	Expected output: Email, name, tags for top customers in each segment
59
60	## GraphQL Operations
61
62	```graphql
63	# orders:query — validated against api_version 2025-01
64	query OrdersForRFM($query: String!, $after: String) {
65	orders(first: 250, after: $after, query: $query) {
66	edges {
67	node {
68	id
69	createdAt
70	totalPriceSet { shopMoney { amount currencyCode } }
71	customer {
72	id
73	email
74	firstName
75	lastName
76	numberOfOrders
77	}
78	}
79	}
80	pageInfo { hasNextPage endCursor }
81	}
82	}
83	```
84
85	```graphql
86	# customers:query — validated against api_version 2025-01
87	query CustomerDetails($query: String, $after: String) {
88	customers(first: 250, after: $after, query: $query) {
89	edges {
90	node {
91	id
92	email
93	firstName
94	lastName
95	numberOfOrders
96	totalSpentV2 { amount currencyCode }
97	tags
98	createdAt
99	}
100	}
101	pageInfo { hasNextPage endCursor }
102	}
103	}
104	```
105
106	## Session Tracking
107
108	Claude MUST emit the following output at each stage. This is mandatory.
109
110	On start, emit:
111	```
112	╔══════════════════════════════════════════════╗
113	║ SKILL: RFM Customer Segmentation ║
114	║ Store: <store domain> ║
115	║ Started: <YYYY-MM-DD HH:MM UTC> ║
116	╚══════════════════════════════════════════════╝
117	```
118
119	After each step, emit:
120	```
121	[N/TOTAL] <QUERY\|MUTATION>

People who liked this also viewed

6 picks

Type