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branches/kds_cdc_improvments/monitor-specs/backend/Kds.Ai.Data/MonitorSpecs/customer-profiles.md 4,048 bytes · Updated Oct 23, 2026 10:44 AM EDT

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id: 9c2e7b54-6f31-4a8d-b2c7-3d9f5e1a6b72 name: Create detailed customer profiles kind: CustomerInsights cron: 0 15 7 * * * analysis_window: 90d comparison_periods: [7d, 28d]

Create detailed customer profiles to support targeted marketing, personalization, and behavioral analysis.

Process should analyze order and item behavior to construct structured customer profiles capturing visit habits, spending patterns, menu preferences, dining context, and payment behavior.

Output must contain customer profile data only, without explanations or descriptions of methodology.

Known Tables & Columns

`curated.pos_orders`

order_id (varchar) - order identifier
customer_number (varchar) - customer linkage surface for identifiable customers
created_at (timestamp) - local order timestamp for visit-pattern analysis
business_date (date) - business trading date
total (decimal) - order value
table_number (varchar) - table identifier when dine-in context is present
payment_method (varchar) - payment method when populated
service_channel (varchar) - dine-in, pickup, delivery, or similar channel label when populated
fulfillment_channel (varchar) - downstream fulfillment grouping when populated

`curated.pos_order_items`

order_id (varchar) - join key to curated.pos_orders.order_id
item_id (integer) - menu item identifier
item_name (varchar) - sold item name
quantity (integer) - sold quantity
category_name (varchar) - sold item category when populated

`curated.pos_menu_items` (optional metadata enrichment)

item_id (integer) - menu item identifier
category_name (varchar) - configured category

`curated.customers` (optional enrichment)

external_id (varchar) - possible customer identifier to compare with curated.pos_orders.customer_number
name (varchar) - customer name when populated
email (varchar) - customer email when populated
phone (varchar) - customer phone when populated

Analysis Approach

Stage 1 - Customer Activity Aggregation

Extract identifiable order data from last 90 days using curated.pos_orders where customer_number is populated.

Calculate per-customer metrics including:

total visits
total spend
average spend per visit
typical visit times.

Stage 2 - Behavioral Attribute Extraction

Derive behavioral attributes including:

Visit frequency classification
daily
weekly
monthly
occasional.

Spending tier classification
low
medium
high
very high.

Dining pattern detection based on created_at, service_channel, and fulfillment_channel
lunch
dinner
late night.

Stage 3 - Preference Analysis

Analyze preference signals using current order and item surfaces:

Menu preferences derived from recurring purchases in curated.pos_order_items, optionally normalized with curated.pos_menu_items.category_name
Service-context tendencies inferred from service_channel, fulfillment_channel, and table_number
Preferred payment methods derived from curated.pos_orders.payment_method.

Use curated.customers only for optional enrichment after confirming curated.customers.external_id matches curated.pos_orders.customer_number for current tenant data.

Do not infer precise party size unless runtime schema discovery confirms trustworthy guest-count data.

Stage 4 - Profile Construction

Combine behavioral metrics and preference signals into structured customer profiles including:

visit frequency
spending tier
dominant dining pattern
menu preference tendencies
service-context tendency
preferred payment methods.

Output Constraints

Use historical data from last 90 days only.
Prefer aggregated summaries.
Avoid requesting more than 3 database queries unless necessary.
Return structured customer profiles only.
Do not include explanations, reasoning, or methodology.

Output Format

Return structured customer profiles.