Repository Docs & Knowledge Base

Consolidated Spensara documentation repository (722 Markdown & architecture documents in ../repository)

Built Word Documents (.docx): Spensara Overview (.docx) Capability Assessment (.docx) Brink POS Ingestion Analysis (.docx) The Borg Ingestion Architecture (.docx)

Branches (328)

Branch-specific documentation across extract-raw-xml, feature-fleet-import, kds_cdc_improvments, life-cycle, and tenat-admin.

branches/kds_cdc_improvments/monitor-specs/backend/Kds.Ai.Data/MonitorSpecs/customer-churn-risk.md 2,826 bytes · Updated Oct 23, 2026 10:44 AM EDT

Raw File

id: e2a9c4f7-6b3d-4d8a-9f21-5c7e1a2b8d66 name: Predict customer churn risk kind: CustomerInsights cron: 0 25 7 * * * analysis_window: 90d comparison_periods: [7d, 28d]

Predict customer churn risk to support retention strategies and proactive customer engagement.

Process should analyze recent customer activity and identify customers at risk of stopping visits based on simple, reliable behavioral signals.

Output must contain churn risk classifications 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
business_date (date) - business trading date
total (decimal) - order value
payment_method (varchar) - payment method when populated

`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
average spend per visit
total spend
days since last visit.

Stage 2 - Churn Signal Detection

Identify churn indicators such as:

declining visit frequency over time
increasing gaps between visits
decreasing spend compared to earlier activity within 90-day window.

Stage 3 - Risk Classification

Classify customers into churn risk levels using simple rules:

High risk
no recent visits within defined recent period
clear drop in visit frequency and spend

Medium risk
reduced visit frequency or increased gaps between visits

Low risk
consistent recent activity and stable spending.

Avoid complex predictive models.

Stage 4 - Basic Profiling

Optionally attach lightweight attributes such as:

dominant payment method 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.

Output Constraints

Use historical data from last 90 days only.
Prefer aggregated summaries.
Avoid requesting more than 2-3 database queries.
Avoid complex joins and predictive models.
Return churn risk classifications only.
Do not include explanations, reasoning, or methodology.

Output Format

Return structured churn risk records.