AI Data Catalog / CustomerPotentialAnalyticsView
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CustomerPotentialAnalyticsView

AI-ready customer intelligence view that analyzes 90-day behavioral, financial, and purchase patterns to estimate customer potential and engagement level.
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Purpose

This view provides a 90-day analytical summary of customer behavior including purchasing patterns, order frequency, basket size, and financial range. It is used for customer segmentation, scoring, and AI-driven sales targeting.

Business Rules
  • Each record represents aggregated customer behavior over the last 90 days.
  • All financial values are calculated based on historical transactions.
  • SKU values represent number of items per invoice (basket size).
  • CountNotOrder represents visits without successful order creation.
  • AveFactorFerq represents average number of days between customer invoices.
  • LastVisitDate and LastFacktorDate are stored as string in YYYY/MM/DD format.
Relationships

This view represents the AI-driven customer intelligence layer of the system. It aggregates behavioral, financial, and engagement signals to generate customer scoring and segmentation. Most relationships are derived through CustomerID (CustID) and are based on aggregated or transactional source views.

Related View Join Keys Description
OrderFinancialSummaryView CustID, SCID Primary financial source used to derive revenue behavior, profitability, and customer monetary value.
OrderCollectionAnalyticsView CustID Provides payment behavior signals including delay patterns, outstanding balances, and cashflow reliability.
OrderReturnSummaryView CustID Captures product return behavior and identifies negative quality or satisfaction signals.
CustomerVisitLogsView CustID, SellerID Measures engagement intensity and interaction frequency between customers and sales representatives.
SellerVisitPathCustomersView CustID, SellerID Compares planned vs actual engagement coverage and supports opportunity gap analysis.
CustAssignToSeller CustID, SellerID Defines sales ownership structure used for attribution of customer potential and performance scoring.
DeliveryOperationAnalyticsView CustID (via Order/Invoice chain) Provides indirect logistics performance signals affecting customer satisfaction and experience quality.
CustomerPotentialAnalyticsView (Current) CustID, SCID Core AI scoring engine output that consolidates behavioral, financial, and engagement metrics into customer potential index.
Primary Customer Intelligence Strategy
  • CustID → Core entity for behavioral, financial, and engagement aggregation
  • SCID → Channel-based segmentation and performance comparison
  • CountFactor → Purchase frequency signal
  • AvgAmount → Monetary strength indicator
  • AveFactorFerq → Purchase consistency / loyalty metric
  • CountNotOrder → Drop-off and missed conversion signal
  • LastVisitDate / LastFacktorDate → Recency signals for AI scoring models
Key Definitions
  • SCID: Sales channel identifier.
  • CustID: Customer identifier.
  • GrpID: Customer group classification.
  • Lat/Lng: Customer geographical location.
  • MinAmount / MaxAmount: Minimum and maximum order value in period.
  • AvgAmount: Average order value.
  • CountFactor: Total number of invoices/orders.
  • AveFactorFerq: Average days between invoices (purchase frequency).
  • CountNotOrder: Number of visits without successful order.
  • MinSKU / MaxSKU / AvgSKU: Number of items per invoice (basket size).
  • LastVisitDate: Last customer visit date (YYYY/MM/DD).
  • LastFacktorDate: Last invoice date (YYYY/MM/DD).
Date Format
schedule
Important

All date fields are stored as STRING in format YYYY/MM/DD (Gregorian calendar). They must be converted before any date-based calculations or filtering.

Usage
  • Customer segmentation (A/B/C classes)
  • Sales targeting and prioritization
  • Churn prediction models
  • Customer lifetime value estimation
  • AI-based recommendation systems
  • Behavioral scoring engines
AI Insight
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Customer Intelligence Layer

This view is designed as a core input for AI-driven customer scoring models combining financial behavior, purchase frequency, and basket diversity to estimate customer potential and engagement level.