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Customer 360 & Relationship Intelligence

Unified customer profiles with household mapping, CLV prediction, churn detection, and next-best-action for banking.

Priority: P0 — Immediate ROI
Time to Value: 4-6 weeks
Category: Sales & Relationship Management

Business Problem

Banks maintain customer data across multiple disconnected systems — Core Banking holds accounts and transactions, Salesforce FSC manages advisor interactions and pipeline, the Loan system tracks borrowing relationships, and AML flags risk events. This fragmentation creates:

  • No single view — a relationship manager cannot see a customer's full portfolio (deposits + loans + cards + investments + advisory history) in one place
  • Household blindness — linked family members, joint accounts, and corporate-individual relationships are not mapped, leading to fragmented servicing of high-value households
  • Silent attrition — high-net-worth customers reduce balances and shift products to competitors over months without triggering any alert
  • Missed cross-sell — a customer with a large savings balance but no investment relationship is never flagged despite high conversion propensity
  • Inconsistent servicing — different channels (branch, digital, call center) operate on different data, creating contradictory customer experiences

Capabilities

Unified Customer Profile

Single golden record combining Core Banking (accounts, balances, transactions), Salesforce FSC (interactions, pipeline, advisor notes), Loan Origination (borrowing history, repayment behavior), and AML (risk flags, KYC status).

Household & Relationship Mapping

Automatically discover and map household relationships (spouse, children, parents), corporate affiliations, and beneficial ownership structures. Calculate household-level AUM and total relationship value.

Customer Lifetime Value (CLV) Prediction

ML model predicting 3-year forward revenue per customer based on product holding trajectory, balance trends, transaction patterns, and life-stage indicators.

Churn Prediction & Early Warning

Detect early signals of customer attrition: declining balances, reduced transaction frequency, competitive product inquiries captured in Salesforce, and negative sentiment from service interactions.

Next-Best-Action Engine

Context-aware recommendations for each customer interaction: product offers, service recovery actions, relationship deepening suggestions, and compliance requirements (KYC renewal, document collection).

Data Sources & Ontology Mapping

flowchart LR
    subgraph Data Plane
        CBS["Core Banking System"]
        SFSC["Salesforce FSC"]
        LOS_SYS["Loan Origination"]
        AML_SYS["AML / Transaction Monitoring"]
    end

    subgraph Ontology Entities
        CUST["Customer / CIF"]
        ACCT["Accounts & Balances"]
        TXN["Transaction History"]
        INTERACT["Interactions & Advisory"]
        RISK["Risk & Compliance Flags"]
    end

    subgraph AI Workflow
        RESOLVE["Entity Resolution"]
        PROFILE["Profile Builder"]
        CLV["CLV & Churn Models"]
        NBA["Next-Best-Action"]
    end

    CBS --> CUST
    CBS --> ACCT
    CBS --> TXN
    SFSC --> CUST
    SFSC --> INTERACT
    LOS_SYS --> ACCT
    AML_SYS --> RISK

    CUST --> RESOLVE
    ACCT --> PROFILE
    TXN --> PROFILE
    INTERACT --> PROFILE
    RISK --> PROFILE
    RESOLVE --> PROFILE

    PROFILE --> CLV
    PROFILE --> NBA
Ontology Entity Source System Key Fields
Customer / CIF CBS Customer Master + Salesforce Account CIF Number, Name, DOB, Segment, KYC Status, Onboard Date
Accounts & Balances CBS + Loan Origination Account Type, Balance, Currency, Status, Interest Rate, Maturity
Transaction History CBS Ledger Date, Amount, Type (credit/debit), Channel, Counterparty, Category
Interactions & Advisory Salesforce FSC Activities Interaction Date, Channel, Type, Advisor, Notes, Outcome, Pipeline
Risk & Compliance Flags AML System + CBS KYC Status, Risk Rating, AML Alerts, PEP Flag, Sanction Match

AI Workflow

  1. Entity Resolution — Match customer identity across CBS CIF, Salesforce Account, LOS Borrower, and AML subject into a single golden record using probabilistic matching (name, DOB, ID documents, phone, address)
  2. Household Discovery — Graph-based analysis of joint accounts, shared addresses, common beneficiaries, and Salesforce relationship groups to build household structures
  3. Profile Assembly — Aggregate all product holdings, balances, transaction patterns, interaction history, loan performance, and risk flags into a unified profile
  4. CLV Modeling — Gradient-boosted model trained on historical customer revenue trajectories; features include product depth, balance trend, transaction intensity, tenure, and segment
  5. Churn Scoring — Survival analysis model identifying customers with elevated exit probability in the next 90 days; key features are balance velocity, transaction frequency change, competitive signals, and complaint history
  6. Next-Best-Action — Contextual recommendation engine combining CLV, product holding gaps, life-stage triggers, and compliance requirements to suggest the optimal action per customer per interaction
  7. Output — Customer 360 dashboard for relationship managers; churn alerts pushed to Salesforce; NBA recommendations surfaced in branch/call center systems

Dashboard & Alerts

Key Metrics

KPI Description Target
Total Relationship Value (TRV) Sum of all product balances + off-balance-sheet AUM per customer Monitor; grow top 20%
Product Penetration Average number of active product categories per customer > 3.5 products
Churn Rate (HNW) Annual attrition rate for high-net-worth segment < 5%
CLV Accuracy Predicted vs. actual 12-month revenue correlation R² > 0.75
NBA Conversion Rate % of next-best-action recommendations that result in product sale > 18%
Household Coverage % of customers linked to a household/relationship group > 80%

Alert Rules

Alert Trigger Severity Action
HNW churn risk Customer with TRV > $500K has churn probability > 0.65 Critical Assign senior RM; trigger retention campaign within 48 hours
Balance erosion Customer balance declines >25% in 60-day window High Notify RM; schedule outreach call
KYC expiry Customer KYC documents expire within 30 days High Push re-KYC task to Salesforce; block high-risk transactions
Cross-sell opportunity High-CLV customer holding deposits but no investment products Medium Surface recommendation in next interaction; push to Salesforce
Household event New account detected in existing household (child turning 18, new spouse) Info Trigger household review; suggest family banking package

ROI Model

Metric Before After Impact
HNW customer churn 8% / year 5% / year 37% reduction → $12M AUM retained (per $400M HNW book)
Product penetration 2.8 products/customer 3.6 products/customer 29% increase → $3.2M additional fee income
NBA conversion rate 8% 18% 125% improvement in campaign effectiveness
RM productivity 45 min per customer prep 10 min per customer prep 78% time savings → more client-facing time
Household identification 35% mapped 82% mapped Unlocked household-level pricing and servicing

Estimated Annual ROI

$8M - $18M annually from retained AUM, increased product penetration, and RM productivity — across a mid-size bank with $5B in customer assets under management.

Implementation Notes

  • Customer entity resolution across CBS and Salesforce FSC is the critical foundation; expect 85-92% auto-match rates with manual review for remaining ambiguous records
  • Household discovery requires joint account data and shared address/phone analysis; accuracy improves with Salesforce relationship group data
  • CLV model needs minimum 24 months of product and balance history for training
  • Next-best-action engine should integrate with the bank's existing campaign management platform to avoid duplicate outreach
  • AML risk flags must be surfaced in the profile but access-controlled to authorized users only

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