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Customer 360 & Churn Prediction

Unified subscriber profiles with churn prediction, retention campaign targeting, and ARPU growth intelligence.

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

Business Problem

Telcos maintain subscriber data across disconnected systems — BSS holds billing and subscriptions, CRM tracks interactions and complaints, CDR captures actual usage behavior, and network systems record quality of experience. This fragmentation creates:

  • No unified view — a care agent cannot see a subscriber's plan details, usage patterns, recent complaints, and network quality issues in one place
  • Late churn detection — subscribers port out or let contracts lapse without early warning; by the time ARPU declines, the customer has already decided to leave
  • Blanket retention — retention offers are applied uniformly rather than targeted to high-value at-risk subscribers, wasting budget on low-risk or low-value customers
  • ARPU blindness — opportunities to upsell (data add-ons, family plans, device bundles) are missed because usage patterns and propensity signals are not analyzed
  • Disconnected household — family members on separate accounts are not linked, preventing household-level offers and shared plan optimization

Capabilities

Unified Subscriber Profile

Single golden record per subscriber combining BSS (plan, billing, tenure), CRM (interactions, complaints, NPS), CDR (voice/data/SMS usage patterns, roaming), and network quality (signal strength, throughput at subscriber's locations).

Churn Prediction & Risk Scoring

ML model predicting voluntary churn probability for each subscriber within the next 30/60/90 days based on usage trajectory, complaint history, contract status, competitive offers, and network experience quality.

Retention Campaign Targeting

AI-driven segmentation of at-risk subscribers by churn driver (price sensitivity, network issues, competitor pull, life event) with matched retention offer recommendations (discount, data upgrade, device trade-in, network fix).

ARPU Growth & Upsell Engine

Identify subscribers with high propensity for plan upgrades, add-ons (data boosters, international packs, streaming bundles), or device financing based on usage patterns, peer cohort behavior, and life-stage signals.

Household & Account Linking

Discover and map household relationships from shared billing addresses, family plan structures, payment methods, and call/SMS patterns to enable household-level offers and family bundles.

Data Sources & Ontology Mapping

flowchart LR
    subgraph Data Plane
        BSS["BSS / Billing"]
        CRM_SYS["CRM"]
        CDR_SYS["CDR / Network Data"]
        INTERACT["Customer Interactions"]
    end

    subgraph Ontology Entities
        SUB["Subscriber / MSISDN"]
        PLAN["Plan & Subscription"]
        USAGE["Usage Patterns"]
        TICKETS["Service History"]
        SENTIMENT["Voice of Customer"]
    end

    subgraph AI Workflow
        RESOLVE["Entity Resolution"]
        PROFILE["Profile Builder"]
        CHURN["Churn Predictor"]
        UPSELL["Upsell Engine"]
    end

    BSS --> SUB
    BSS --> PLAN
    CRM_SYS --> SUB
    CRM_SYS --> TICKETS
    CDR_SYS --> USAGE
    INTERACT --> SENTIMENT

    SUB --> RESOLVE
    PLAN --> PROFILE
    USAGE --> PROFILE
    TICKETS --> PROFILE
    SENTIMENT --> PROFILE
    RESOLVE --> PROFILE

    PROFILE --> CHURN
    PROFILE --> UPSELL
Ontology Entity Source System Key Fields
Subscriber / MSISDN BSS Customer Master + CRM Account MSISDN, IMSI, Account ID, Name, Segment, Tenure, Contract End Date
Plan & Subscription BSS Product Catalog Plan Name, Monthly Fee, Data Allowance, Voice Mins, Add-ons, Status
Usage Patterns CDR / Network Data Lake Voice Minutes, Data Volume, SMS Count, Roaming Events, Peak Hours, Top Apps
Service History CRM Cases + BSS Tickets Ticket ID, Category, Resolution Time, Escalation, Repeat Contact Flag
Voice of Customer Customer Interactions (Calls/Chat/Social) Sentiment Score, Topics, NPS Response, Complaint Themes, Channel

AI Workflow

  1. Entity Resolution — Match subscriber identity across BSS (account/MSISDN), CRM (contact/case), and CDR (IMSI/MSISDN) into a single golden record
  2. Household Discovery — Graph analysis of shared billing addresses, common payment methods, and high-frequency call/SMS pairs to build household structures
  3. Profile Assembly — Aggregate plan details, 90-day usage trends, complaint history, NPS scores, network quality metrics at subscriber locations, and contract status
  4. Churn Feature Engineering — Compute features: usage velocity (declining data/voice), complaint frequency trend, days to contract end, competitor port-in activity in area, network quality score at home cell
  5. Churn Scoring — Gradient-boosted model trained on historical churn events; output 30/60/90-day churn probability per subscriber; segment by churn driver (price, network, competitor, life-event)
  6. Retention Matching — Map churn drivers to optimal retention offers: price-sensitive → discount/loyalty reward; network-frustrated → priority escalation + coverage fix commitment; competitor-pulled → matched/beat offer
  7. Upsell Scoring — Propensity model for plan upgrades and add-ons based on usage headroom, peer cohort adoption, and life-stage triggers (e.g., frequent international calls → roaming pack)
  8. Output — Subscriber 360 dashboard for care agents; churn watchlist pushed to retention team in CRM; upsell recommendations surfaced in digital and agent channels

Dashboard & Alerts

Key Metrics

KPI Description Target
Monthly Churn Rate % of subscribers churning per month < 1.2% (down from 1.8%)
Churn Prediction Accuracy % of actual churners flagged by model 30+ days prior > 75%
Retention Save Rate % of at-risk subscribers retained after intervention > 35%
ARPU Average Revenue Per User per month 5-8% year-over-year growth
Upsell Conversion Rate % of upsell recommendations accepted > 12%
Household Coverage % of subscribers linked to a household group > 70%

Alert Rules

Alert Trigger Severity Action
High-value churn risk Subscriber with ARPU > $60 has churn probability > 0.7 Critical Assign to senior retention agent; trigger personalized offer within 24 hours
Contract expiry window High-value subscriber within 30 days of contract end with no renewal activity High Push renewal offer via preferred channel; notify assigned agent
Usage collapse Subscriber data usage drops > 50% in 14-day window High Flag for churn risk review; check for network quality issues at subscriber locations
Port-out cluster 5+ port-out requests from same cell site area in 7 days Medium Investigate network quality; assess competitive activity in area
Upsell trigger Subscriber consistently exceeds data allowance by >30% for 3 consecutive months Info Surface data upgrade recommendation in next interaction

ROI Model

Metric Before After Impact
Monthly churn rate 1.8% 1.2% 33% reduction → 36K subscribers retained / year (on 5M base)
Revenue retained from churn reduction — — $18M / year (at $42 ARPU × 36K subscribers × 12 months)
Retention campaign efficiency 8% save rate (blanket offers) 35% save rate (targeted) 4.4x improvement
Upsell attach rate 4% 12% 3x improvement → $4.2M incremental ARPU
Care agent handle time (360 view) 8 min average 4.5 min average 44% reduction → $2.1M labor savings

Estimated Annual ROI

$20M - $35M annually from reduced churn, increased ARPU, campaign efficiency, and care productivity — across a mid-size telco with 5M subscribers and $42 average ARPU.

Implementation Notes

  • BSS subscriber extract must include MSISDN-level plan details, billing status, and contract dates; aggregated account-level data is insufficient
  • CDR processing requires a mediation layer or data lake that provides aggregated usage metrics (daily/weekly voice, data, SMS) per MSISDN
  • Churn model needs minimum 18 months of subscriber lifecycle data including labeled churn events (voluntary port-out, non-renewal, deactivation)
  • Network quality per subscriber requires correlating CDR cell IDs with OSS performance counters at those cells
  • Household discovery accuracy improves significantly when address normalization is applied to BSS service addresses

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