Customer Experience Management¶

NPS prediction, journey analytics, proactive service recovery, and sentiment-driven experience optimization.

Priority: P1 — High Value
Time to Value: 6-8 weeks
Category: Customer Experience

Business Problem¶

Telco customer experience is shaped by a combination of network quality, billing accuracy, service interactions, and digital channel usability. Most operators measure experience reactively through post-event surveys with low response rates:

NPS sampling bias — survey response rates of 5-10% skew toward extremes (very happy or very angry), missing the silent majority
No journey view — a customer's experience spans network usage, billing, store visits, app interactions, and care calls — but these are never stitched into a single journey
Reactive service recovery — customers experience failures (dropped calls, billing errors, slow data) and must initiate contact to get resolution; the operator never proactively reaches out
Complaint root cause opacity — complaint categories (billing, network, service) are assigned manually with inconsistent granularity, making systemic root cause analysis unreliable
Digital channel gaps — friction points in self-service apps and web portals (failed payments, confusing plan changes, broken troubleshooting flows) are invisible without journey instrumentation

Capabilities¶

Predicted NPS (pNPS)¶

ML model predicting every subscriber's NPS score continuously — not just the 5-10% who respond to surveys — based on their actual experience signals (network quality, billing accuracy, interaction sentiment, digital behavior).

Customer Journey Analytics¶

Reconstruct and analyze multi-channel journeys: customer signs up (store) → activates SIM (digital) → experiences slow data (network) → calls support (care) → files complaint (CRM) → receives credit (billing). Identify journey patterns that lead to churn or delight.

Proactive Service Recovery¶

Detect experience failures in real time (network degradation at subscriber location, billing errors, failed digital transactions) and trigger automated recovery actions before the customer contacts support.

Complaint Intelligence¶

AI-powered analysis of complaint themes from call center transcripts, chat logs, emails, and social media. Automated categorization, trend detection, and systemic root cause identification.

Digital Experience Optimization¶

Monitor self-service channel funnel analytics: where do customers drop off in plan changes, bill payments, troubleshooting flows? Identify friction points and recommend UX improvements.

Data Sources & Ontology Mapping¶

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

    subgraph Ontology Entities
        JOURNEY["Customer Journeys"]
        NPS_DATA["NPS & Survey Data"]
        SENTIMENT["Interaction Sentiment"]
        NETQUAL["Network Quality / Subscriber"]
        DIGITAL["Digital Channel Events"]
    end

    subgraph AI Workflow
        PNPS["pNPS Model"]
        JOURNEY_AI["Journey Analyzer"]
        RECOVERY["Recovery Engine"]
        COMPLAINT_AI["Complaint NLP"]
    end

    CRM_SYS --> JOURNEY
    CRM_SYS --> NPS_DATA
    CDR_SYS --> NETQUAL
    INTERACT --> SENTIMENT
    OSS --> NETQUAL
    CRM_SYS --> DIGITAL

    JOURNEY --> JOURNEY_AI
    NPS_DATA --> PNPS
    NETQUAL --> PNPS
    SENTIMENT --> PNPS
    SENTIMENT --> COMPLAINT_AI

    PNPS --> RECOVERY
    JOURNEY_AI --> RECOVERY

Ontology Entity	Source System	Key Fields
Customer Journeys	CRM + BSS + Digital Logs	Subscriber, Touchpoint, Channel, Timestamp, Action, Outcome
NPS & Survey Data	CRM / Survey Platform	Subscriber, NPS Score, Verbatim Comment, Survey Date, Channel
Interaction Sentiment	Customer Interactions (Calls/Chat/Social)	Transcript, Sentiment Score, Topics, Resolution, Effort Score
Network Quality / Subscriber	CDR + OSS	MSISDN, Cell ID, Throughput, Latency, Drop Events, Signal Strength
Digital Channel Events	App/Web Analytics	Session ID, Page/Screen, Action, Success/Failure, Duration, Drop-off

AI Workflow¶

Journey Reconstruction — Stitch touchpoints from CRM (interactions, cases), BSS (billing events, plan changes), digital logs (app/web sessions), and CDR (usage events) into per-subscriber journey timelines
Experience Feature Engineering — Compute per-subscriber experience signals: network quality score (weighted throughput, latency, drops at subscriber locations), billing accuracy (error frequency, credit frequency), interaction effort (repeat calls, escalations, handle time)
pNPS Modeling — Train regression model on labeled NPS survey responses; predict NPS for all subscribers using experience features; identify top drivers of detraction and promotion
Journey Pattern Mining — Sequence mining across customer journeys to identify: (a) journey patterns that precede churn, (b) friction patterns that drive repeat contacts, (c) delight patterns that drive upsell acceptance
Proactive Recovery Triggers — Define event-driven triggers: subscriber experiences 3+ dropped calls in 24 hours → auto-send apology + data credit; billing error detected → auto-correct + notify customer before they call
Complaint Theme Extraction — NLP on call transcripts, chat logs, and social posts to extract complaint themes, categorize by root cause (network, billing, product, service), and detect emerging trends
Output — CEM dashboard for VP Customer Experience; pNPS heatmaps by segment/region; proactive recovery automation; complaint trend reports for operations

Dashboard & Alerts¶

Key Metrics¶

KPI	Description	Target
NPS	Net Promoter Score (survey-based)	> 35 (industry top quartile)
pNPS Coverage	% of subscribers with predicted NPS score	> 95%
First Contact Resolution (FCR)	% of issues resolved in first interaction	> 78%
Customer Effort Score (CES)	Average effort required to resolve an issue (1-7 scale)	< 2.5
Proactive Recovery Rate	% of experience failures resolved before customer contact	> 30%
Repeat Contact Rate	% of customers contacting support 2+ times for same issue in 14 days	< 12%

Alert Rules¶

Alert	Trigger	Severity	Action
NPS crash — segment	Predicted NPS for any segment drops >10 points in 30 days	Critical	Investigate root cause; escalate to CXO; initiate segment-specific recovery
Complaint spike	Complaint volume on any theme increases >50% week-over-week	High	Identify systemic root cause; notify responsible team; issue customer communication
Service recovery trigger	Subscriber experiences qualifying failure event (drops, billing error, failed digital txn)	High	Execute automated recovery action (credit, apology, fix)
Digital funnel breakdown	Self-service flow completion rate drops below 60%	Medium	Notify digital product team; investigate UX issue
Detractor cluster	Geographic area shows >30% detractors (pNPS < -50)	Medium	Correlate with network quality; investigate local issues

ROI Model¶

Metric	Before	After	Impact
NPS	+18	+32	14-point improvement
Repeat contact rate	22%	12%	45% reduction → $3.6M care cost savings
Proactive recovery	0% (fully reactive)	30% of failures auto-recovered	30% fewer inbound complaints
Call center volume	1.2M calls/month	900K calls/month	25% reduction → $7.2M annual savings
Churn from experience issues	0.4% monthly (experience-driven churn)	0.25% monthly	37% reduction → $6M retained revenue

Estimated Annual ROI

$12M - $20M annually from reduced care costs, proactive recovery, experience-driven churn reduction, and NPS improvement — across a mid-size telco with 5M subscribers.

Implementation Notes¶

pNPS model requires a minimum of 10K labeled NPS survey responses linked to subscriber IDs with at least 6 months of experience data
Journey reconstruction depends on consistent subscriber identification across all touchpoints (MSISDN or CIF as common key)
Proactive recovery automation requires integration with BSS for credit issuance and CRM for communication triggers — define business rules with commercial team
Complaint NLP needs transcription of call center recordings; if not already transcribed, speech-to-text pipeline must be deployed first
Digital channel instrumentation (app/web event tracking) may require development effort if not already in place

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