Deliver exceptional customer experiences, optimise network investments and accelerate commercial agility with AI-powered telecom operations.
NPS prediction, journey analytics, proactive service recovery, and sentiment-driven experience optimization.
Telco customer experience is shaped by network quality, billing accuracy, service interactions, and digital channel usability. Most operators measure experience reactively through post-event surveys with low response rates:
Survey response rates of 5-10% skew toward extremes, missing the silent majority
A customer's experience spans network usage, billing, store visits, app interactions, and care calls — but never stitched into a single journey
Customers experience failures and must initiate contact; the operator never proactively reaches out
Categories assigned manually with inconsistent granularity, making systemic root cause analysis unreliable
Friction points in self-service apps and web portals are invisible without journey instrumentation
ML model predicting every subscriber's NPS score continuously — not just the 5-10% who respond — based on actual experience signals (network quality, billing accuracy, interaction sentiment, digital behavior).
Reconstruct and analyze multi-channel journeys: sign up → activate SIM → experience issues → call support → file complaint → receive credit. Identify journey patterns leading to churn or delight.
Detect experience failures in real time (network degradation, billing errors, failed digital transactions) and trigger automated recovery before the customer contacts support.
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.
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.
Source systems mapped into the NexusAI ontology, reasoned over, and turned into the KPIs your teams track.
NexusAI ontology · reasoning · agents
| 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 | 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 |
Stitch touchpoints from CRM, BSS, digital logs, and CDR into per-subscriber journey timelines
Compute per-subscriber experience signals: network quality score, billing accuracy, and interaction effort
Train regression model on labeled NPS responses; predict NPS for all subscribers; identify top drivers of detraction and promotion
Sequence mining to identify patterns preceding churn, friction driving repeat contacts, and delight driving upsell acceptance
Event-driven triggers: 3+ dropped calls → auto-send apology + data credit; billing error → auto-correct + notify before they call
NLP on call transcripts, chat logs, and social posts to extract themes, categorize by root cause, and detect emerging trends
CEM dashboard for VP Customer Experience; pNPS heatmaps by segment/region; proactive recovery automation; complaint trend reports for operations.
Net Promoter Score (survey-based)
% of subscribers with predicted NPS score
% of issues resolved in first interaction
Average effort required (1-7 scale)
% of failures resolved before contact
Customers contacting 2+ times in 14 days
Predicted NPS drops >10 points in 30 days
Volume on any theme increases >50% WoW
Subscriber experiences qualifying failure event
Self-service completion rate drops below 60%
Geographic area shows >30% detractors
Annually from reduced care costs, proactive recovery, experience-driven churn reduction, and NPS improvement — across a mid-size telco with 5M subscribers.
pNPS model requires minimum 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
Complaint NLP needs transcription of call center recordings; speech-to-text pipeline must be deployed if not already in place
Digital channel instrumentation (app/web event tracking) may require development effort if not already in place
Traffic forecasting, cell site optimization, and spectrum management for efficient network investment.
Network CAPEX is the largest investment category for telcos. Planning decisions made today determine network performance 2-3 years out. Current planning processes are inefficient:
Conservative traffic forecasts lead to excess capacity in low-growth areas, wasting CAPEX
High-growth areas hit congestion before capacity is deployed
RF configurations are set at deployment and rarely optimized for changing traffic patterns
Available spectrum is not dynamically allocated based on demand, leaving capacity on the table
4G-to-5G migration planning lacks granular demand and revenue data
Site build decisions driven by engineering metrics rather than business impact
AI-driven prediction of data traffic growth per cell site, sector, and technology layer for 6-24 month horizons based on subscriber growth, usage trends, and area development plans.
Identify cells approaching congestion thresholds 3-6 months before they hit — enabling proactive capacity augmentation.
AI analysis of cell performance with recommendations for antenna tilt, power adjustments, and frequency retuning to maximize spectral efficiency.
Analyze spectrum utilization and recommend refarming opportunities — shifting underutilized spectrum from legacy technologies to 4G/5G.
Rank network investment projects by expected ROI: projected traffic served, revenue impact, subscriber experience improvement, and competitive positioning.
Source systems mapped into the NexusAI ontology, reasoned over, and turned into the KPIs your teams track.
NexusAI ontology · reasoning · agents
| Ontology entity | Source system | Key fields |
|---|---|---|
| Cell Sites & Sectors | OSS Inventory | Cell ID, Site ID, Sector, Technology (4G/5G), Band, Antenna Config |
| Traffic Volumes | CDR / Network Data Lake | Cell ID, Hour, Data Volume (GB), Voice Erlangs, User Count, PRB Utilization |
| Spectrum Allocations | OSS Configuration | Cell ID, Band, Bandwidth (MHz), Technology, Utilization %, Refarming Eligibility |
| Subscriber Geography | CDR + BSS | MSISDN, Home Cell, Work Cell, ARPU, Plan Type, Device Capability |
| Physical Site Data | Field & Tower Data | Site ID, Location, Tower Height, Lease Expiry, Power Source, Co-location |
Aggregate CDR/xDR data into per-cell, per-hour traffic profiles (data volume, voice erlangs, signaling load, PRB utilization)
Combine historical trends with external drivers: population changes, construction permits, device upgrade cycles
Time-series + regression ensemble predicting 6/12/18/24-month traffic per cell; decompose into organic + event-driven + migration
Flag cells where forecasted traffic exceeds capacity threshold (>80% PRB) within 3/6/12 months
Simulate tilt changes, power adjustments, and frequency reallocation using propagation models + actual traffic data
Identify cells with underutilized legacy spectrum; model capacity gain from refarming to 4G/5G
Score each investment candidate by traffic served per $ invested, subscribers impacted, and revenue at risk
Capacity planning dashboard for CTO; congestion heatmaps for network planning; RF optimization recommendations; CAPEX priority list for investment committee.
Average PRB utilization during peak hour
Cells exceeding 80% PRB in busy hour
Predicted vs. actual monthly traffic (MAPE)
Mbps per MHz per cell (average)
GB served per $1K CAPEX invested
% of subscribers with 5G at home cell
Cell projected >85% PRB within 3 months
Actual traffic exceeds forecast by >30% for 2 months
Cell has >40% unused spectrum on decom-eligible tech
Site build costs trending >15% above budget
New development with >500 households has no serving cell
Annually from CAPEX deferral, spectral efficiency gains, congestion avoidance, and planning agility — across a mid-size telco with $120M annual network CAPEX.
Requires per-cell, per-hour traffic data from CDRs or OSS counters; 15-minute granularity is preferred for busy-hour analysis
Traffic forecasting models need 18-24 months of historical data per cell for reliable predictions
RF optimization requires an RF propagation model calibrated to the operator's network; integration with planning tools (Atoll, Planet) recommended
CAPEX ROI ranking methodology must be agreed with finance and network planning leadership before automating
5G migration planning requires device capability data from BSS (which subscribers have 5G-capable handsets)
Real-time network monitoring, predictive fault detection, and automated root cause analysis for telecom infrastructure.
Telecom networks generate millions of performance counters and alarms daily. Traditional threshold-based monitoring struggles to keep pace:
A single root cause triggers hundreds of correlated alarms across dependent elements, overwhelming NOC operators
Degradation detected only when static thresholds are crossed, not when early patterns emerge
Operators manually correlate alarms across network layers taking hours per major incident
Gradual performance decline (packet loss, latency, handover failures) goes unnoticed until customers complain
Faults not correlated with affected subscribers, making it impossible to prioritize by business impact
Consolidated view across RAN, transport, and core layers — aggregating performance counters, active alarms, and SLA metrics with drill-down from national to cell-site level.
ML models predict equipment failures 24-72 hours before they occur, based on degrading counter trends, environmental signals, and equipment age/type.
AI-powered alarm correlation that clusters related alarms, identifies root cause, and suppresses downstream noise — reducing effective alarm volume by 60-80%.
Real-time mapping of network faults to affected subscribers using CDR cell-site associations and BSS data — enabling impact-weighted fault prioritization.
Continuous SLA monitoring with prediction of breaches before they occur, enabling preemptive action.
Source systems mapped into the NexusAI ontology, reasoned over, and turned into the KPIs your teams track.
NexusAI ontology · reasoning · agents
| Ontology entity | Source system | Key fields |
|---|---|---|
| Network Elements | OSS Inventory | Element ID, Type (eNodeB/gNodeB/Router), Vendor, Model, Status, Location |
| Alarms & Faults | OSS Fault Management | Alarm ID, Element, Severity, Category, Timestamp, Acknowledgment |
| Performance Counters | OSS Performance Mgmt | Element, Counter Name, Value, Timestamp, Granularity (15 min) |
| Affected Subscribers | CDR + BSS | MSISDN, Cell ID, Session Quality, ARPU, Segment, Plan Type |
| Cell Sites & Topology | Field Data + OSS | Site ID, Location, Sector Count, Technology (4G/5G), Dependencies |
Stream performance counters from OSS (throughput, latency, packet loss, handover success, call drop rate) at 15-min granularity
Build per-element behavioral baselines accounting for time-of-day, day-of-week, and seasonal patterns
Score each counter reading against its baseline using isolation forest + LSTM autoencoder; flag deviations
Cluster incoming alarms by topology, timing, and causality (known fault propagation patterns)
Apply causal inference models to identify the most probable root cause from correlated alarm clusters
Count affected subscribers by correlating fault cell IDs with recent CDR data; weight by ARPU and segment
For degradation patterns matching pre-failure signatures, generate 24-72 hour advance warnings
NOC dashboard with correlated alarms and root cause; predictive maintenance queue for field ops; customer impact reports; SLA breach predictions.
% of time network elements are operational
Average hours from detection to resolution
% of raw alarms suppressed by correlation
Hours between prediction and actual failure
Faults affecting >1,000 subscribers
% of SLA metrics within committed thresholds
Cell/node down affecting >5,000 subscribers
Element shows pre-failure pattern >80% confidence
KPIs degrade >20% from baseline for 4+ intervals
SLA metric projected to breach within 4 hours
>50 alarms from interconnected elements in 5 min
Annually from reduced outage impact, NOC efficiency, SLA penalty avoidance, and preventive maintenance — across a mid-size telco with 8,000+ cell sites.
Requires near-real-time performance counter feeds from OSS at 15-minute granularity
Alarm correlation engine needs the complete network topology model (parent-child relationships, dependencies) from OSS inventory
Customer impact mapping requires CDR-to-cell-site association with <30 minute latency
Predictive fault models need minimum 12 months of historical alarm and counter data with labeled fault outcomes
Multi-vendor environments require normalization of vendor-specific counter names and alarm codes
Personalized plan recommendations, contextual offers, and price elasticity optimization for telecom commercial teams.
Telco pricing has shifted from simple voice/SMS plans to complex bundles. Traditional pricing approaches cannot keep pace:
A limited portfolio of 5-10 plans serves millions of subscribers with vastly different usage patterns
New plans take weeks to design, configure in BSS, and launch; competitors respond faster
Two subscribers paying the same price may have completely different usage profiles; neither is optimally served
Pricing decisions based on competitor benchmarking and gut feel, not measured elasticity per segment
Subscribers bombarded with generic promotional offers, leading to declining response rates and brand dilution
Device + plan + VAS bundles designed on commercial negotiations rather than subscriber preference data
AI model matching each subscriber's actual usage pattern (data heavy, voice heavy, roamer, streamer) to the optimal plan — surfacing savings or better-fit options in self-service channels.
Real-time trigger-based offers: subscriber runs out of data → data booster; travels internationally → roaming pack; contract expires → personalized renewal offer.
Measure price-response curves per segment: how does uptake change at different price points? Identify revenue-maximizing levels for each plan × segment combination.
Data-driven identification of optimal product combinations (plan + device + streaming + insurance) that maximize lifetime value and reduce churn.
Monitor competitor offerings, pricing changes, and promotional activity. Alert when competitive gaps emerge and model impact of pricing responses.
Source systems mapped into the NexusAI ontology, reasoned over, and turned into the KPIs your teams track.
NexusAI ontology · reasoning · agents
| Ontology entity | Source system | Key fields |
|---|---|---|
| Subscriber Profile | BSS + CRM | MSISDN, Segment, Tenure, Contract End, Device, ARPU, Payment History |
| Plan & Pricing | BSS Product Catalog | Plan ID, Name, Price, Data Cap, Voice Allowance, Validity, Add-ons |
| Usage Behavior | CDR / Network Data Lake | Daily Data (GB), Voice (min), SMS, Top Apps, Peak/Off-Peak, Roaming |
| Campaign History | CRM Campaign Mgmt | Campaign, Offer, Channel, Response, Conversion, Date, Segment |
| Market & Competitor | External Sources + CRM | Competitor Plans, Pricing, Promotions, Market Share Estimates |
Cluster subscribers into behavioral archetypes: data-heavy streamer, voice-centric, balanced user, international caller, weekend warrior
Calculate current plan utilization, monthly overage charges, potential savings on alternatives, and revenue impact to operator
Monitor real-time events: data exhaustion, international roaming, contract expiry, birthday/anniversary, complaint resolved
Select highest-propensity offer based on archetype, price sensitivity, channel preference, and recency of last offer
A/B test-driven measurement of price response per segment; build elasticity curves predicting volume impact of price changes
Association rule mining on product holding patterns + CLV modeling to identify bundles maximizing 24-month subscriber value
Monitor competitor changes; model subscriber migration risk; recommend defensive or offensive pricing actions
Personalized offer recommendations in app/web/SMS; pricing optimization dashboard for commercial team; bundle proposals for product management; competitive alerts.
Average Revenue Per User per month
% of contextual offers accepted
% of subscribers on optimal plan
Subscribers with 2+ products
Incremental revenue / Promotion cost
Days from plan design to market
Competitor launches >15% better value plan
Segment acceptance rate <5% for 4 weeks
Subscriber at 90% data with >3 days remaining
>10K subscribers entering 30-day expiry window
Elasticity changes >20% from previous quarter
Annually from ARPU growth, improved offer conversion, bundle penetration, and faster commercial response — across a mid-size telco with 5M subscribers.
Personalized recommendations require individual-level CDR usage data mapped to current plan parameters from BSS
Contextual offer engine needs near-real-time data triggers; integration with BSS policy/charging and CRM campaign platform
Price elasticity measurement requires controlled A/B testing infrastructure with statistical rigor
Competitive pricing monitoring requires automated web scraping or subscription to competitive intelligence service
Dynamic plan configuration requires BSS product catalog flexibility; legacy BSS may constrain speed of changes
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Recorded in the live NexusAI product.