Trends in AI for telecom market

1. AI in Telecom
How artificial intelligence is reshaping
the vision of telco industry
Gabriele Randelli
May 18th, 2018

2. Once upon a time in a telco operator…
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- Traffic Volume
- Infrastructure as asset
- Customer-agnostic
- Data-agnostic

3. … there was an intruder
3
- Service oriented
- No infrastructure
- Customer-centric
- Data eager

4. Over-the-top (OTT) Players
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OTTs
scrape out
44% of revenues
 No network infrastructure costs
 Customer-centric
 Leverage on data insights
 Low return from investment
 Customer-agnostic
 Content-agnostic
OTTs
market value
seven times
bigger than CSPs

5. Network costs vs revenues
5
US$354 billion into maintaining, building
and upgrading networks in 2014

6. Towards a subscriber-centric paradigm
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AI
AI-assisted customer service
Next Best Action
Predictive Maintenance
Churn/Traffic PredictionNetwork
Anomaly Detection
Content Usage Trends
Fraud Detection
Customer
Profiling
Self-healing
Networks
Intelligent Edge
IoT & Edge
Converge data sources
(voice, data, fixed,
mobile, sensors,
billing, CRM,
network)
Automate
network processes
(zero-touch)
Customer-centric
services
Generate
new revenue
streams
Enable
AI-based
services

7. AI models adopted in Telco
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Prediction
Supervised: Artificial Neural Networks, Decision Trees learning, Regression Analysis,
Support Vector Machines, Naive Bayes
Customer Churn
Predictive Maintenance
Fraud Propensity
Traffic Peak Prediction
Problem Use Case Model
Anomaly
Detection
(Semi-)Supervised: one-class Support Vector Machine
Unsupervised: Auto-encoders, cluster analysis, self-organizing maps, k-means
Traffic Analysis
Behavior Analysis
Network Monitoring
Predictive Maintenance
Profiling
Supervised: Recurrent Neural Networks (RNN)
Unsupervised: Principal Component Analysis
Customer Profiling
Network Usage
Next Best Offer
Classification
Supervised: Artificial Neural Networks, Decision Trees, Logistic Regression, Support
Vector Machines
Customer Care
Failure Analysis
Content Classification

8. Spotlight on Deep Learning
Data Availability
- 2.5 exa-bytes
(1018) per day!!!
- Deep learning
performance scales
almost linear wrt.
data amount
Text here
Computation Power
- Deep learning is
eager for training
resources
- Telco data centers
embed thousands
of machines with
very fast
connection
Text here
Edge Computing
- 5G moves
intelligence at the
edge
- Deep learning
scales very well on
dedicated hardware
(e.g. GPU, TPU)
- Collaborative
learning techniques
- Mobile AI
frameworks
(TensorFlow Lite)
Text here
Online Model Training
- From batch
analysis to real-
time streaming
- AI models need to
re-adapt to
evolving patterns
- What yesterday
was anomalous…
today is not!
- Still a lot to do in
this area…
Text here
Unsupervised Learning
- Labeling datasets
is not feasible
- No training effort
- Anomaly detection
algorithms largely
adopted for traffic
analysis (e.g. Auto-
encoders)
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9. AI-based Customer Care
• By 2020, 85% of all customer interactions will be handled
without a human agent
• Resolving customer service issues before they arise could
significantly lower customer churning rate
• Combination of AI, NLP, chat-bots
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HOW
• Anticipate customer needs by continuously profiling user
behaviors (anomaly detection)
• Extract potential complaints published on social networks
(sentiment analysis)
• Correlate user complaints with detected network failures
(cluster analysis)
• Compare incoming problems to support cases already
evaluated (root cause analysis)
• Predict potential problems (time-series analysis)

10. Customized Marketing
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• 79% of CMOs are planning to boost customer experience
with AI
• AI algorithms combine historic patterns and behaviors to
provide personalized offers for subscribers
HOW
• Extract enriched customer insights from multiple data
sources
• Predict the potential interest of each customer for
available offers (prediction and ranking)
• Automatic offering proposition (when, what, how)
• Feedback collection and model update (reinforcement
learning, incremental learning)

11. Self-healing Networks
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• A UK mobile operator was recently fined £1.9 million for
a network design configuration that would have
compromised access to the 911 emergency service
• AI predicts future traffic peaks and load distribution for
automatic network scaling
• AI predicts potential network failure for automatic alarm
triggering and network re-configuration
• Combination of AI and network virtualization (NFV)
HOW
• Predict what will happen in the network (predictive
networks)
• Evaluate and guide in assessing the impact of the
prediction (prescriptive networks)
• Automatic action selection to mitigate the impact of a
predicted outcome (self-healing networks)

12. Thank you
Gabriele Randelli
gabriele.randelli@hpe.com