Describes Machine learning and predictive analytics applied to telecom use cases. Maps cognitive computing concepts to machine learning.

1. 1
Machine Learning use cases in Telecom
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Sai Devulapalli, Dir. of Data Analytics Market Development
Ericsson Inc.
July 21
6:30 PM to 8PM
Call: 605-475-5950
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Remote Page Login: www.cognitivecomputingexperts.com
Physical Meeting...
Improving Enterprises
16633 Dallas Parkway Suite 110 Addison, TX 75001, Addison, TX

2. Cognition in Telecom
 Memory – Store results and outcomes
 Learning – Supervised and unsupervised
 Judgment – Cost functions
 Perception – Behavioral Signature detection
 Multi-modal – Social networks to Radio networks
 Reasoning – Decision trees and rules
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3. Machine Learning in Telecom
 Predictions about….
 Customers – Behavior, Experience, Churn
 Devices – Uptake, Performance, Failure
 Networks – Performance, Failure
 Campaigns – Uptake, Network Impact
 Services – Uptake, Performance, Failure, Network Impact
 Customer Segmentation and recommendations
Machine vs.
Management
Machine as tursted
advisor to
Management

4. Data Sources and Use-cases
Mobile Device Agent Data
CDR/Billing Data
CRM Data
Network Performance Data
Social Media Data
Service Experience Data
Network Asset Data
(Inventory, NFV Assets, etc.)
M2M Data
DataAnalyticsFramework
Data Sources
CostFocusedRevenueFocused
Predictive Network Operations
Customer Experience Management
Proactive Care
Self Care
Single Pane of Glass
Centralized SON
M2M Analytics
Retention Analytics
Customer Segmentation/Upsell
External Data Monetization
Predictive Inventory/Logistics
Use Cases
Customer Centric Network Ops