Identified key data patterns to give recommendations for reducing customer churn and better network management

1. Big Data Solution For
YTT Telecom

2. YTT Telecom
● Leading edge mobile voice, data and multimedia services
Company (63 M customers)
● Focus on R&D to enrich customer lives
● Adoption rate > 20%.
● 20% users switched to smart phone, > 3 times over 2013
● Need robust infrastructure to accept rapidly growing
network traffic
Telecom Eco-system

3. Data Data Everywhere
YTT Data Challenge Matrix
POS Data
Locations
Payments
Sensor Data
Customer Profiles
Weather
Shipments
Transactions
HR Records
Financial Records
Google+
Twitter
Facebook
Call Center Data
Click Stream
Text Messages
Online Forums
Video
Sharepoint
3rd Party Text Documents
Velocity
Variety and Volume
Portal
Customer
Touch
Points
Store
FB
Twitter
Yelp
Mailers
Offers
Call
Center
Email
SMS
Network
Data
• Billions of Call Detail Records
Location
Data
• 60 TB of Location Data
Customer
Data
• Millions of records for
63 M customers
Structured Unstructured

4. Problems Highlights
Smart Devices Data Needs Services Provided
Customer Churn
• How to keep the customer happy/satisfied and reduce churn?
Network Management
• What strategies should YTT apply to store and analyze network data and resolve issues in
real time?
Marketing Campaign Efficiency
• How to run effective and targeted campaigns?

5. Key Trends
Expected Budget of Telecom Companies for Handling
Big Data
Big Data Analytics to optimize
network performance and reduce
cost - T Mobile
Big Data Analytics for effective
promotions
Big Data for Real Time
Intelligence and control back into
the network
Responses to Big Data Initiatives

6. Proposed Solution - Strategy
Gather
internal/external
data
Ingest and
standardize data
Apply S/W Tools
to Prepare,
Process, Analyze
and Export Data
 Learn and Label : Segment Customers on the usage patterns, learn preferences,
create labels and store with the profile. Create/offer suitable/customized plans.
 Empower Customer Service : Allow a representative to help in near real time to
resolve issues and make offers. A customer is rated/ranked on the basis of usage,
payment history and interests.
 Proactive Network Management : Detect network spikes, analyze dropped calls
from CDR analysis, inform Customer Service in case of dropped calls to make a
friendly call to the customer facing the problem.
 Understanding Sentiment : Find out +ve/-ve sentiments on Social Networks/blogs
etc. pre/post campaign to see the effectiveness.
Derive actionable
insights
Determine
actions on
results
obtained

7. Solution Tech Stack
DATA SOURCES
ERP
CRM
Billing
Records
Subscriber
Data
Network
data
Product
Related
Data
Customer
Behavior
Click Stream
Online chat
Sensor Data
Social Media
System
Operations
Server Logs
Call Detail Records
Merchant Listings
Signaling Logs
Protocol Logs
INGEST
Sqoop
Flume
HDFS.Put
Web.HDFS
Physical Layer
Ad-hoc Query
Analysis
CDR Analysis
Proactive network maintenance
Bandwidth Allocation
Infrastructure Investment
Operational dashboards
Customer scorecards
Product Development
Oracle Workflow
Scheduler
Pig Data Analytics
Hive Data
Warehouse
Metadata Management : HCatalog
Multitenant Processing : YARN
Mapreduce Libraries Hbase Database
Compute and HDFS Storage
Hadoop Distributed File System
Analysis Layer

8. Proposed Solution Architecture
Call Detail Records
Call Center Record
Network Logs
Tower CDRs
Hadoop MapReduce + QoS Reports, Billing Information
Pig Data Analytics
Hbase Database +
Hive Queries
Social Media Data
Traffic Reports, Network Audit
Reports
Hbase Database + Pig
Data Analytics
Call Volume Reports, Routing
Graphs
Traditional
Datawarehouse + SQL
Customer Service Reports, Closed
Loop reports
Hadoop MapReduce +
COGSA
Sentiment Analysis Reports,
Funnel Reports

9. Solution Design Mock-Up
Handling Network Congestion
Tower CDR Log
Caller A;Caller B;Date;Time;Duration;Call Type;First Cell ID;Last Cell ID;Cell ID Zip
9096714043;9163281129;8/4/2014;9:45:23;0;SMS-IN;405-799-20-36023;405-799-20-36361;94709
7276789858;9806154895;8/5/2014;9:50:11;1161;CALL-IN;405-799-20-36023;405-799-20-31611;94150
…………………………………………………………………………………………………………………….....
MapReduce Job
Generates pairs of
(tower id, # calls
routed)
Tower Number #Calls (in000s)
405-805-105-60382 234
405-805-127-10223 213
405-805-127-33891 206
405-805-127-10221 156
405-805-105-60383 143
…………………….. …..

10. Deployment- Strategy
2 Week plan to validate proposed solutions for:
 Customer churn
 Network traffic
 Optimized Marketing spend
Identify Data
Sources
Unify And
Assemble Data
Clean and
Enhance Data
Quality
Append
Content
Build Analytics Analyze
Review
Dashboard
OK to Proceed
 Give us Access to YTT data and
approval
 Provide following resources:
 1 Data Engineer
 1 Network Engineer
 1 Data Scientist
 1 BI Engineer
Allow access to Cloud AWS infra
(free trial) or equivalent

11. Proposed Solution Benefits

12. Summary
• Big data offers YTT Telecom a real opportunity to gain a more complete picture of
their operations impacting their customers, and to further their innovation efforts.
• YTT’s focus on R&D is to enrich customer lives. This solution proposal is in
consistence with their focus.
• Big data challenge can be met on the lines of the proposed Solution Architecture.
• YTT should incorporate new agile strategies into their organizational DNA fast so
that it will gain a real competitive advantage over their slower rivals.
References:
1. TELECOMS.COM INTELLIGENCE INDUSTRY SURVEY 2014 –
http://www.telecoms.com/wp-content/blogs.dir/1/files/2014/03/IndustrySurveyReport14_latest1.pdf