Big Data Solution For

YTT Telecom

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

Data Data Everywhere

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

Vel

oci

ty

Variety and Volume

YTT Data Challenge Matrix

Customer Touch Points

Portal

Store

FB

Twitter

Yelp

Mailers Offers

Email SMS

Call Center

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

Problems Highlights

Smart Devices Data Needs Services Provided

• How to keep the customer happy/satisfied and reduce churn?

Customer Churn

• What strategies should YTT apply to store and analyze network data and resolve issues in real time?

Network Management

• How to run effective and targeted campaigns?

Marketing Campaign Efficiency

Key Trends

Expected Budget of Telecom Companies for Handling Big Data

Big Data Analytics for effective promotions

Big Data for Real Time Intelligence and control back into the network

Big Data Analytics to optimize network performance and reduce cost - T Mobile

Responses to Big Data Initiatives

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.

Proposed Solution - Strategy

Gather internal/external

data

Ingest and standardize data

Apply S/W Tools to Prepare,

Process, Analyze and Export Data

Derive actionable insights

Determine actions on

results obtained

Solution Tech Stack

DATA SOURCES

CRM

Network data

SubscriberData

Billing Records

ERP

Product Related

Data

Customer Behavior

Click Stream Online chat Sensor DataSocial Media

System Operations

Server LogsCall Detail RecordsMerchant Listings

Signaling LogsProtocol Logs

Physical Layer

INGEST

SqoopFlume

HDFS.PutWeb.HDFS

Hadoop Distributed File System

Mapreduce Libraries Hbase Database

Ad-hoc Query Analysis

Oracle Workflow Scheduler

Pig Data AnalyticsHive Data

Warehouse

Multitenant Processing : YARN

Compute and HDFS Storage

Metadata Management : HCatalog

CDR AnalysisProactive network maintenanceBandwidth AllocationInfrastructure InvestmentOperational dashboardsCustomer scorecardsProduct DevelopmentAnalysis Layer

Proposed Solution Architecture

Call Detail Records

Network Logs

Tower CDRs

Call Center Record

QoS Reports, Billing InformationHadoop MapReduce + 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

Tower CDR LogCaller A;Caller B;Date;Time;Duration;Call Type;First Cell ID;Last Cell ID;Cell ID Zip9096714043;9163281129;8/4/2014;9:45:23;0;SMS-IN;405-799-20-36023;405-799-20-36361;947097276789858;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 234405-805-127-10223 213405-805-127-33891 206405-805-127-10221 156405-805-105-60383 143…………………….. …..

Solution Design Mock-UpHandling Network Congestion

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

Proposed Solution Benefits

• 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.

Summary

References:

1. TELECOMS.COM INTELLIGENCE INDUSTRY SURVEY 2014 – http://www.telecoms.com/wp-content/blogs.dir/1/files/2014/03/IndustrySurveyReport14_latest1.pdf