Challenges and

Opportunities in

Telco Big Data

Baofeng(Felix) ZHANG

Noah’s Ark LAB, Huawei

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Big data is going from bubble to practice

“High volume, velocity, and/or variety information assets that

demand new, innovative forms of processing for enhanced

decision making, business insights or process optimization.”

The ability to analyze data in new ways, leveraging new sources,

all in economically quicker ways, on enormous, varied or

rapidly changing datasets.

- Gartner2014

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How Big Data Analytics bring value?

Descriptive Diagnostic

Business

Value

A B

Prescriptive

D

Predictive

C

___

___

___

___

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What’s Telco Big Data?

Personal information, Billing, Balance, etc.

Call graph, SMS graph, Co-occurrence graph, etc.

Complaint information, Search queries, etc.

Signaling data, Video streaming, Audio streaming, Photo streaming, etc.

Voice call recording

Messurement Reports (MR), Voice CDR, SMS CDR, GPRS CDR, etc.

Tabular

Stream

Graph

Spatiotemporal

Multimedia

Text

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Case Study: Telco Big Data in Volume

Data Source

SMS, VAD

Records

Data & Voice CDR

Profile/Subscription/Accoun

t Info

Call Center

Drive Test Data

MIS Data Net/Flux DR、

SC Data SIG Fix Net

Data Set 合计

Volume/day

15G 26G 94G 0.14G 0.02G 1900G 2880G 9000G 13.8T

Records/Day

0.12B 80M 50M 160K 370K 2.5B 10.6B 18B 31.35B

Above is only about 4M Mobile Subscribers data per day, totally 1.2B in China

BSS OSS + ~3% ~97%

B Side Data Set: Small

volume, Collection, Off Line,

More for subscriber behavior

O Side Data Sets: Big volume, in

detail, real-time, more for network

behavior

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Telco Big Data Challenge 1: Telco

Spatiotemporal (TST) Data

Sparseness Inaccuracy

Dependency

Telco Spatiotemporal

Data

Heterogeneity

Green：GPS Trajectory

Red：Telco Trajectory

Low sampling rate

Data gathering noise

Temporal dependency

Spatial dependency Temporal graph

Location-based Social Network

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Case Study: Churn Prediction and Retention

Systems（1）

•Customer churn is perhaps the biggest challenge in Telco industry.

• Two contributions: 1) feature engineering based on OSS data and

2) profit-driven retention campaign system.

•Location features improve around 8% performance.

Yiqing Huang, Fangzhou Zhu, Mingxuan Yuan, Ke Deng, Yanhua Li, Bing Ni, Wenyuan Dai, Qiang

Yang, Jia Zeng: Telco Churn Prediction with Big Data. SIGMOD Conference 2015: 607-618

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Case Study: Churn Prediction and Retention

Systems（2）

Month 2015-01 2015-02 2015-03 2015-04

#Churner - - - -

#Non-churner - - - -

#Total - - - -

Churn rate % 9.0% 8.5% 7.1% 6.5%

Group A: no retention offer is provided.

Group B: retention offers provided like “Get 100

cashback on recharge of 100”, “Get 50 cashback on

recharge of 100”, “Get 500MB flux on recharge of 50”,

and “Get 200-minute voice call on recharge of 50”.

Month 8: without matching retention offers with

potential churners, .

Month 9: offering retention offers to churners.

The recharge rate has been significantly boosted by

using our solution.

After deployment of this system, the churn rate of prepaid customers drops significantly in 2015.

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Case Study: Customer Insight ~ Precision

Marketing

24% 11%

4% Conversion rate after data-based Filtering

Precise User-interest modeling

Precise User-interest modeling with proper

channel selection

0.7% Regular Marking conversion rate

Phase I： 2013年

Phase II First half of 2014

45% Best Case：Scenario selective, precise user interest modeling with proper channel selection

Phase III After

Benchmark:

15%

Profile Tags

Basic Characteristics 94

Terminal info 80

Voice calls 88

Billing info 77

SMS/MMS 121

Traffic 70

Internet behavior 56

Apps 191

Product subscribed 5

IVR/Call center 96

Account settlement 120

Total 998

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Case Study: TST Data Openness

• Distribution map of Network Speed

• Grid map for coverage +

DT/CQT+complaint

• Distribution Map of Valuable User

• Correlation between Coverage and

Complaint rate.

Nielsen(尼尔森) 对上海烟草公司提供店铺选址服务和销售渠道评估。数据来源于：

• 城区人流量栅格化分析

GFK 对××地的××个公交站台和××块LED 户外广告屏进行人流量分析，提供以下服务

• 为广告主提供广告屏价值依据分析. • 广告内容投放位置建议

Value Evaluation of Out-door Advertise Screen

Retail store location selection

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Telco Big Data Challenge 2: Knowledge-

enabled Efficiency Improvement

Search Engine

Learning to rank

Interactive search

Knowledge Representation and Inference

Text mining Probabilistic

models

Natural Language Dialogue

Deep learning

Knowledge integration

Network Fault Diagnosis

Dialogue Analytics Solutions

Knowledge Base

iCase iCare Other sources …

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Case Study: Root Cause Analysis(On-going)

Entity and Relation Extraction from

iCase (300K cases, 20K service

engineers)

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Practice in Telco Big Data

End-user Centric

Business

Value

SpatioTemporal

Data as Clue

“Full-size” Data Modeling

Copyright©2015 Huawei Technologies Co., Ltd. All Rights Reserved.

The information in this document may contain predictive statements including, without limitation,

statements regarding the future financial and operating results, future product portfolio, new technology,

etc. There are a number of factors that could cause actual results and developments to differ materially

from those expressed or implied in the predictive statements. Therefore, such information is provided

for reference purpose only and constitutes neither an offer nor an acceptance. Huawei may change the

information at any time without notice.

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