DataCon Talk

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DATA-CON BOSTON 2014

TANYA CASHORALI

@TANYACASH21

Real Time Market Data & Analysis for the Telecom Industry

2 THE TELECOMMUNICATIONS

MARKET IS FULLY SATURATED

67% of new subscriber growth comes from switchers.*

Last year, switchers put $29B up for grabs in the wireless industry.

WINNING SWITCHERS IS KEY FOR

GROWTH

*Percentage of net adds that are not activations, tablets or wholesale


3 CARRIER VISION – THE WHAT,

WHERE, WHEN


4 VOICE OF THE CUSTOMER –

THE WHY


5 OBSERVED DATA ON TWITTER


6 STARTED GATHERING DATA USING

STREAMR


7 HUMAN SCORED DATA

We “S-scored” about 150-500 tweets per day until we had ~4,000 human scored tweets.

Used this data set to learn how to systematically crowd source the same process and also

automate the process using machine scoring “M-scoring” in R

Training Set

of 4,000

tweets

Crowd sourced ~80,000 tweets

Derived rules

M-scoring rules in R


8 CLASSIFY CARRIERS


9 NLP AND SENTIMENT ANALYSIS IS

HARD


10 BASIC PATTERN MATCHING IS

COMPLICATED ENOUGH

• Phone Price

• Customer Service

• Coverage Quality

• Upgrade Plan

• Coverage Availability

• Family Plan

• Plan Price

• Service Promo

• Outage

• Data Plan

• Phone Availability

• Device Promo

• Switching to / from

iPhone + expensive

Samsung Galaxy + money

Phone + cost

Data, unlimited

Switch from Verizon to ATT


11


12 M-SCORING EXAMPLES

Phone

iPhone 6 Switch to T-Mobile

Mentionto T-Mobile

Switch from [carrier1] to [carrier2]


Switch from [carrier1]

Switch to [carrier1]


13 M-SCORING EXAMPLES

T-Mobile

Mention

Switch from ATT

From ATT


Switch from [carrier1] because I love [carrier2]

Assume carrier2 is the ‘switch to’ carrier

Phone


14 CATEGORY CLASSIFICATION


15 SWITCHING TO/FROM


16 ARE WE IMPROVING?

Sensitivity = TP / (TP + FN)

Specificity = TN / (TN + FP)


17 MATURING INTO A REAL PRODUCT


18 GNIP PROCESS

Table Daily Average Total

Wireless 655 111,483

Wireline 152 25,264

Other 11,803 2,006,649


19

StreamR GNIP DataSift

Full firehose data - X X

Historical Twitterdata

- X X

Real-time X X X

Data Destinations - - X

Data Buffer - 5 days ($$$) 2 hours(included)

Demographics - - X

Sentiment - - X

Gender Detection - - X

Topic Detection - - X

Entity Extraction - - X

Link Analysis - - X

STREAMR VS GNIP VS DATASIFT


20Deployment Architecture Comlinkdata

Amazon RDSData Source

GNIP

Amazon EC2

Technology Stack

PostgreSQLR, PostgreSQL

Kantar

Events

PostgreSQL

/home/ubuntu/Documents/Scripts/TwitterScrapes/GNIP/GNIPExtract.R

/home/ubuntu/Documents/Scripts/TwitterScrapes/voc-dataops/GNIP/Json_Interpreter.R

/home/ubuntu/Documents/Scripts/TwitterScrapes/voc-dataops/M-Scoring/simple_MScore_GNIP.R

/home/ubuntu/Documents/Scripts/TwitterScrapes/voc-dataops/Aggregates/af_tweets.R

HTTPS

JSON

Chrome, Safari, Firefox, IE

Internet

Application and Web Servers

MGTomcat

Web Service (REST) GUI

Java 6, Spring (Security, MVC,

JDBC)

Angular JS/Ajax, HTML5, CSS3, D3,

Bootstrap

Shiny

server@ubuntu

gnip_wireless_rawgnip_landline_raw

gnip_other_raw

tweet_mwltweet_mll

a_tweetsf_tweets

/home/ubuntu/GNIP/data/


21 INITIAL PROTOTYPE IN TABLEAU


22 WHAT WE LEARNED

• Always store raw unprocessed data

somewhere

• Beware of UTF encodings and special

characters

• Ensure time zones are synched across

databases / applications

• Don’t be afraid to cast a larger net of tweets

given the ~1M tweet/month limit provided by

most vendors

• Consider how to deal with blast tweets,

retweets. Source can be used to help

identify blasts (TweetCaster, Scoop.it, etc.)


23 ENTIRE PRODUCT LIFECYCLE


24 CONSIDERATIONS

Many variations of changing carriers:

• Bye Sprint!

• Getting rid of Verizon

• Peace out T-Mobile

• Going to AT&T

• Twitter data is not necessarily representative of the entire population

• Other languages

• Geo-tagged is only ~5%

• Expanding to Canada


25 FUTURE WORK

• Migrate to DataSift from GNIP

• Improve M-scoring using more complex NLP algorithms

• Integrate additional data sources including downdetector.com, ad spend, and other

relevant sources

• Integrate additional Twitter data (mentions to carriers as well as tweets from the

carriers, various language around switching)

• Develop landline version and Canada version

• Trending words / Keyword search

• ???


26 ACKNOWLEDGEMENTS

Jacob TobiasSoftware Developer

Sarah BoltMarketing

Manager

Dylan DoyleData Scientist

Mallorie EkstromGraphic Designer

Alan TamWeb Developer

Josh WalkerCOO

Ken YeohData Engineer

For more information, hiring, or

questions, email

[email protected]


27 REFERENCES

• streamR - http://cran.r-project.org/web/packages/streamR/index.html

• sqldf - http://cran.r-project.org/web/packages/sqldf/index.html

• GNIP - http://gnip.com/

• DataSift - http://datasift.com/

http://cran.r-project.org/web/packages/streamR/index.html

http://cran.r-project.org/web/packages/sqldf/index.html

http://gnip.com/

http://datasift.com/