"Telco Data Science in Real Life"

1. Telco Data Science in Real Life
Jan Romportl
Chief Data Scientist, O2 Czech Republic

2. Data, map and territory

3. Data Science
Yet another probably useless definition:
Interdisciplinary technically-oriented field focusing on acquisition of
innovative business-relevant intelligence from very heterogeneous
data sources.
The key word in “Data Science” is not Data, it is Science.
(Jeff Leek, 2013)

4. Three big-data loving modes of telco

5. Three big-data loving modes of telco (1)
Mode 1
Selling Data
• many subjects are interested in
insights hidden in telco data
• anonymized telco data are sold
externally
• telco company exploits only
negligible potential of the data
• what about differential privacy?
$

6. Three big-data loving modes of telco (2)
Mode 2
Data-Driven Enhancements
• data products enhance
internal processes and current
business
• utilization of Big Data platform
for BI and product/process
improvement
• “data science“ can be partially
automated out-of-the-box
Actions
Enhancements
Optimization

7. Three big-data loving modes of telco (3)
Mode 3
Monetizing Data-Driven
Intelligence
• new revenue streams based on
data products
• data-driven intelligence, insights
and products are sold instead of
raw data
• Big Data platform and full-blown
Data Science team – disruptive
potential for new business
development
Actions
Enhancements
Optimization

8. O2 Czech Republic
„Mode 3“

9. O2 Big Data Platform
Teradata
EDW
HDP
Hadoop
Teradata
Aster
Local R
and
Python

10. O2 Big Data Streams
Customer
EDW data
Webtraffic
DPI
SS7
network
stream
O2TV
STB event
stream

11. Some of O2‘s Data Products
OneTable
Customer
Geoprofiling /
TVprofiling
Customer
Webprofiling
Look Alike
Targeting
Models
Outdoor
Advertisement
Measuring
Credit Scoring
Mobility
Modeling and
Analysis
Next Best Offer
Cross-Media
Analysis and
Targeting
O2
Media
Liberty
API

12. Some of O2‘s Data Products

13. Customer Profiling
• continuous modelling of attributes and segmentations
of resident customers
• used for marketing segmentation, targeting, BI
analyses, etc., but also as inputs (predictors) to most
of other data products
• done from all data sources
• wide range of mutually interconnected models and
filters of various types
• fast/slow attributes
• interpretable/latent attributes
• automatization and integration of all outputs in our
OneTable

14. Next Best Offer
• O2 product recommendation system
• developed in-house – suits all O2’s specificities
• own algorithm – combination of unsupervised
clustering a supervised predictive models
• utilization of all predictors from OneTable
• large matrix operations
• ARPU increased at all channels using NBO

15. Mobility Data Products
measurements and planning
of outdoor billboard advertisement
traffic analyses
analyses of visitors, transits, etc.

16. Mobility Data Products

17. Liberty API – Socio-Demo and Mobility

18. Smart Targeting
• supervised and unsupervised machine learning for
targeting of B2B2C campaigns (SMS, online)
• click prediction from all predictors in OneTable
• feedback-based learning

19. SNA and Community Detection
• predictive models for detection of communities
(families, interest groups, etc.) and Social Network
Analysis from CDR
• marketing utilization
• input to other data products
• utilization of NLP methods modified for graph
analytics
• utilization of neural networks and other graph
analytics tools

20. Web-Interests
• segmentation of customers’ interests based on web
browsing behavior
• automatic webscraping and machine classification of
web pages to interest categories based on text
processing
• good old bag-of-words & SVM approach with n-grams
and TF/IDF – still suitable for this needs
• statistical modelling of customers’ interests based on
interest categories of visited webs

21. Thank you
Connect with me @JanRomportl
jan.romportl@o2.cz