Ceska sporitelna is one of the largest banks in Central Europe and one it’s main goals is to improve the customer experience by weaving together the digital and traditional banking approach. The talk will focus on the story of how in order to reach this goal Ceska Sporitelna created a new team focused on building use cases on top of a combined digital and offline customer engagement 360 powered by a Spark and Databricks-centric agile advanced analytics platform in the Azure cloud combined with a on-prem data lake. This talk will cover: The customer engagement 360 vision powered by machine learning and the cloud Deep dive into the use case of optimizing and personalizing programmatic ad buying on the individual user and ad placement level thanks to Spark MLLib and NLP on top of hundreds of millions of ad interaction data Deep dive into the use case of supporting the seamless transition of the customer journey from digital to traditional offline channels The approach to building the agile analytics platform and experience of adopting the cloud in a EU-regulated financial institution

1. WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

2. Creating an Omnichannel Banking
Experience with Machine Learning
on Azure Databricks
Petr Pluhacek (Ceska Sporitelna)
Jakub Stech (DataSentics)

3. Who is presenting
today
Petr Pluháček, Česká spořitelna
Product owner:
• Digital engagement and acquisition squad
Responsibilities:
• Digital sales
• Web platforms and chats
• Digital marketing data and analytics
• Online customer experience
Contacts:
• ppluhacek@csas.cz
• www.linkedin.com/in/pluhacek

4. Česká Spořitelna
About us:
• Almost 200 years history
• 4,6 millions customers
• 10 000 of employees
• Part of ERSTE group
• Driver of innovation in the group
• Undergoing agile transformation
ČS Mission:
“We are your lifelong guide on the path to prosperity, and in
this way we contribute together to the prosperity of the whole
country. When someone believes in you, you achieve more."

5. Who is presenting
today Jakub Stech, DataSentics
Data Science architect in:
• DataSentics and Digi data team in CSAS
Responsibilities:
• Translate business problems for data science team
• Personalizing user experience using data and machine
learning approaches
• Building and employing the analytical platform in cloud
Contacts:
• jakub.stech@datasentics.com
• www.linkedin.com/in/jakubstech

6. DataSentics – European Data Science Center
of Excellence based in Prague
• Machine learning and cloud data
engineering boutique
• 50 data specialists (data science,
data/software engineering)
• Helping customers build end-to-end data
solutions in cloud
• Incubator of ML-based products
• Partner of Databricks & Microsoft
• Make data science and machine learning
have a real impact on organizations across
the world
• Bring to life transparent production-level
data science.

7. Digital marketing spend optimization
Case study #1

8. ADVERTISMENT WWW.CSAS.CZ INTERNET BANKING OTHER
SOURCES
BRAND AWARENESS
SALES (ONLINE AND OFFLINE)
CARE
Digital marketing interactions

9. How to improve digital marketing spend
effectiveness
Business challenge:

10. 10

11. ADVERTISMENT WWW.CSAS.CZ INTERNET BANKING OTHER
SOURCES
BRAND AWARENESS
SALES (ONLINE AND OFFLINE)
CARE
Digital marketing interactions

12. Adform is one of the world's largest private and independent
advertising technology companies and is best known for its
seamlessly integrated DSP, DMP, and Ad Server.
ADVERTISMENT

16. A viewable impression is a standard measure of ad
viewability defined by the International Advertising
Bureau (IAB) to be an ad which appears at least
50% on screen for more than one second.

17. Decrease costs for visible seconds
Better specification of business challenge

18. WHEN
WHO
WHAT
WHERE
DEVICE
GEO
INTERACTIONS
DURATION
INT. COUNT
HOW MUCH
Adform Master Data…

19. Robot score (Adform definition)
Soft fraud score (own definition)
Multiple impressions
CTR (too low/high)
Visibility %
Visibility
time
SEZNAM.CZ; BID 0.2 ;
NOVINKY.CZ; BID 0.6 ;
IDNES.CZ ; BID 0.1 ;
BLESK.CZ ; BID 0.4 ;
…
Metrics of domain model

20. • API
• BigQuery
• SFTP
• CSV
• Packages
• JSON
• Database dump!
• Web Pages
• …
Daily download, transformation and scoring jobs
4+ BLNs rows in 12months!
Data sources

21. Automated
download
MS Azure
CSAS Storing the data in
Data Lake
Results stored
into Data Lake
Scoring new
domains
Whitelists, bid
multipliers,
cookies lists,
blaclicks, …
Automated
update
SEZNAM.CZ; BID 0.2 ;
NOVINKY.CZ; BID 0.6 ;
IDNES.CZ ; BID 0.1 ;
BLESK.CZ ; BID 0.4 ;
…
Fully automated pipeline

22. 23% Desktop
28% Mobile
Increased effectivity of 1 EUR:

23. Omnichannel Banking Experience
Case study #2
23

24. Leading every client to prosperity
=
Data-driven advisory based on
clients needs and real-time situations
24

25. … is not easy
in bank
Customer-
centricity….
Low frequency of
interactions between a
client in offline channels

26. 100 Things,
We touch our
phones 2,617
times a day,
says study
Around 100
sessions every
day…

27. Offline vs. Online
Typical CRM data
• Age/sex/address, policy history, policy
configuration, claim history, sales channel,
…
• Static, mostly long-term behaviour
• Facts and transactions
• Well structured, easy to process with
traditional tech
Digital „footprints“
• Ad interactions (wider internet behaviour),
web interaction (own sites), mobile apps,
external/partner data, …
• Dynamically changing, reflecting short and
long-term needs
• Uncertainty, fragments about interests,
behaviour, lifestyle
• Enormous data (B+ ads, M+ visits of
website…), messy, unstructured, changing
interfaces

28. 1
Ad Interactions
(what the person is
interested in across
the internet)
Own website
interactions
Emailing /
SMS / Push
Siloed customer behaviour data
Classic client profiles
4) Limited customer
experience
2) Missing
environment for
data analytics
and machine
learning
Classic
CRM / data
processes Branches
& sales networks
Transactional
data / product
data
Callcentrum
data / call
logs
Digital campaign
management tools
Classic campaign
management tools
1) Missing connection
between digital and
CRM
3) On-premise
environment is lacking
customer data from
digital
Mobile app
interactions
Client portal /
Internetbanking
interactions
3rd party data,
voice, text,
image, geo
data, etc.
Digital
engagement
(3rd party)
Offline vs. Online

29. 1
Ad Interactions
(what the person is
interested in across
the internet)
Own website
interactions
Emailing /
SMS / Push
Non-client & client behavior
Classic client profiles
Automatic
optimization,
personalization of
customer journeys
Machine
learning
Your Customer
Engagement
360° Platform
(CSAS)
Classic
CRM / data
processes
Automatic signals
for classic
channels
Branches
& sales networks
Transactional
data / product
data
Callcentrum
data / call
logs
Connecting the data on
individual customer level
Digital campaign
management tools
Classic campaign
management tools
New opportunities
Higher
efficiency
Mobile app
interactions
Client portal /
Internetbanking
interactions
3rd party data,
voice, text,
image, geo
data, etc.
Digital
engagement
(3rd party)
AI-augmented Customer Engagement 360°

30. CASH LOAN MORTGAGESAVING ACCOUNT
Non-client & client behavior
Classic client profiles
Connecting the data on
individual customer level

31. Digital data based sales signals
Mortgage / Loan / Saving account / Investments …
What product?

32. OK, Loan… but what message?
Digital data based sales signals

33. Digital data based sales signals

34. MACHINE LEARNING
Digital data based sales signals

35. Call script: “Client is interested in taking loan. He
is planning wedding according to his activity on
internet.”
Digital data based sales signals

36. Standard classification models
(GLMs as benchmark, GBTs in production)
ML(lib) part
TEXT REPRESENTATIONS
CUSTOMER PATHS
NUMERIC REPRESENTATIONS

37. Loans predicted by Adform data
4x Higher probability
Words with predictive power: loan, car, moto, wedding, …

38. ADFORM
WEB
DMP
CRM
CALL CENTRES
…
ADFORM
GA
CRM
Automated
downloadMS Azure
ČS
Storing the data in
Data Lake
Results stored
into Data Lake
AI monitoring
dashboard
Model (AI)
re-training
Scoring new
data using
existing model
(AI) Whitelists, bid
multipliers,
cookies lists,
blaclicks, …
Automated
update
Transforma
tion
Results
(bids,
audiences,
…)
Fully automated pipeline

39. Current architecture

40. 50% Improvement
Benchmark:
Offline sales signals for mortages sales: call centre 10% sucess rate
Current:
Digi data enhanced signals for mortages sales: call centre 15% sucess rate

41. Take aways
• No extra tech, just extend platforms with AI
models
• Connecting the data on individual customer
level is crucial
• Clear business specifications and convincing
results are essential

42. Thank you

43. DON’T FORGET TO RATE
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