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 real world (both technical and enterprise) challenges during shifting the vision from powerpoint slides into production: Implementing Spark and Databricks-centric analytics platform in the Azure cloud combined with a on-prem data lake in the EU-regulated financial environment Forming a new team focused on solving use cases on top of C360 in the 10 000+ employee enterprise Demonstrating this effort on real use cases such as client risk scoring using both offline and online data Spark and its MLlib as an enabler for employing hundreds of millions of client interactions personalized omni-channel CRM campaigns

1. Bank struggles along the way for
personalization: Customer 360°
Jakub Stech, Veronika Pjescakova

2. Speakers
• Data science architect at DataSentics, a
European machine learning and cloud data
engineering boutique.
• Bringing data science solution for business
use case based on 360° view on the customer
to understand customer needs and
personalise experience using ML approaches.
Jakub Stech

3. 3
Make data science and machine learning
have a real impact on organizations across
the world
Bring to life transparent production-level
data science.
• Machine learning and cloud data engineering
boutique
• 80 data specialists (data science, data/software
engineering) mainly focusing on FSI and Retail
• Helping customers build end-to-end data solutions
in cloud
• Incubator of ML-based products
• Partner of Databricks & Microsoft
DataSentics - European Data Science Center of
Excellence

4. Speakers
• Product owner of Customer 360 at Ceska
sporitelna, one of the largest banks in Central
Europe.
• She is responsible for building the Databricks-
centric analytics platform in Azure combined
with an on-prem data lake in order to fulfil the
customer engagement vision.
• Data science architect at DataSentics, a
European machine learning and cloud data
engineering boutique.
• Bringing data science solution for business
use case based on 360° view on the customer
to understand customer needs and
personalise experience using ML approaches.
Jakub Stech Veronika Pjescakova

5. Česká Spořitelna
• Almost 200 years history
• 4,6 million customers
• 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

6. Agenda
• Why personalization matters
• Challenges in change management
• Typical data in (retail) banking
• Solution: C360 Personalization
+ Struggles along the way

7. Personalization

9. Higher expecations from
customers

10. Personalization in Retail Banking

11. Financial products are
complicated
= Education?
= Personalization?
= Explanation?

12. *PERSONALIZATION „SEGMENT OF 1“USEFUL CONTENT
Products
Services
Addons
Hints
Articles
Guides Tailored
Offers
...
Right time
Right channel
Right content
Riders
=

13. Key challenges in driving personalization
ü Our standard Customer 360 data model for banks + ever
growing set of standard models to probabilistically
determine key relevant interests, life situations, etc.
Challenges
• Internal data scientist are not technically
enabled to impact real communication processes
and campaign workflows (campaign targeting, ....)
• Customer 360 insights (interest, etc.) are not
accessible/democratized across entire company.
Large analytical teams needed for simple tasks.
• Limited internal experience in leveraging soft
customer behaviour using interaction data
(digital, branch, payments, ...)
ü Data scientists develop their custom algorithms (CLV,
prioritization, etc.) and easily plug them into production
using our preprepared framework and pipelines. No
engineering needed.
Our solution
üSimple user interface for customer journey
experts to explore persona insights and algorithm
results and carry them over to their marketing
automation tools

14. Why personalize experience in banking?
Data sources
Data sources
Customer 360 data
model
Customer behavioural
insights
Communication tools
Communication tools
Before (weeks to month to prepare personalized comms)
With democratized C360 (immediately even during meetings)
Scientists
Engineers
IT
Customer
journey
experts
Scientists
Engineers
IT
Campaign
managers
Faster, more
personalized
data-driven
communicatio
n/campaigns
Digital interactions
Scientists
Analysts
IT
IT
Engineers
Engineers
Campaign
experts
Campaign
experts
Campaign
managers

15. New agile organization to deliver personalized experience
Need 10+ data campaign analysts to support
CJ experts experts on a daily basis
= slowing down the comms prep, lost in
traslation etc.
Missing data engineers to support data
campaign analysts, ad hoc firefighting
= heavy time consuming....
Hard to impact communication to customers
by their models, CLV, segmentations...
Before
(weeks - month to prepare perosnalized comms)
With C360
(immediately during meetings)
Data scientists
CJ experts are self-sufficient in working
with customer data insights and think
about customer journeys
Data engineers no longer needed in to
support CJs and communication prep
processes
Easy to plug their models into User
Interface consumed by CJ experts
Customer insights
Data engineers
PO CJ CJ
ANALYST ANALYST ANALYST
ANALYST ANALYST ANALYST
ANALYST ANALYST ANALYST
SCIENTIST SCIENTIST SCIENTIST
PO CJ CJ
SCIENTIST
SCIENTISTCJ CJ
C360
Team structure

16. What data does a typical retail bank have?

17. 10 B+
Card transactions
Discoveries:
Customer spending money (taxi, luxury goods,
cinema, …) that could be invested…
Struggle:
Performance and scalability issues onprem,
necessary transaction categorization

18. 100 M+
Interactions on their or partner‘s websites
1 B+
Interactions in internetbanking
1 B+
Interactions in mobile apps
Discoveries:
Customer is interested in investments (reading our
product page, play with invest. calculator, …)
Struggle:
Billions of interaction in an unstructured
manner

19. 100 M+
Ad impressions on Publisher‘s websites
Discoveries:
Customer is interested in topics like “financial independency”,
“retirement”, … that could be used as content for our campaign
Struggle:
Hard to convince a bank to migrate all data
in the cloud, hard security restrictions, ...

20. Understanding key topics from voice, surveys, NPS…
…sentiment
Discoveries / Metric:
Are the customers satisfied with the communication/
products/bank…? What topics?
Struggle:
Many customers ready to tell us their needs, if
asked. Topic detection in semi- automatic way

21. What data a typical retail bank has?
10 B+Card transactions
& payments
100 M+
Interactions on their or partner‘s websites
1 B+
Interactions in internetbanking
1 B+
Interactions in mobile apps
100 M+
Ad impressions on Publisher‘s websites
1M +
call recordings/texts
100k +
branch meeting activitiesRule-based approach
not working
(effectively)
for 90 % of customer
base…

22. Use case for AI-based
approach

23. Solution – C360 Personalization

24. RETAIL BANKING CUSTOMER 360 DATA MODEL
Customer features
“L2”
Client attributes and micro insights 500+ (1:1, ETL, segmentations, ML/AI based calculations)
(households, product next best offers, lifestyle from transaction, web and online ad data, geo
location from transactions and digital behavior, funnel stage, call to action preferences, risk...)
Customer journeys
“L2”
Unified customer event table, forming customer journeys
Customer 360°
“L1”
+ IDs management
Customer touchpoints (branch, web, mobile banking, call centers, …)
– Who (which segment, combination of online and offline IDs)
– When (what daytime)
– Where (what channel)
– Topic (product categories, life situations, …)
– Source of interactions (customers, bankers, call centers, …)
Data sources
(internal + external)
“L0”
CRM
activities
(DWH)
Card
Transactions
- payments
(DWH)
Products
(DWH)
SAS MA -
campaigns
(DLK)
Mobile
banking
(DLK)
Web - online
store
(GA 360)
Banners –
3rd party
(Adform API)
NPS, chats,
PSD2, ATM,
SLDB, ...

25. CUSTOMER JOURNEYS
(automatically created from events in C360 data model)
Mobile banking visit Mobile.de Mobile.de 1
Finance.com - - 1
Finance.com Loan product page Loan product page 1
Lidl.cz ATM seznam 0
News.cz - 0
News.cz - - 0
MOMENTS
(read about, did more often,
purchased, …)
Learned by ML models
(supervised, word2vec, …)
AI/ML-MODELS PREDICT IMPORTANT MOMENTS
IN THE CUSTOMER JOURNEY
Discoveries:
Customers with high probability to start investing,
customers ready for upsell investments…
Struggle:
Strong need for feature store, empowering
datascients and share exploited information

26. 100+ CUSTOMER TRAITS (interests, socdemo, preferences, ...)
100+ ML-TRAINED MODELS
Discoveries:
Customers with traits regarding personal finance, retirement
planning, and correlated traits (technology, etc.)
Struggle:
Strong need for MLops, scalable machine
learning environment, ...

27. Content
Syncing to communication
tools = personalized
experience in every channels
(Apps, Push, Web, Ads, Social,
Emailing, SMS, … + including
Branches)
Democratizing the insights
(to support organization/teams)
Automating the
insights
= 360° personalized experience
4x higher engagement
2x more new investment accounts
Increased in NPS

28. Pre-build product for Modern Customer Analytics and Personalization
DATASENTICS‘ PERSONALIZATION SUITE
Runs on your cloud
platform environment
Customer 360°
data models
Persona 360° App
Publishing to
communication
tools
Customer insights lab
(ML/AI train, test, deploy)
Online-offline ID
matching

29. Feedback
Your feedback is important to us.
Don’t forget to rate
and review the sessions.