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Bank struggles along the way for
personalization: Customer 360°
Jakub Stech, Veronika Pjescakova
Speakers
• Data science architect at DataSentics, a
European machine learning and cloud data
engineering boutique.
• Bring...
3
Make data science and machine learning
have a real impact on organizations across
the world
Bring to life transparent pr...
Speakers
• Product owner of Customer 360 at Ceska
sporitelna, one of the largest banks in Central
Europe.
• She is respons...
Česká Spořitelna
• Almost 200 years history
• 4,6 million customers
• Part of ERSTE group
• Driver of innovation in the gr...
Agenda
• Why personalization matters
• Challenges in change management
• Typical data in (retail) banking
• Solution: C360...
Personalization
Higher expecations from
customers
Personalization in Retail Banking
Financial products are
complicated
= Education?
= Personalization?
= Explanation?
*PERSONALIZATION „SEGMENT OF 1“USEFUL CONTENT
Products
Services
Addons
Hints
Articles
Guides Tailored
Offers
...
Right tim...
Key challenges in driving personalization
ü Our standard Customer 360 data model for banks + ever
growing set of standard ...
Why personalize experience in banking?
Data sources
Data sources
Customer 360 data
model
Customer behavioural
insights
Com...
New agile organization to deliver personalized experience
Need 10+ data campaign analysts to support
CJ experts experts on...
What data does a typical retail bank have?
10 B+
Card transactions
Discoveries:
Customer spending money (taxi, luxury goods,
cinema, …) that could be invested…
Strug...
100 M+
Interactions on their or partner‘s websites
1 B+
Interactions in internetbanking
1 B+
Interactions in mobile apps
D...
100 M+
Ad impressions on Publisher‘s websites
Discoveries:
Customer is interested in topics like “financial independency”,...
Understanding key topics from voice, surveys, NPS…
…sentiment
Discoveries / Metric:
Are the customers satisfied with the c...
What data a typical retail bank has?
10 B+Card transactions
& payments
100 M+
Interactions on their or partner‘s websites
...
Use case for AI-based
approach
Solution – C360 Personalization
RETAIL BANKING CUSTOMER 360 DATA MODEL
Customer features
“L2”
Client attributes and micro insights 500+ (1:1, ETL, segment...
CUSTOMER JOURNEYS
(automatically created from events in C360 data model)
Mobile banking visit Mobile.de Mobile.de 1
Financ...
100+ CUSTOMER TRAITS (interests, socdemo, preferences, ...)
100+ ML-TRAINED MODELS
Discoveries:
Customers with traits rega...
Content
Syncing to communication
tools = personalized
experience in every channels
(Apps, Push, Web, Ads, Social,
Emailing...
Pre-build product for Modern Customer Analytics and Personalization
DATASENTICS‘ PERSONALIZATION SUITE
Runs on your cloud
...
Feedback
Your feedback is important to us.
Don’t forget to rate
and review the sessions.
Bank Struggles Along the Way for the Holy Grail of Personalization: Customer 360
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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

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Bank Struggles Along the Way for the Holy Grail of Personalization: Customer 360

  1. 1. Bank struggles along the way for personalization: Customer 360° Jakub Stech, Veronika Pjescakova
  2. 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. 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. 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. 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. 6. Agenda • Why personalization matters • Challenges in change management • Typical data in (retail) banking • Solution: C360 Personalization + Struggles along the way
  7. 7. Personalization
  8. 8. Higher expecations from customers
  9. 9. Personalization in Retail Banking
  10. 10. Financial products are complicated = Education? = Personalization? = Explanation?
  11. 11. *PERSONALIZATION „SEGMENT OF 1“USEFUL CONTENT Products Services Addons Hints Articles Guides Tailored Offers ... Right time Right channel Right content Riders =
  12. 12. 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
  13. 13. 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
  14. 14. 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
  15. 15. What data does a typical retail bank have?
  16. 16. 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
  17. 17. 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
  18. 18. 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, ...
  19. 19. 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
  20. 20. 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…
  21. 21. Use case for AI-based approach
  22. 22. Solution – C360 Personalization
  23. 23. 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, ...
  24. 24. 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
  25. 25. 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, ...
  26. 26. 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
  27. 27. 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
  28. 28. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.

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

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