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[DSC Europe 22] Bank evolution to Machine Learning driven sales model - Miroslav Grbovic

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[DSC Europe 22] Bank evolution to Machine Learning driven sales model - Miroslav Grbovic

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Talk and presentation aim to share evolution journey of Banca Intesa Belgrade from sales force self-initiative to data driven sales model. CRM department was the initiator and leader in this evolution, from developing first Machine Learning algorithms in Python to nowadays when CRM campaigns based on Machine Learning algorithms are dominant source of sales in the bank. Prerequisites, challenges, solutions, milestones and results in this journey are shared highlighting key elements for the successful data driven business evolution from our first-hand experience.

Talk and presentation aim to share evolution journey of Banca Intesa Belgrade from sales force self-initiative to data driven sales model. CRM department was the initiator and leader in this evolution, from developing first Machine Learning algorithms in Python to nowadays when CRM campaigns based on Machine Learning algorithms are dominant source of sales in the bank. Prerequisites, challenges, solutions, milestones and results in this journey are shared highlighting key elements for the successful data driven business evolution from our first-hand experience.

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[DSC Europe 22] Bank evolution to Machine Learning driven sales model - Miroslav Grbovic

  1. 1. Bank evolution to Machine Learning driven sales model Miroslav Grbovic Head of CRM – Banca Intesa Belgrade Data Science Conference – Europe 2022 Belgrade, Serbia, November 14–18th 2022
  2. 2. Company initiated vs. Customer initiated sales Inbound Outbound Customer base: → Consumers: ~1.2 million → Small Business: ~120,000
  3. 3. Machine Learning based Outbound Campaigns Targeting – selection of clients for campaigns: → Binary Classification Machine Learning (ML) algorithms – propensity to purchase models (predict likelihood that a customer will purchase certain product/service) → Separate ML algorithm for each Product/Service campaign Outbound
  4. 4. ML based Outbound Campaigns – Products & Services Personal Loans (standard, pre-approved, senior, top-up...) Overdrafts External refinance (churn) prevention Credit Cards Car Loans Small Business working capital & invest loans Mobile / Online services
  5. 5. Machine/Deep Learning algorithms evaluated, used, in testing
  6. 6. Recommender engine: → identification of bank clients on public website (http cookie matching) → collection of client’s behaviour data on public website → Rating for Visited product/service web pages – 1st ML algorithm (Unsupervised ML, Clustering) → Rating for NOT Visited product/service web pages – 2nd ML algorithm → implementation of commercial campaign based on Recommender Campaigns based on client behaviour on public Website http cookie http cookie
  7. 7. Machine Learning based Inbound Campaigns Inbound Next Best Offer: → implemented for entire consumer base → almost all bank products/services included → clients Segmentation: 1st ML algorithm (Unsupervised ML, Clustering) → Targeting: 2nd ML algorithm (Multi-class Classification) → periodically updated Credit Card Account Loan Mortgage Deposit Online Mobi €
  8. 8. Machine Learning Datasets CRM system database Payment Cards transactions database Core banking system database Digital channels transactions & behaviour database Account CRM External sources (e.g. credit bureau reports...) 2017 2022 x 8 number of Features
  9. 9. Evolution to CRM Campaigns driven Loan sales 27% 31% 39% 35% 54% 54% 27% 27% 35% 32% 51% 50% 2017 2018 2019 2020 2021 2022 Jan-Oct Units [%] Volumes [%] 23% 21% 43% 59% 61% 64% 27% 25% 45% 62% 54% 57% 2017 2018 2019 2020 2021 2022 Jan-Oct Units [%] Volumes [%] CRM campaigns share in CONSUMER loan sales [%] CRM campaigns share in SMALL BUSINESS loan sales [%]
  10. 10. ML algorithms Targeting results in Outbound campaigns Customer base Campaign Control Group 7 2.5 8 Prediction Model „Lift" Personal Loans External refinance (churn) prevention Car Loans
  11. 11. ML based Outbound Campaigns – Process Model Training dataset – used to develop (Train/Fit and Verify) ML algorithm: → single observation & target period → represents customer behaviour with regards to purchases of product/service of interest (avoiding biased periods) Model Testing datasets – used to Test results of the ML algorithm: → multiple observation & target periods → encompass periods before and after training period “Black“ Period: - during this period (1 month or less) Prediction Model is implemented and predictions are made Observation Period: - is the customer behaviour data during this X months period used as an input for Prediction Model Target Event Period: - is this Y months period, during which model predicts Target event (e.g. Loan purchase) to happen 1 month 1 month 1 month 1 month 1 month 1 month 1 month 1 month
  12. 12. Key CRM roles in ML driven sales model Operational CRM: • is 2nd half of CRM (1st one is Data Science team) • Crucial role in ML campaign lifecycle management – from implementation to result analysis • Key CRM interface towards the Sales Channels, responsible for ensuring quality of CRM campaigns execution on all channels Data Science Competence Centre: • Good enough to start evolution: at least 1 person who knows ML and willing to share experience and others willing to learn • Continuous learning - exploration of new Deep / Machine Learning algorithms - academic articles, ML competition results, data science conferences ☺ ... • Continuous exploration and enrichment of ML datasets with quality data from existing and new sources New Business/Use cases: • Continuous development of new/potential Business / Use Cases – feasibility studies, testing, implementation... CRM Value Proposition - Talk & Walk: • ML driven sales - key value propositions: → Effectiveness – selling the right products (e.g. NBP) → Efficiency – selling the products right (ML Campaigns vs. Sales Force self initiative) • Convince with data, not with “opinion“ in all phases of this evolution journey (campaign results data evaluation) - from “not believing“ in ML campaigns to demanding too much from ML based campaigns • Talk in business language – in communication with other organizational units and top management
  13. 13. Other Stakeholders roles Marketing – key roles: • Ensure that more complex marketing communication - different products, channels and customer segments • Shorten marketing materials time to market - more frequent campaigns • Contribute with sales „lift“ to overall campaign results Sales – key roles: • Shift own mindset from „we know what our customers want“ to “customer behaviour is what customer wants“ • Ensure shift of Sales Force mindset and activities from self-initiated sales to CRM campaign driven sales. • Monitoring Sales Force activities on campaigns execution (from overall level to individual sales person level) IT – key roles: • Dedicate human resources for continuous support of Business requirements – enrichment of databases for ML datasets (ETL from various sources) • Validation of data quality and troubleshooting of ETL processes • Ensure primarily HW requirements for storage and processing of excessive amounts of data CRM Sales IT Marketing Top Management Top Management – key roles: • Understands, believes and supports evolution to Data driven Business • Ensure support of all organisational units and their engagement in the evolution journey towards ML driven sales model. • Evaluate and Assess ML driven sales model results
  14. 14. Key CRM challenges in journey towards ML driven sales Challenges in Early phase: → ML competence → Confidence and experience in real/commercial campaigns based od ML → Enrichment of ML datasets → Explanation how ML works (e.g. “we don’t tell ML algorithm what to do, he tells us…“) → Uneven distribution of clients in campaign among sales people → Communication of ML results - Important Features vs. Expectations of the audience (previous business rules/logic used) → Communication of campaigns results: campaign conversion rate vs. ML model lift vs. Sales lift Challenges in Mature phase: → Demand for: − too many clients in campaign based on ML − too many campaigns based on ML prediction models (resource vs. time constrains) − ML prediction models for target events without enough data for learning (e.g. sales of new products) → Data engineering, ML computational power (GPUs) → People resources (Data Scientists and Operational CRM)
  15. 15. Data driven sales model – In house vs Consultants/Outsource In-house: data science is core business – has pivotal role in modern business development data science team is business organisational unit - resource for informed business decision data science today is company’s market differentiator, in the future imperative for organizational structure → Long-term solution Consultants / Outsource: for strategy at the beginning of the journey towards data driven sales for building competence in new stages of the evolution – sharing new use cases, solution architecture and data engineering (e.g. new data sources, GPUs)... → Short-term solution (project based)
  16. 16. Final word Practices and Results shared are: → from REAL business journey → acquired in last 5 years → from corporation (not startup) in banking industry (not e.g. gaming) ☺ We believe that our example will convince some of you: → to believe that you can do it in your company → to convince others to believe the same ☺
  17. 17. Thank you!

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