Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

Apply (Big) Data Analytics & Predictive Analytics to Business Application

2 424 vues

Publié le

This presentation described Big Data concept. Then it shows example of applications in Banking. The presenter is Dr. Tuangtong Wattarujeekrit in Big Data Analytics Day event.

Publié dans : Données & analyses
  • Hi there! Essay Help For Students | Discount 10% for your first order! - Check our website! https://vk.cc/80SakO
    Voulez-vous vraiment ?  Oui  Non
    Votre message apparaîtra ici

Apply (Big) Data Analytics & Predictive Analytics to Business Application

  1. 1. Apply (Big) Data Analytics & Predictive Analytics to Business Application Tuangthong Wattarujeekrit, Ph.D. 23/Sep/2017
  2. 2. Who am I ? Working Experience 2014-current: Senior Customer Data Analytics & Intelligence Specialist TMB Bank Public Company Limited, Bangkok, Thailand 2008-2013: Manager, Data Mining Department Total Access Communication (DTAC), Bangkok, Thailand 2006-2007: Business Analyst/System Integration Engineer (AIS Thailand and DiGis Telecommunication Malaysia projects) WEDO Consulting, Co. Ltd., Bangkok, Thailand 1996-1999: Computer Engineer, Head of System Management Control Division Hoya Glass Disk (Thailand) Ltd., Lamphun Factory, Thailand Education Doctor of Philosophy (Informatics), September, 2005 Department of Informatics, The Graduate University for Advanced Studies, Tokyo, Japan Master of Computer Engineering, October, 2002 Department of Computer Engineering, Kasetsart University, Bangkok, Thailan Bachelor of Computer Engineering, March, 1996 (First Class Honour) Department of Computer Engineering, Chiangmai University, Chiangmai, Thailand
  3. 3. Tuangthong W. (23/Sep/2017) Big Data
  4. 4. Source: IBM Tuangthong W. (23/Sep/2017) Big Data’s 4 Vs
  5. 5. Big Data = Data in high scale or high complexity Big Data = Data in different forms Video Photo Voice Natural Language Tuangthong W. (23/Sep/2017)
  6. 6. Big Data = Data in motion Time–to-Value of Data Big Data = Data in doubt Language Ambiguity Data Incomplete Data Deception Tuangthong W. (23/Sep/2017)
  7. 7. Tuangthong W. (23/Sep/2017) Source of Information “Think about your Customer Journey” Customer Input Financial Transaction Touch Point Social Media
  8. 8. Tuangthong W. (23/Sep/2017) Data Analytics “Derive Value to Business”
  9. 9. Tuangthong W. (23/Sep/2017)
  10. 10. Business Competitiveness Complex Analytics Level of Analytics What’s happening? Why’s happened? What will happen? Tuangthong W. (23/Sep/2017) What’s good decision?
  11. 11. Tuangthong W. (23/Sep/2017) Machine-Learning & Data-Mining Technique
  12. 12. I) Association Rules Discovery
  13. 13. “finds co-occurrence of products based on the transaction list of customer’s usage” Association Rules Discovery Tuangthong W. (23/Sep/2017)
  14. 14. Uses of Association Rules Tuangthong W. (23/Sep/2017)
  15. 15. II) Clustering
  16. 16. Unsupervised Leaning to separate the data items into subsets Data points in one cluster are more similar to one another Data points in separate clusters are less similar to one another Clustering/ Segmentation Tuangthong W. (23/Sep/2017)
  17. 17. B3(155M) B6(260M) B4(484M) B7(676M) B2(717M) B5(1095M) B1(1440M) B8(1088M) V6 (377B) V2 (386B) V4 (731B) V8 (738B) V7 (766B) V1 (835B) V3 (1886B) V5 (1938B) Receiver Businessman Night Life Caller Long Call Short Call New Generation Customer Segmentation ILLUSTRATIVE Uses of Clustering Tuangthong W. (23/Sep/2017)
  18. 18. III) Classification/ Prediction
  19. 19. Supervised-Learning to analyze current data & historical facts to determine patterns, then predict • Classify unknown • What might happen in the future • Predict potential opportunities Classification/ Prediction Tuangthong W. (23/Sep/2017)
  20. 20. Help to Find the Right Target! Reduce Cost of Execution/Marketing Increase Revenue/Market Share Uses of Predictive Analytics Tuangthong W. (23/Sep/2017)
  21. 21. Tuangthong W. (23/Sep/2017) Tools Centralized Data Platform HadoopEDW Analytics Solution Enterprise Analytics Platform Open source Data science Language
  22. 22. Tuangthong W. (23/Sep/2017) How we apply to our Business
  23. 23. Tuangthong W. (23/Sep/2017) Tools Algorithms Skilled Coding Idea Ask the Right Question Business Knowledge
  24. 24. X-Sell Tuangthong W. (23/Sep/2017) Start with thinking about mission • Right Product • Right Time • Right Channel Up-Sell Retention Need Revenue Growth, with maintaining Good Customer Experience Analysis to know “What Offer is Relevant to the customer?” Saving High Yield Saving Term-Deposit Bank Assurance Saving Mutual Fund Credit-Card Personal Loan
  25. 25. Tuangthong W. (23/Sep/2017) Know your customer • Who’s your customer ? • What’s your customer life-style & preference ? • What’s your customer network ? • What’s your customer location ? • What’s your customer personality ? • What’s your customer financial?
  26. 26. Tuangthong W. (23/Sep/2017) Derive your 360-view of customer “Getting Attributes or Predictable Fields to do Analytics” • Identification • Age • Gender • Income • Residence • Financial Plan Demographics • Inflow, Outflow • Outstanding • CC spending • Loan Payment • Product holding • Networking Financial Usage • Branch • ATM/ADM • Mobile Banking • Internet Banking • Web-Site, Facebook • Contact Center Channel Behavior • Preference e.g. Hang-Out place • Traveler?, Shopper? • Wealth Level • Interest Event • Event-of-Life • Personality Life-Style
  27. 27. Tuangthong W. (23/Sep/2017) Use-Case “MASS Promotion Target” Business Return Key Idea • Precision vs. Coverage Operational Possibility • Propensity Model vs. • Market Basket Analysis
  28. 28. Tuangthong W. (23/Sep/2017) Use-Case “Propensity to Buy Product” Sub-Target Key Idea • Channel • Customer Segment • Execution Segment Feature Engineering • SNA (Word-of-Mouth)
  29. 29. Tuangthong W. (23/Sep/2017) Use-Case “Propensity to Churn” Churner = Customers who stop using your service Pre-Analysis to define Target Definition Key Idea • Who you can win-back • Account Level or Customer Level
  30. 30. Tuangthong W. (23/Sep/2017) Use-Case “Behavior Segment” Product Holding Define Segment Universe Key Idea • Expected Strategic Outcome Grouping/ Re-Grouping • Direct Machine-Learning outcome need to be refined by value-chain
  31. 31. Tuangthong W. (23/Sep/2017) Additional Key • Monitoring Accuracy of output before & after • Explainable to business
  32. 32. Others' Use Case Tuangthong W. (23/Sep/2017)
  33. 33. Morgan Stanley • Real-time predictive analytics from Web-Log and DB-Log to understand who did what, how, when and what caused the market issue • Detect market freak out • Better recommendations for their investments in stocks Tuangthong W. (23/Sep/2017)
  34. 34. Bank of America • Transactional data of 50 million customers • Raise the bar from sampling-analysis to the full customer set (all channel and interaction) by using Big Data technology • Propensity to buy model to appeal offers to well-defined customer segments The largest bank in US Tuangthong W. (23/Sep/2017)
  35. 35. Commonwealth Bank • 9 millions transactions per day (40% of card transactions in Australia); 12 million account profiles • Real-time analytics to create personalized services to customers both in person and online Tuangthong W. (23/Sep/2017)
  36. 36. • Analytics customer basic profiles, their services used, their business, market trend for personalized financial advice for each customers • Less frequent that customers have to meet-up with the financial advisor Tuangthong W. (23/Sep/2017)
  37. 37. • Analysis on average spending habits of people in that demographic (such as monthly shopping, housing, communication costs) both from UBank transactions and customers’ input • [PeopleLikeU] application (which is not survey-based, but it’s real transactional data) to compare and benchmark the spending habits of different types of people Tuangthong W. (23/Sep/2017)
  38. 38. • a wide range of sources to decide loan approval • For example, whether a customer has GitHub account Tuangthong W. (23/Sep/2017)
  39. 39. • Use bulk of data other than simple credit report • For example, how borrower uses smartphones and social network • This reduces 40% default rate. Tuangthong W. (23/Sep/2017)
  40. 40. • Solve the limited traditional historical credit bureau data • 4.5 billions of “credit invisibles” • Predictive algorithms to customer scoring from some self-declared data and other reliable sources, such as LinkedIn • Be able to increase acceptance by 14% Tuangthong W. (23/Sep/2017)
  41. 41. Questions?
  42. 42. Thank You
  43. 43. Special Thanks Customer Dynamic Marketing team @TMB, especially 1. Boontarika Maythayodom 2. Tanaporn Tunyaset 3. Pornnareumol Kaewyok who make TMB case-studies to go-live.