Marketing to the Segment of One

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Presented by Dr Kingshuk Banerjee from IBM at ISS Seminar: Analytics for Enhanced Customer Experience on 9 May at Institute of Systems Science, NUS.

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Marketing to the Segment of One

  1. 1. © 2012 IBM Corporation Marketing to the Segment of One Trends and A Case Study Kingshuk Banerjee, D.Sc. Leader, Center-of-Competence, Business Analytics and Optimization IBM Global Consulting Services
  2. 2. Trends 1. Describe, Predict and Prescribe for A Specific Customer 2. Micro-segmentation, Personalization and Next Best Action 3. Big Data Leverage 4. Mining the Unstructured 5. Realtime decision-making: People, Process and Technology
  3. 3. © 2012 IBM Corporation Trend 1: Describe, Predict and Prescribe ... Business Impact Heroics Foundational Competitive Differentiating Break-away •Spreadsheets •Extracts •MDM •Data Warehouses •Data Governance •Micro-Segmentation •Pattern recognition •View Consolidation •Dashboards •Mathematical Optimization •Reinforced Learning Descriptive Customer 360 View Prescriptive Prescribe the Optimized Action Source: IBM; Davenport et al, “Analytics at Work” Predictive Predict the Behavior ... for A Specific Customer
  4. 4. © 2012 IBM Corporation Trend 2: Micro-Segmentation, Personalization, NextBestAction Driving a major Shift in Sales and Marketing Strategy .. from selling “what I have” to focusing on “what YOU need” Allocate Optimized Offer CUSTOMER Needs ENTERPRISE Objectives Who am I ? What do I need? When do I buy? Where do I buy? Whom should I offer? What should I offer? When should I offer? How should I offer? • Demographics • Purchases • Interactions • Preferences • Purchase Cycle • Propensity to Buy • Purchase Drivers • Purchase Triggers • Purchase Affinity • Activity Based • Life Event Based • Shopping Trip Types • Channels / Devices • Locations • Occasions Customer Profile Foundation • Micro- segmentation and Personalization Optimized Marketing Activities using Mathematics and Simulation • Offer Allocation based on Goal and Constraints • Offer Timing • Channel Selection
  5. 5. © 2012 IBM Corporation5 e.g. Banking Customer Multiple Manifestation of the same Individual Behavioral data - Orders - Transactions - Payment history - Usage history Descriptive data - Attributes - Characteristics - Self-declared info - (Geo)demographics Attitudinal data - Opinions - Preferences - Needs and Desires Interaction data - Email / chat transcripts - Call center notes - Web Click-streams - In person dialogues Who? What? Why?How? Consolidate Data across Lines- of-Business Analyze Predict and Prescribe Describe customer holistically .. multiple dimensions .. 360 view Care Retain Enhance Bill Collect Sell Cross Sell / Up Sell Centralize Data on Customer Interactions Across Channels Retail Client ---> Small Business - Wealth Management Trend 2 (continued): Understanding the Customer, Good Practices … must be in tandem with Societal Characteristics, Technology Adoption and Business Needs
  6. 6. © 2012 IBM Corporation This Asset focuses on Long Term Gain This asset is based on Reinforcement Learning and Constrained Markov Decision Process framework - π (s,a,r) (s) - Customer is in some "state" (his/her attributes) at any point in time (a) - Enterprise's action will move customer into another state (r) - Enterprise's goal is to take sequence of actions to guide customer's path to maximize customer's lifetime value Current marketing policy Optimized marketing policy Customer A’s path under… Bargain Hunter Repeater Loyal Customer Valuable Customer One Timer Repeater Defector Defector Repeater Loyal Customer Potentially Valuable Action A Action B Action C Action E Action D Trend 2 (continued): An IBM Research Lab Asset Next Best Action
  7. 7. © 2012 IBM Corporation Transactional & Application Data Machine Data Social Data • POS / e-commerce transactions • Call detail records • Utility meter readings • RFID tag data • Refinery sensors • Web log data • Tweets • Blogs • Social network members / actions Enterprise Content • Emails • Document images • Video archives Are you tapping into data beyond the traditional, structured sources? Trend 3: Big Data Leverage
  8. 8. © 2012 IBM Corporation Trend 4: Mining the Unstructured How IBM Watson performs Natural Language Processing in Unstructured Data?
  9. 9. © 2012 IBM Corporation Trend 5: Real-time Personalization Step 1: Customer walks into an Electronic store to window-shop for smart phone Step 2: Customer ends up buying a Smart Phone gadget Step 3: Pays by the Bank Mobile App Next Best Action (NBA) B2C Commerce Platform Step 4: Transaction event and spending location are detected by the Bank Platform Step 5: Based on past spending patterns, house-holding analytics, current location and transaction details – NBA suggests best offer for this customer from its eco-system partner, located nearby Step 6: 15% discount on a Luis Vuitton bag in an outlet located in the same plaza; offer valid for this specific customer for next 2 hours Payment Gateway A Use Case A Credit Card Company generating Next Best Offer at the time of Purchase
  10. 10. A Case Study Data-driven, Personalized Marketing for an European Bank
  11. 11. Confidential Material Not for Public Share
  12. 12. © 2012 IBM Corporation12 THANK YOU

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