New Best Practices in Managing Customer Information Overview

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With access to rich, real-time information, today’s consumers expect businesses to understand their unique needs in the context of their current situation. CIO’s, CDO’s and architects, however, face several challenges inhibiting them from utilizing their information assets to meet this expectation. In this session, we will share the latest practices and case studies from leading companies on their customer information management strategy helping them standout in the marketplace and get ahead of the competition.

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New Best Practices in Managing Customer Information Overview

  1. 1. New Best Practices for Managing Customer Information Navin Sharma, VP of Product Management, Pitney Bowes
  2. 2. 2 The New Age of the Smart Consumer
  3. 3. 3 What Makes Consumers Smart? “The Nexus of Forces” 1.Ubiquitous Mobile, Cloud, Social Platforms 2.Access to timely & relevant information 3.Ability to share “bad experiences” quickly thru personal networks
  4. 4. 4 What Makes A Business Smart? Business Agility The Capacity to Identify and Capture Opportunities More Quickly Than Rivals
  5. 5. 5 Internal Barriers Stall Business Agility The main obstacles to improved business responsiveness are slow decision-making, conflicting departmental goals and priorities risk- averse cultures and silo-based information.
  6. 6. 6 Adverse Impact of Information Silos Sales • Who are my top clients? • Where else do they have a relationship within the enterprise? • What is their current status – service requests, VOC surveys? Support • What is the value of the customer calling in across the enterprise? • What products do they own across the portfolio? • What was the feedback from recent VOC surveys? Marketing • What’s our current share-of-wallet across portfolios? • What opportunities exist for cross-sell/up-sell? • Which of my prospects are actual customers? Partner • Who are my top performing partners across the enterprise? • What’s a profile of an ideal partner selling a particular product? • How do we leverage their relationships and make them more effective in understanding what leverage we have with clients?
  7. 7. 7 Knowledge Graphs Power Smart Consumers Next Generation Approach
  8. 8. 8 Knowledge Graphs Should Power Smart Businesses
  9. 9. 9 Information Management Best Practice Model to the business outcome Source with trusted data & insights Consume Search Integrate Visualize
  10. 10. 10 • Rigid data models tied to RDBMS lose agility • Limited views • Business-outcome drive, white- boarding approach to modeling • Multi-dimensional views enabled via complex relationships & hierarchy management Knowledge Graphs: Intuitive & Agile
  11. 11. 11 STEP 1: Model to the Business Outcome
  12. 12. 12 STEP 2: Source Trusted Data
  13. 13. 13 • Who is a high spender? • What is their propensity to buy? • Is the customer within my pre-defined Geo-fence? • How does it influence my marketing offers? • Who is both influential in their community & a high spender? • Which products would customers prefer that others “like” them have purchased? STEP 2: And Combine it with Insights
  14. 14. 14 STEP 3: Visualize the Knowledge Graph
  15. 15. 15 STEP 3: Search the Knowledge Graph
  16. 16. 16 STEP 3: Integrate the Knowledge Graph
  17. 17. 17 Retail – Case In Point Information Silos of Traditional Approach Location/Site Hub Product Hub Customer Hub
  18. 18. 18 What Traditional Approaches Don’t ‘See’
  19. 19. 19 Extended Network of a Customer
  20. 20. 20 Discover Non-Obvious Relationships
  21. 21. 21 Determine Sphere of Influence
  22. 22. 22 Financial Services – Case In Point Payment Graph (e.g. Fraud Detection, Credit Risk, Analysis, Chargebacks…) Spend Graph (e.g. Org Drillthru, Product Recommendations, Mobile Payments, Etc.) Asset Graph (e.g. Portfolio Analytics, Risk Management, Market & Sentiment, etc.) Master Data Graph (e.g. Enterprise Collaboration, Corporate Hierarchy, Data Governance, etc.):
  23. 23. 23 Poor Data Management Blinded Chase to Madoff Fraud: WSJ by Penny Crosman JAN 8, 2014 Data locked in silos and the lack of a common customer identifier that could link accounts were to blame for JPMorgan Chase's failure to identify Bernard Madoff's massive fraud, according to an article in Wednesday's Wall Street Journal. (Madoff, who was arrested in 2008, stole about $18 billionfrom clients, sending them fake monthly statements reflecting fake trades, assuring customers they were getting high returns when in fact their money was gone.) Madoff Investment Securities maintained several linked checking and brokerage accounts at JPMorgan Chase, its primary bank, for 22 years. The bank structured and sold investment vehicles tied to the firm's purported returns. The bank has agreed to pay $2.7 billionin fines to the federal government for failing to report warning signs of Madoff's scheme. "Despite recognizing suspicious activity in its U.K. unit in 2008 — and notifying U.K. regulators that Mr. Madoff's returns were 'too good to be true' — the bank didn't notify its own U.S.-based AML staff or American authorities. AML experts say that JPMorgan's anti-fraud systems should have automatically flagged Madoff accounts across the company," the paper reports. In one of the terms of the bank's settlement, JPMorgan has agreed to continue reforms of its Bank Secrecy Act/Anti-Money Laundering compliance program. Customer data that's strewn across a company and not linked has been a problem that has plagued large banks for many years. A London division of a bank could have no idea of the Madoff Fraud: WSJ by Penny Crosman JAN 8, 2014 Data locked in silos and the lack of a common customer identifier that could link accounts were to blame for JPMorgan Chase's failure to identify Bernard Madoff's massive fraud, according to an article in Wednesday's Wall Street Journal. (Madoff, who was arrested in 2008, stole about $18 billionfrom clients, sending them fake monthly statements reflecting fake trades, assuring customers they were getting high returns when in fact their money was gone.) Madoff Investment Securities maintained several linked checking and brokerage accounts at JPMorgan Chase, its primary bank, for 22 years. The bank structured and sold investment vehicles tied to the firm's purported returns. The bank has agreed to pay $2.7 billionin fines to the federal government for failing to report warning signs of Madoff's scheme. "Despite recognizing suspicious activity in its U.K. unit in 2008 — and notifying U.K. regulators that Mr. Madoff's returns were 'too good to be true' — the bank didn't notify its own U.S.-based AML staff or American authorities. AML experts say that JPMorgan's anti-fraud systems should have automatically flagged Madoff accounts across the company," the paper reports. In one of the terms of the bank's settlement, JPMorgan has agreed to continue reforms of its Bank Secrecy Act/Anti-Money Laundering compliance program. Customer data that's strewn across a company and not linked has been a problem that has plagued large banks for many years. A London division of a bank could have no idea of the activity of a customer in New York, for example, creating fraud as well as customer service issues. Shortly before the financial crisis,several large banks appointed C-level data management chiefs (calledchief data officers) and had them start creating unified customer data warehouses in which all accounts, transactions and other activity related to a customer could be gathered in one place. Bank of the West recently completed such a project. During the financial crisis,these large, multi-year projects with an elusive ROI were put aside. Recently, with the dust settling, a few banks have been turning their attention again to customer data management. But software can only do so much. The other side to this is that in Manhattan U.S. Attorney Preet Bharara's criminal charges against JPMorgan Chase, a pattern of willful ignorance is described. Time and time again, according to the U.S. Attorney's office, the bank had strong reason to The Case for Data Governance
  24. 24. 24 • Limited to non-existent support for roles, responsibilities, and processes between the business and IT • KPIs tied to process • Monitor for trends over-time • Enable business stewardship • Embedded workflows & exception management • PII data anonymized Data Governance: In Service of the Business Process
  25. 25. 25 Information Management for Smart Businesses Knowledge Graphs are Intuitive & Agile Establish Process-centric Data Governance Businesses Can Get Smarter Just Like Consumers
  26. 26. 26 Questions?

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