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Webinar | Using Big Data and Predictive Analytics to Empower Distribution and Marketing


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With the proliferation of Big Data-oriented technology and its accompanying applications of advanced statistical techniques, asset managers are enabling their sales and marketing teams with more insight into the preferences and proclivities of their clients, both advisors and investors. This webinar will give attendees a general understanding of Big Data’s technologies and techniques especially as they pertain to using predictive analytics for more effective and targeted marketing and distribution.

Desired Outcomes:
Understanding Big Data and how it is enabling adopters to use data more effectively than in the past
Familiarity with some of the technological and analytical approaches Big Data enables
Understanding of attribution models for measuring advisor and investor responsiveness
Knowledge of how to prioritize campaigns and contacts by combining measures of valuation and responsiveness
Grasp of some of the more effective way to adopt predictive analysis for sales and marketing
Understanding basics of recommender systems and how next best action is determined

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Webinar | Using Big Data and Predictive Analytics to Empower Distribution and Marketing

  1. 1. Using Big Data and Predictive Analytics to Empower Distribution and Marketing January 13, 2016 SPONSORED BY:
  2. 2. Moderators: Mike Gilligan Assistant Vice President, Specialized Services, MFS Service Center, Inc. Rick Niedt Officer, Product Development & Strategy, DST Systems, Inc. Panelists: Daniel Cross Vice President, Applied Analytics, DST Systems, Inc. Chuck Gallant Managing Director, BNY Mellon Jerry Potts Vice President of Global Marketing, MFS Fund Distributors, Inc. Deep Srivastav Head of Business Strategy, North American Advisory Services, Franklin Templeton Investments
  3. 3. Big Data: Gaining Competitive Advantage from Your Data Assets Chuck Gallant BNY Mellon
  4. 4. Big Data Gaining Competitive Advantage from Data Assets 1. Big Data – Volume, Velocity and Variety 2. Descriptive vs. Prescriptive Analysis 3. Business Intelligence / Data Visualization
  5. 5. Investment Firm Opportunities – Where are We seeing Big Data in Our Industry… • Predictive analysis around sales and prospect data • Large scale aggregation and analysis of operational performance data metrics and attributes • Personalization of investor content and product offerings in real-time as customer accesses your site • Proactive outreach by service associates based on analyzing history of contact in real time • Leading advisors in selling customized product to clients in real time • Advanced analytics tools used to predict risk / results of investment decision scenarios • Service and selling based on multi-generational heuristics and related characteristics
  6. 6. Digital Pulse Digital Pulse is our proprietary Big Data analytics platform, enabling us to generate actionable insights to improve processes and business performance. Digital Pulse is comprised of four core pillars combined to empower working smarter through evidence-based decision making. Capture Collecting event based data 1 Store Big Data repository2 Act Delivering actionable insights 4 Analyze Consistent, meaningful analytics 3 Value to Our Client Some Examples • Improves service levels by trending SLA process steps and providing insights into activities in our Fund Accounting Process • Reduces processing times by providing usage patterns of operations and providing improvement opportunities in our Transfer Agency business • Reduces structural cost by identification of where manual effort is spent and applying automation or machine learning in Straight Through Processing • Reduces risk and increases transparency by leveraging real-time position health meter to view critical intraday Liquidity metrics Our Evidence Based Ecosystem
  7. 7. CPE CODE: 093
  8. 8. Addressing Common Challenges With Big Data & Analytics Daniel Cross DST Systems, Inc.
  9. 9. Seven Common Challenges 1. Knowing Where to Start 2. Access to Actionable Market Insights 3. Access to Integrated Internal Data 4. Segmentation of Advisors and Investors 5. Targeting of Sales & Marketing 6. Attribution & Testing of Sales & Marketing 7. Analytical Approaches to Compliance & Risk
  10. 10. Data Science Customer Engagement Domain Expertise Professional Services Product Development DATA Platform as a Service Professional Services • Data and Analytics Strategy • Data Management • Analytics • Customer Engagement Current Products • Predictive Selling for Mutual Funds • Predictive Selling for Alternatives • Retirement Intelligence • Alternative Intelligence
  11. 11. What Data is Important Shareholder and Advisor Demographics Phone Interactions & Call Activity Shareholder and Advisor Metadata Transactions, Product & Market Data Contextual Events (Media, Industry, Regulatory) Sales Interactions Web & Other Interactions
  12. 12. What Does This Allow?
  13. 13. Generating Results
  14. 14. Using Big Data for Distribution Deep Srivastav Franklin Templeton
  15. 15. Developing Big Data Analytics • Does our industry fit the big data paradigm? • How did we determine the focus of our modeling efforts- multiple models and lots of data? • How do we refine, improve and modify our models? • Strategy vs Analytics
  16. 16. Consuming Big Data Analytics • How did we blend the art and science together? – Who to contact? – When to contact? – What to talk about? • How does data get consumed- sales management and front line ? • Iterative: Pilot/Test/Learn
  17. 17. Case Study: US Retail • Started small but high impact- visits, calls, sales • Added Segments, Behavior, Client Value • Added market share, AUM, digital, more • Integrated Sales and Marketing Analytics • Constant focus on quantifying results
  18. 18. CPE CODE: 387
  19. 19. Marketing Opportunities with Data: The Good, the Bad, the Ugly Jerry Potts Vice President, MFS Fund Distributors, Inc.
  20. 20. The Wave of Big Data • Understanding critical behaviors • Generating actionable insights • Leading the organization's strategic decisions and execution • Your relationship with MFS
  21. 21. "How Did Our Data Get So Big… and So Ugly?" • Understanding critical behaviors • Generating actionable insights • Leading the organization's strategic decisions • Your relationship with MFS Big UGLY Data– • So much data, so many sources
  22. 22. • Not all data is created equal! – Clear goals, KPIs that matter • Deeper engagement = Greater Average Sales (2X) • Omni channel vision introduces many more touchpoints… and DATA • "Simple complexity"– Providing context – Existing clients- • Transactional-- Frequency, breadth, retention and share • Behavioral– Actions, activities, preferences – New clients- • Awareness Engagement First trade • Examples What Data Matters?
  23. 23. Questions?