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Using Big Data and Predictive Analytics to Empower Distribution

and Marketing

January 13, 2016

SPONSORED BY:

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Moderators:

Mike GilliganAssistant Vice President, Specialized Services, MFS Service Center, Inc.

Rick NiedtOfficer, Product Development & Strategy, DST Systems, Inc.

Panelists:

Daniel CrossVice President, Applied Analytics, DST Systems, Inc.

Chuck GallantManaging Director, BNY Mellon

Jerry PottsVice President of Global Marketing, MFS Fund Distributors, Inc.

Deep SrivastavHead of Business Strategy, North American Advisory Services, Franklin Templeton Investments

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Big Data:Gaining Competitive Advantage

from Your Data Assets

Chuck Gallant BNY Mellon

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Big DataGaining Competitive Advantage from Data Assets

1. Big Data – Volume, Velocity and Variety 2. Descriptive vs. Prescriptive Analysis3. Business Intelligence / Data Visualization

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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

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Digital PulseDigital 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.

CaptureCollecting event based data

1

StoreBig Data repository2

ActDelivering actionable insights

4

AnalyzeConsistent, 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 inour 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

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CPE CODE:

093

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Addressing Common Challenges With Big Data & Analytics

Daniel CrossDST Systems, Inc.

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Seven Common Challenges1. Knowing Where to Start2. Access to Actionable Market Insights3. Access to Integrated Internal Data4. Segmentation of Advisors and Investors5. Targeting of Sales & Marketing6. Attribution & Testing of Sales & Marketing7. Analytical Approaches to Compliance & Risk

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Data Science

Customer Engagement

Domain Expertise

ProfessionalServices

ProductDevelopment

DATA

Platform as aService

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

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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

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What Does This Allow?

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Generating Results

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Using Big Data for Distribution

Deep SrivastavFranklin Templeton

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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

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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

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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

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CPE CODE:

387

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Marketing Opportunities with Data: The Good, the Bad, the Ugly

Jerry PottsVice President, MFS Fund Distributors, Inc.

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The Wave of Big Data

• Understanding critical behaviors• Generating actionable insights• Leading the organization's strategic decisions

and execution• Your relationship with MFS

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"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

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• 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?

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Questions?

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CPE CODE:

404PLEASE SUBMIT YOUR CODES AT THE CONCLUSION OF THE WEBINAR.

YOU WILL EARN 1-CPE CREDIT IN THE MARKETING FIELD OF STUDY.QUESTIONS? EMAIL INFO@NICSA.ORG