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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Using Big Data and Predictive
Analytics to Empower Distribution
and Marketing
January 13, 2016
SPONSORED BY:

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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

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Big Data:
Gaining Competitive Advantage
from Your Data Assets
Chuck Gallant
BNY Mellon

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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

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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 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

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CPE CODE:
093

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Addressing Common Challenges
With Big Data & Analytics
Daniel Cross
DST Systems, Inc.

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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

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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

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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 Srivastav
Franklin 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 Potts
Vice 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:
404
PLEASE 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