Analytics in Financial Services: Keynote Presentation for TDWI and NY Tech Council in New York City

Analytics in Financial Services:
A Competitive Imperative
Keynote Presentation: March 30, 2011

Architects of Fact-Based Decisions™

Presentation Outline Inaugural Event on Practical Analytics

1. Quick Intro

2. The Challenge ("The Gap")

3. A Solution to The Challenge ("Bridging the Gap")

4. Application to Your Business Models

5. From Opportunities to Results

6. Key Takeaways

Analytics in Financial Services: How Analytics and Data are Changing the Financial Industry, © Fitzgerald Analytics 2011, all rights reserved 2

Introduction Inaugural Event on Practical Analytics

Jaime Fitzgerald,
Founder @ Fitzgerald Analytics

To help clients find and unlock opportunities
Professional
Mission: via data, technology, and processes, while
always remembering the crucial role of people.
To Achieve
my Mission Easier Ways to and Better Ways to
I Focus On: Find Opportunities Unlock That Potential

Writing a book about the role of “thought-style” in the
Other information-era
Projects: Collaborating with others in Analytics, Info Mgt, Data Mgt, &
Tech Innovation via Groups like TDWI, NYTC, DAMA, AIIM…


Business Model Focus for Today Inaugural Event on Practical Analytics

1
Retail Financial Services

2
Commercial/Business Banking / FS

3
Trading

4
Investment Banking


What Led to Today’s Event: “The Gap” Between Buzz and Reality Inaugural Event on Practical Analytics

On the subject of data analytics, a gap has emerged between
buzz/hype and reality
1. So Much “Buzz” About Analytics… 2. …is not Proportional to Results

Best-selling books on Analytics(Competing Execs not yet satisfied with results
on Analytics, Supercrunchers, etc.) Many professionals still not benefitting in
Entire new business units & teams their roles and teams
(e.g. IBM Business Analytics) Lots of new tech efforts launched to meet
demand for analytics. Results vary.

3. Analytics Does Have Potential 4. But This Potential Needs to Benefit YOU
Select firms use analytics unusually well, You can’t deposit buzz into the bank!
and their profit growth shows it! For more of us to benefit, we need:
I am a “witness” for 15 years of results Simpler ways to identify opportunities
Efficient ways to plan and execute analysis
Transparent methods (less black boxes)

Our goal today is to help to bridge that gap.


Today’s Themes Inaugural Event on Practical Analytics

Based on our mission for today, we are guided by 3 themes
Key Theme Therefore We Will

1 Share a Practical Methodology ...
Explained via Real Examples…
Getting Practical
with Reference to Work of our
Panelists
2
Be explicit about how concepts
Takeaways
apply to your businesses
You Can Use
Review key takeaways at the end

3 Address both business context and
technical implications
“Bridging the Gap” Show how the method helps bridge
the gap between business goals and
technical preconditions to those goals

Defining a Few Terms I Will Use Inaugural Event on Practical Analytics

To ensure clear communication, let me define up front a few terms I
will use today.
Term Intended Meaning
1. Point of Opportunity An opportunity for improvement within YOUR
business model
Defined because it impacts key drivers of your
results

2. Causal Clarity
Clear Definition of key drivers, cause + effect in
Cause Effect your business model, business unit, etc.
Easy to Explain to others, preferably visually

3. Causal Model
A visual representation of “what drives results” in
Price
Revenue
your business model
Txns Create this, and you have achieved “Causal Clarity”



1. Quick Intro





6. Key Takeaways


The Challenge: Closing the Gap between Hype and Reality Inaugural Event on Practical Analytics

As we began to discuss… there is a “performance gap” here!
1. So Much “Buzz” about Potential of Analytics
Best-selling books on Analytics (Competing on Analytics, Supercrunchers, etc.)
New efforts (business units, teams, roles, initiatives)

2. When Analytics Works, it Really Works…
Selected firms have made analytics a source of competitive advantage
It happens every day… just not as broadly as would be ideal
1 2 3 4
Right Focus Right Method Execution Results!

3. Yet Most Teams and Organizations are Not Benefitting Enough
Lots of effort, some wins, many losses… not “firing on all cylinders”

What is the root cause of this “performance gap”?

The #1 Cause: Complexity Inaugural Event on Practical Analytics

Seeking opportunity in analytics is complex - there are “too many
options”…

1. Thousands of 2. Infinite Decisions +
Business Processes
Business Models (existing and potential)
Analytic
Initiatives
3. Many Types of 4. Lots of Tech Tools,
Analysis w/ Platforms, Plug-ins,
1000s of Sub-Concepts Deployment Options

We need a way to simplify this complexity without losing the potential

Other Root Causes of “Analytic Underperformance” Inaugural Event on Practical Analytics

While complexity is the largest impediment to results in analytics,
there are several other challenges worth mentioning…
1
Cross-Functional Collaboration

2
The good news is…
Communication Challenges
…managing complexity
3 makes all these other
Technology and problems easier to manage
Data Management Barriers also…

4
People: Talent, Skills, Learning,
Team Effectiveness



1. Quick Intro





6. Key Takeaways


Simplify Your Analytic Process via “Causal Clarity” Inaugural Event on Practical Analytics

Clearly defining “Cause and Effect” is the most crucial enabler of
analysis that is

Find
Opportunity Unlock More
Simpler Easily
Faster

More Efficient Fewer Wasted Steps

More Impactful. Benefit /Cost


“Causal Clarity” Inaugural Event on Practical Analytics

If cause and effect are clear, practical analytics becomes feasible

Optimized by Our Lead to
Drivers of Results…
Analysis of Data… Better Results

Better Revenue
Key Decisions
Decisions
Costs

Risks
Key Business Better
Processes Processes Profit

Causes Effects

Causal Models: A Simple “Base Case” Inaugural Event on Practical Analytics

Each business model has an inherent “causal model,” but the
“core branches” are similar

Example: Drivers
of Net Profit
Revenue

less
Your Has Cost of Revenue Gross Profit
Business Operating Costs
Model less Net Profit
Marketing Other Costs
Overhead

Other


The “Point of Opportunity” Concept Illustrated Inaugural Event on Practical Analytics

Has “Causal Creating
Your Business
Model” A Point of
Model
(aka Drivers) Opportunity

Returning to the causal model above on the previous slide,
let’s find a concrete point of opportunity

A Point of Opportunity Inaugural Event on Practical Analytics

Here is an opportunity to enhance ROI on Marketing + Sales efforts:

Point of Opportunity: “Efficiency of New Client Acquisition”
Key Driver / KPI: Acquisition Cost per New Client
Formula: [spending on new client marketing]/[# New Clients)

Transactions
per Client Price per Txn
X

# of Clients Volume

Sales and
Marketing


The Good News: Establishing “Causal Clarity” is Not Rocket Science Inaugural Event on Practical Analytics

Easy and Quick: There are 3 Main Steps
1 2 3

3 Things
To Goal Business Model Causal Model
Define:

Inputs Usually net profit Products / services Aka “drivers tree”
To Can be anything!: Distribution Makes the causal
Use: – Marketing ROI Target customers model visual
– Non-profit impact At what price
– Customer Cost structure
satisfaction Known KPIs and
– Etc. rationale for them



1. Quick Intro





6. Key Takeaways


Background: Types of Questions Analytics May Answer Inaugural Event on Practical Analytics

We are about to get practical, let’s keep the following in mind…

Past Present Future

What happened? What is happening What will happen?
Information now?
(Reporting) (Alerts) (Extrapolation)

What’s the
How and why What’s the next best/worst that
did it happen? best action? can happen?
Insight
(Modeling, (Recommendation) (Prediction,
experimental optimization,
design) simulation)
Source: Tom Davenport , et al. Analytics at Work: Smarter Decisions, Better Results (Harvard Business School Press, 2010)


One More Framework: Value vs. Volume Inaugural Event on Practical Analytics

In some cases, analytics makes a single high stakes decision better.
In other cases, we “make it up in volume”

High
High-Value, Low-Volume
Value Decisions

Example: M&A, capital investment,
Economic Impact
of Each Decision

strategic market positioning
Medium-Value, Medium-Volume
Decisions

Example: Product development and
pricing, customer segmentation,
targeting
Low-Value, High-Volume Decisions

Example: Loan approval, customer
cross-sell offer, customer request for
credit-line increase. Direct Marketing.
Low
Value
Low Volume Decision Volume High Volume
Source: Neil Raden and James Taylor. Smart Enough Systems (Prentice Hall 2007).

Financial Services Business Models Inaugural Event on Practical Analytics

To get practical, let’s establish causal clarity for several key business
models in Financial Services
Core Products / Key Drivers
Business Model
Services (illustrative)
Customer acquisition,
Deposit Products
1. Retail Financial retention, and profitability
Loan Products
Services Product pricing
Investment Products
Share of wallet

2. Commercial / Debt Financing
Fee structure / yields
Institutional Financial Svcs
Business Banking / (e.g. for Money Managers)
Volume
Cost efficiencies
Financial Svcs Cash Management

Proprietary trading Risk-adjusted returns
3. Trading Market-making Transaction spreads
Trade execution Cost efficiencies

Underwriting Deal flow
4. Investment
M&A Deal completion rates
Banking Other advisory services Fee structure

1. Retail Financial Services Illustrative on Practical Analytics
Inaugural Event
Example

Products

Point of Increase ROI on Marketing Spend 1. Deposits
Opportunity: BY Decreasing Acq. Cost / Customer*
Key Driver / KPI: Acquisition Cost per New Client 2. Investments

Formula: [spend on new client marketing]/
3. Loans
[# New Clients)

Products per Profit per
Client Product

Allocation of X
Marketing $
# of Clients Volume

*2nd Order Causality + Pt of Opportunity

* P = Profit per year per customer, n=number of years the customer stays

2. Commercial/Business Banking / FS Illustrative on Practical Analytics
Inaugural Event
Example

Point of Grow Fees BY Increasing “Share of
Opportunity: Wallet” from Corporate Clients
Key Driver / KPI: Share of Wallet (“SOW”)
Formula: [Total Fees from Client]/[Total Client Fees
on Products YOU offer, via ALL providers]

Customer
Experience Share of
Optimization Marketing Wallet
Customer X
Better Outreach Loyalty
via Predictive Total Size of
Analytics Client benefit Wallet
of using your
• Everything ok? platform more The size of the
• You would benefit exclusively pie we are
from product X sharing….
* “Total Client Fees includes spending on ALL companies that offer the same or similar products/services

3. Trading Illustrative on Practical Analytics
Inaugural Event
Example

Point of
Maximize Alpha!
Opportunity:
Key Driver / KPI: Risk-Adjusted Return
Formula: Alpha

Volume: #
Trading
Trade-able
Quality of Real- Profits
Opportunities
Time Decision X Less…
Models + Tools Quality:
“Cost of
Profit per
Discovery”
Opportunity
1. Accuracy of “Triggers”
2. Cost of False Positives How “Big” are Investments
3. How well do models these Trades in Finding
adjust to changed these
world? Opportunities

4. Investment Banking Illustrative on Practical Analytics
Inaugural Event
Example

Point of
Increase Profit per Employee
Opportunity:
Key Driver / KPI: Return on Human Capital (“HCROI”)
Formula: [NET Profit] / [# Employees]

Staff & Team
Profit per Gross Profit Net
Staff + Team Person-Hour Profit
Effectiveness X Less…
“Other Investments in
# Hours
Staff Performance”
1. Resource allocation
(who does what. Why?) •Cost of analysis
2. Re-use of IP: how well do we •Cost of training
re-purpose? •Cost of new systems
3. Task Value to Cost: (e.g. knowledge mgt + workflow)
how much waste?

Analytic Themes in Financial Services Inaugural Event on Practical Analytics

Before getting into specific methods, here’s a quick tour of key topics
Risk Management: always important, more now than ever
Big data / quant frontiers (especially relevant to trading)
“Recommendation engines,” other predictive models, and their
practical applications
Process optimization & automation using data, analysis, and “system-
imbedded decision models”
Customer Experience Analytics: expectations, service levels,
satisfaction, and impact on client loyalty, retention, and profitability
Marketing Analytics (esp. with the impact of multi-channel marketing)
Customer Lifetime Value (CLV) analysis and related predictive analysis
Product pricing analysis: a perennial that just became more crucial w/
new financial regulations, constraints, and customer behaviors


Analytic Methods: Top 5 “Most Promising” in Financial Services Inaugural Event on Practical Analytics

The best methods depend on your business model & “point of
opportunity”, but this list is a good place to start…
Method Source of Impact
1 “Predictive yet “Better odds” of accuracy (decision, offer, risk-assessment)
Practical” You won’t be right all the time, and you don’t have to be

2 Allocate resources to your most profitable customers
Customer
Use WITH predictive analytics to INFER WHO WILL be most
Lifetime Value profitable in the future, not just the present.

3 Maximize profit yield per product, per transaction, per dollar
Price invested…
Optimization By finding the optimal balance of price and feature parameters

4 Broad category: from deal-level risk to trade counter-party risk
Risk Mgt
2008 to 2011: remarkable changes in “parameters”

5 Use of analytic insights “in-system” to improve process
Operational
efficiency, quality, and automation
Analytics Ranges of use include staffing, workflow enhancement, etc.


Analytic Methods: Trends into the Future Inaugural Event on Practical Analytics

While several “perennial” analytic methods remain essential, the
“mix of opportunity” is changing

Risk Mgt
Customer
Potential Value

Lifetime Value Predictive
Analytics Open-Data w/
Price Proprietary
Optimization Analysis
Text Analytics

Video /
Image

“Old Standby” New / Cutting Edge



1. Quick Intro





6. Key Takeaways


What We Need to Get Practical Inaugural Event on Practical Analytics

To get practical about analytics, we need three things…

What We Need Definition

1. Causal Clarity re: Your How You Make Money
Business Model Key Drivers of Results

2. Definition of Your Points Gaps vs. Potential
of Opportunity Room for Improvement

3. A Plan to Capture the Insight You Need
Opportunity Method to Get It


Planning Your Analysis Inaugural Event on Practical Analytics

Planning starts with the goal, the “point of opportunity”

Your Point of Opportunity (Decision or Process)
Translates to

Insights or Information Required

Which drives

Analysis Methods Required to Create this Information

Allowing definition of

Required Data

And selection of the right

Tools, Platforms, Technology, People, and Processes


How We Choose Analytic Methods Inaugural Event on Practical Analytics

Selecting the right analytic method is a key success factor. Consider
the logic below…

1. Your Goals

2. Types of Info
Informs Analytic Method
you Need

Note: This is a key topic, yet covering it
fully would require more time than we
have today.
3. Information
The Practical Analytics Knowledge Portal
Available will include a “method selection template”
that helps select the right analytic method
for your specific opportunity



1. Quick Intro





6. Key Takeaways


Summary of Key Takeaways Inaugural Event on Practical Analytics

We hope you will benefit from the concepts shared today

For All 1. Begin Your 2. Define + 3. Identify 4. Define the 5. Keep
Attendees Business Agree on Points of info needed analysis as
Model the Causal Opp. to unlock simple as
Model the opp. possible…

Tips By Executives Leadership
Role: Establish “causal clarity” visually so that everyone understands
Encourage teams to use this context to prioritize and target effort
Expect recommendations to be justified by their impact on key drivers

Business Professionals Technology Professionals
Identify points of opportunity Insist upon understanding the
before investing time in analytic business context and causal logic
details of requests for analytic systems
and effort


FYI: Sources of the Methods Shared Today Inaugural Event on Practical Analytics

In my quest to “Bridge the Gap” between business results & technical
solutions, I have been “integrating” & applying the ideas of others:

1. Although the methods I shared are my own creation, I feel a huge
debt of gratitude to numerous “idea sources”, innovators, and
subject matter experts who influenced my work
2. My focus has been on:
a. Applying their ideas in new ways
b. Combining their ideas to solve problems

c. Helping my team apply the ideas to get results

If you find my method helpful, take it and use it too!


Complementary Resources You Can Access Inaugural Event on Practical Analytics

After you leave today, you may want more information…

To provide you with ongoing access to relevant knowledge & tools, we are
creating a knowledge portal re “Practical Analytics.”

To signup for access to the portal:
1. Send an Email to Subscribe@PracticalAnalyticsPortal.com
2. Text the word “analytics” to 917-846-3759

We hope that today’s event, plus the portal, will help YOU benefit as
much as possible from analytics in your career


Analytics in Financial Services: Keynote Presentation for TDWI and NY Tech Council in New York City

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Andere mochten auch

Andere mochten auch (20)

Ähnlich wie Analytics in Financial Services: Keynote Presentation for TDWI and NY Tech Council in New York City

Ähnlich wie Analytics in Financial Services: Keynote Presentation for TDWI and NY Tech Council in New York City (20)

Mehr von Fitzgerald Analytics, Inc.

Mehr von Fitzgerald Analytics, Inc. (14)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Analytics in Financial Services: Keynote Presentation for TDWI and NY Tech Council in New York City