Lessons From the

Front Line – Turning

Data Into Revenue

Steve Hodgson

CEO -RedPort International, LLC

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About RedPort

• As specialists in financial services, we at RedPortprovide strategic guidance and analytic solutions to banks, insurers, lenders, and Credit Unions. Our hands-on experts work with C-suite executives across the globe to build superior-performing, customer-centric financial services companies

• Through our RedPort Applied Analytics subsidiary –RedPort provides FI’s with advanced customer analytics and predictive modeling tools

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Turning Data Into Revenue

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The Data Imperative

• Sometime in

most of our

careers,

computers will be

capable of being

“smarter” than

humans

2012

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The Data Imperative

• According to PWC investment in “big data” will grow from $5B USD in 2013 to $53B in 2017 – your competitors are investing in analytics

• The cost of a gigabyte of memory fell from $300K in 1980 to $.03 per month on Amazon Cloud today (.00001% of the cost) – you can afford to manage data and perform advanced analytics

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Example: Support Vector Machine

… a support vector

machine constructs a

hyperplane or set of

hyperplanes in a high or

infinite dimensional

space…

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Practical Approach to Turning Data Into

Revenue

1. Understand your data

2. Develop a clear understanding of how you will make money with your data

3. Commit to managing with your data

4. Organize your data around your business model

5. Architect your data infrastructure in a way you can afford

6. Operationalize your data

7. Continually learn from your data

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Lesson 1 – Develop a Deep

Understanding of Your Data

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Internal Data

Sources

(Real-Life

Data

Mapping

Example)

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Data Pyramid

Foundational Data

Applied Data (Metrics)

Intuitive Data (Models)

Financial Data Customer DataOperational

DataExternal Data

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Oxford English

Dictionary – Big

Data

“data of a very

large size, typically

to the extent that its

manipulation and

management

present significant

logistical

challenges.”

Wikipedia -

Unstructured Data

“refers to information

that either does not have

a pre-defined data

model or is not

organized in a pre-

defined manner.

Unstructured

information is typically

text-heavy, but may

contain data such as

dates, numbers, and

facts as well.”

Structured Un-Structured

BIG

“Not Big”

Social Data

Weather Data

Machine Data

Call Center Data

Yelp Reviews

Satellite Images

Survey Results

POS Data

Account Data

CC Transactions

Foundational Data

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Applied Data Example: Insurance

Company Metrics

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Intuitive Data Example: Optimal Price

• Determining the

“optimal price”

metric requires

several internal

and external

sources of data

to be brought

together into a

complex model

Optimal Price

Risk of Loss

Market Pricing

CorporateStrategy

Cost of Funds

Demand Elasticity

Regulations

FICO, Modeling

Econometric Model

Rate Watch

ALM Tool

Manual Input

Rule Set

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Lesson 2 – Develop A Clear

Understanding of How You Will

Grow Revenue With Your Data

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Where’s the RoI?

• Campaign optimization

• Branch channel optimization

• Product pricing/term structure

• Channel profitability management

• Segment selection

• Cost to serve management

• Attrition management

• Fraud management

• Etc. etc. etc.

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The Use Case

• Articulates how your organization will use data to

accomplish a specific goal (i.e. make money)

• Integrates the processes, customer impact events,

modeling, reporting and learning components to

shrink the Time-Response window

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Example: Auto Lending Optimization

Use Case

A Use Case

defines how data

will be used to create value

ActiveLoan(ProfitProfile)

ApplicationStart

shoppingPayOff

SmartBanker

Pre-ApprovalExecution

GeneralAwareness

ActiveCreditTrigger

Campaign

LeadtoCRM/Other

Pre-ApprovalCampaign

AutoRecaptureCampaign

FundLoan

LeadtoCRM

ExecuteCampaign

CaptureTrigger Email/

Text/SMS

Customer FI

ReceiveApplication

XDays

ApprovedNot

FundedCampaign

SignLoanDocs

CreditBur.

ApproveLoan

LeadtoCRM/Other

MakePayments

InsuranceCo..

CreditBur.

CreditBur.

InsuranceCo..

Turnon“noautomarket”flag

Turnoff

“noautomarket”flag

MissPayment

CollectionsCampaign

NotinMarket CustomerShopping CustomerPayingOffLoanCustomerInPurchaseProcess

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Lesson 3 – Commit to Managing

With Data

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Data driven culture examples – US Navy

• Detailed planning for every air mission

• Step by step, brutally honest “No Rank” data-driven review of what went well and what didn’t

• Ability to “debrief” or analyze what happened and why was a key part of your performance evaluation

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Data Driven Culture Case Study: US

Bancorp 2001

Culture

• “Hyper” data driven

• Hired for analytical skill

sets

• “Uber accountability”

driven to low levels

• Substantial rewards tied to

data verified results

Tools

• Relationship Management

System (RMS)

• Monthly “murder board”

meetings with all decision

makers (junior to senior)

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Retention Analysis and Planning Tool

Value Attrition Overall Customer Attrition Rate 9.0% VIP Impact Rate 30%

Yearly attrited Value 90,089,253$ (assumes not helping negative CRV customers)

Digital Projects Consumer Projects

Close reason

% of CRV

Attrition

Contribution

to attrition

rate

Reactive Save

Rate

CIR Phase

1

Full CRM (less

CIR)

Preferred

Retail

Investments

into RMS

Asset

Management

Account

Lobby 2000

(SVA)

Process

Improvement Consumer Window

VIP/SIP 2000/Mystery

Shopping/ Customer

Sat studies/ KPI/ BMIP

Life Change 40% 3.6% 5.8% 0% 1% 1% 1% 0% 0% 0% 0% 2%

Fees 20% 1.8% 59.5% 0% 4% 6% 5% 5% 0% 0% 0% 16%

Achieved Goal 14% 1.3% 8.7% 0% 0% 4% 2% 0% 0% 0% 0% 2%

Service 11% 1.0% 33.6% 20% 4% 2% 0% 0% 5% 6% 5% 8%

Competition 11% 1.0% 3.0% 0% 1% 2% 0% 0% 0% 0% 0% 1%

Pricing 2% 0.2% 21.9% 0% 5% 5% 5% 5% 0% 0% 0% 5%

Other 2% 0.2% 34.1% 0% 0% 0% 0% 0% 0% 10%

100.0% 9.0%

% of Attrition Saved 2.2% 1.7% 2.6% 1.7% 1.1% 0.6% 0.7% 0.6% 5.5%

% Reduction in Attrition Rate 0.2% 0.2% 0.2% 0.2% 0.1% 0.1% 0.1% 0.0% 0.5%

Dollars Saved ######### 1,537,856$ 2,344,220$ 525,675$ 619,160$ 500,000$ 4,946,540$

CRM SVA CRM SVA PR SVA Lobby 2000 SVA Estimate Verionica SVA Guess

Service Close Reasons

% of Service

Attrition

% of CRV

Attrition

Reactive Save

Rate CIR (SVA)

Full CRM (less

CIR)

Preferred

Retail

Lobby 2000

(SVA)

Process

Improvement

Consumer

Projects

Bank Error 25% 2.8% 27.7% 22.5% 0% 0% 21% 25% 4%

Poor Service Branch 29% 3.2% 19.3% 22.5% 5% 0% 0% 0% 6% Assumes VIP at 1.5X the save rate of phone

Don't Like Changes 22% 2.4% 43.5% 22.5% 5% 5% 0% 0% 9%

Telephone Rep 12% 1.3% 33.3% 22.5% 5% 5% 0% 0% 7%

Put into wrong account 8% 0.9% 72.4% 0% 5% 5% 0% 0% 20%

Automated Phone 4% 0.4% 46.7% 0% 5% 5% 0% 0% 13%

100.0% 11.0%

% of Service Attrition Saved 0.225 19.8% 3.8% 2.3% 5.3% 6.2% 7.8%

% of CRV Attrition Saved 2.2% 0.4% 0.3% 0.6% 0.7% 0.9% 2900015.52

CRM Projects

Current Projects

Process Projects

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Auto Lending Example: Understanding

Campaign Profitability

Quarterly Pre-Approval Campaigns Over Time

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Lesson 4 – Capture and

Organize Your Data In a Way

That Supports Your Business

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The Data Model

• Data models define how data is connected to

each other and how it is processed and stored

inside the system

• The choice of a data model is critical Typically, FI’s organize data models around products

Modern FI’s are increasingly building up from the bottom –

relating accounts to CUSTOMERS first

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Simple product-centric data model

Account

Product

Balances

Fees

Transactions

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Product Centric View

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Simple customer-centric data model

Customer

Customer Segment

Account/ Policy (Fees,

Transactions, Balances)

Sales/ Services

Interactions (CRM, Web,

Teller etc.

Customer Experience

Demographics

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Customer Centric View

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Lesson 5 – Architect Your Data

Infrastructure In A Way That You

Can Afford To Manage

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“Tools” are everywhere…

…but what matters is how the tools worktogether to enable your data strategy

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Typical Data ArchitectureMember Experience

Channels

Credit Card

Mortgage

LPL

Insurance

HELOC

Core Data “Storage”

Research&

Analytics

CampaignManagement

LeadManagement

SalesManagement

Member ServiceManagement

Social Relationship Management

Member Experience Solution Capabilities

Telephony

Email

Chat

Community

Self Service

Source Systems

Enterprise SystemData and Reporting

Infrastructure

Customer Experience Solution Capabilities

Customer Experience Channels

Cash Management

Commercial Lending

Mobility

Direct MailAccounting

External Data

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Tool selection

• Tools that collect data

• Tools that store data

• Tools that analyze data

• Tools that report data

• Tools that help data scientists build models

• Tools that “do something” with data

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Tools that collect data

• ETL (Extract, Transform, Load) tools collect structured data, clean it, normalize it and transform it into formats that are useful for analytics

Rules

ETL

ODS

Analytics

Customer Financial Product Txns

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Tools that store and process data

• Traditional relational database

• Organized around the data model

• Oracle, Terradata, MySQL etc.

• New column oriented db’s, etc.

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Tools that analyze data

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Tools that report and display data

• New tools

such as

Tableau add

richness to

data

visualization

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Tools that help Data Scientists model data

• Statistical and

Machine Learning

software packages

such as SPSS or

RapidMiner

simplify preparing

data and building

complex models

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Tools that “do something” with insights

data gives us

• Marketing automation

• CRM

• Underwriting

• Fraud

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Example – Analytics Tool Box

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Once you have the tool box you need to

tradesmen to run them – Typical Skills

Skill Set Background Cost

Vice President 10 years experience in data management

$180,000

Business/Financial Analyst MBA, Accounting, etc. $66,000

Data Architect IT, Computer Science $119,000

Data Scientist Big Data, Statistics $119,000

Database Administrator Database $67,000

Typical data team will cost between $500K and $1M annuallySource: Glassdoor.com

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Summary of Tools

• There are lots of tools to choose from Many have overlapping functions

• Each will required dedicated staff and specialized skill sets to operate

• None work by themselves – financial institutions need “a box of tools” and “a bunch of carpenters” to manage data effectively

• Selecting a tool set that can be operated affordably is of critical importance

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Lesson 6 – Operationalize Your

Data

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Example: Auto Lending Optimization

Use Case

A Use Case

defines how data

will be used to create value

ActiveLoan(ProfitProfile)

ApplicationStart

shoppingPayOff

SmartBanker

Pre-ApprovalExecution

GeneralAwareness

ActiveCreditTrigger

Campaign

LeadtoCRM/Other

Pre-ApprovalCampaign

AutoRecaptureCampaign

FundLoan

LeadtoCRM

ExecuteCampaign

CaptureTrigger Email/

Text/SMS

Customer FI

ReceiveApplication

XDays

ApprovedNot

FundedCampaign

SignLoanDocs

CreditBur.

ApproveLoan

LeadtoCRM/Other

MakePayments

InsuranceCo..

CreditBur.

CreditBur.

InsuranceCo..

Turnon“noautomarket”flag

Turnoff

“noautomarket”flag

MissPayment

CollectionsCampaign

NotinMarket CustomerShopping CustomerPayingOffLoanCustomerInPurchaseProcess

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Time - Response Challenge

TIME

VA

LUE

Data Ready for Analysis

Business event

Information Delivered

Action Taken

Operationalizing data involves organizing and managing it in a way that it efficiently “Makes Us Money”

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Time - Response Challenge

DW

BI CM/CRM

TIME

VA

LUE

Data Ready for Analysis

Business event

Information Delivered

Action Taken

Capture Latency

Analysis Latency Decision Latency

“Making information more readily available is important, but making better and faster decisions based on information is what pays the bills.”

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Lesson 6 – Continually Learn

from Your Data

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OODA Loop – Agility vs. Raw Power

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Classifying Missiles – No time to wait

?

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RedPorts FI future

• In our perfect “future financial institution”, data is constantly captured, processed, decisioned and acted upon with little or no human intervention

• The FI always knows what has happened in the past, where the company is in the present, the environment around it and is able based on this to automatically “decide” what to do in the future

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Google Car –

“Predicting the Normal” Where am I?

The car processes both map and sensor information to determine where it is in the world. Our car knows what street it's on and which lane it's in. (map software, GPS, wheel encoder)

What’s around me?

Sensors help detect objects all around us. The software classifies objects based on their size, shape and movement pattern. It detects a cyclist and a pedestrian in this case. (four radars and laser range finder)

What will happen next?

The software predicts what all the objects around us might do next. It predicts that the cyclist will ride by and the pedestrian will cross the street.

What should I do?

The software then chooses a safe speed and trajectory for the car. Our car nudges away from the cyclist, then slows down to yield to the pedestrian.

“We’ve improved our software so it

can detect hundreds of distinct objects

simultaneously—pedestrians, buses, a

stop sign held up by a crossing guard,

or a cyclist making gestures that

indicate a possible turn. A self-driving

vehicle can pay attention to all of these

things in a way that a human

physically can’t—and it never gets

tired or distracted…”

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Google’s Genius

• What google has really done is taken advantage of the vast increases in computing power to INTEGRATE a number of tools into a purpose built machine that can drive itself and AUTOMATE the learning process

• This shrinks the OODA loop/ time-response function to “drivable” levels

• FI’s need to think in this paradigm – and either INTEGRATE a number of tools, or buy pre-integrated “data cores” that will allow them to AUTOMATE the analysis process and SELF LEARN from their experience

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“Predicting the Normal”

Where am I?

- How is the business doing today?

- What segments am I profitable with?

- How much money do I earn in each channel/segment/product combination?

What’s around me?

- How are my competitors pricing?

- Which markets are growing?

- Where do people like my target customers live?

What will happen next?

- What is likely to happen to the economy?

- What will competitors do if I drop prices?

- How will a customer respond to an offer?

What should I do?

- Given what I have predicted, how should I price? What markets should I focus on? What channels should I invest in? What cost structure do I need? Etc.

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So how do we get there?

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So how do we get there?

Successful credit unions in our experience…

Start small and build incrementally from the foundation up

Go after the “big easy stuff” first (and make the CFO happy)

Have the patience to…

Put a ”learning system” in place

Make constant, incremental progress

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Practical Approach to Turning Data Into

Revenue

1. Understand your data

2. Develop a clear understanding of how you will create revenue with your data

3. Commit to managing with your data

4. Organize your data around your business model

5. Architect your data infrastructure in a way you can afford

6. Operationalize your data

7. Continually learn from your data

Stephen K Hodgson

skhodgson@redportinternational.com

+1 608 354-6196

Thank you!