lessons from the front line turning data into revenue€¦ · pricing corporate strategy cost of...
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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
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