Fun with Numbers: Applied Economics in Retail Banking
Gary W. Class
1 February 2012
Who is Wells Fargo?
Wells Fargo & Company is a diversified financial services company
providing banking, insurance, investments, mortgage, and
consumer and commercial finance through more than 9,000 stores
and 12,000 ATMs and the Internet across North America and
internationally.
–
One in three households in America does business with Wells
Fargo.
–
Wells Fargo has $1.3 trillion in assets and more than 270,000
team members across our 80+ businesses.
–
We first in market value of our stock among our U.S. peers as of
December 31st, 2011.
Our vision: “We want to satisfy all our customers’
financial needs and help them succeed financially.”
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Who is Gary Class & why should I listen to him?
ACADEMIC:
B.A. cum laude, University of Pennsylvania, 1982
Major: English & Economics
M.B.A. Haas School, University of California, Berkeley, 1988
Concentration: Finance
PROFESSIONAL:
Senior Vice President
Internet Strategy
Wells Fargo Bank
COMMUNITY:
Chair, Parks & Recreation Commission, City of Albany, California
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Who is Gary Class and why should I listen to him ?
Developed at Wells Fargo:
 Branch
 Teller
& ATM Site Planning Models
Staffing & Scheduling Tools
 Online
Banking Customer Data Warehouse
 Customer
Behavior Predictive Models
 Customer
Behavior Across Multiple Channels
Analytics to make Anytime, Anywhere Banking Happen
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Retail Banking Defined
Retail Financial Services involves families and small
businesses.
Organizations which can participate directly in
securities markets (businesses, governments, institutions)
are by definition excluded and are the province of
“wholesale or investment banking.”
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Functions of Retail Financial Services
Payments. The financial system must provide a mechanism for the
transfer of money and payments for goods and services.
Risk Management. mechanisms to mitigate the financial risks faced by
consumers, notably via insurance products.
Borrowing—advancing funds from the future to today. The function
of household credit encompasses short-term unsecured borrowing,
longer-term unsecured borrowing and secured borrowing.
Saving / Investing—advancing funds from today until a later date.
These products vary based on the intended time horizon, level and
type of risk borne by the investor, tax treatment, and other factors.
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Functions of Retail Financial Services
…are facilitated by Quantitative Models that leverage information technology
Payments.
Extensive fraud risk prevention & mitigation models, most notably “Falcon” from FICO.
Borrowing—advancing funds from the future to today.
Centralized Credit Bureau data & Credit Risk Score-cards (e.g. the “FICO Score”).
Saving / Investing—advancing funds from today until a later date.
Application of Modern Portfolio Management (mean-variance analysis and the Fama-French
extension thereto).
Risk Management.
A critical aspect of the traditional deposit banking system is “delegated monitoring” where
banks can gain incremental insight into a customers’ credit risk by carefully evaluating the
customers’ usage of deposit accounts.
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Applied Economics in Practice
A. Identify a pressing Strategic Issue
and where the issue is amenable to a systematic solution
B. Identify the appropriate analytical approach (i.e. Model) to address
the Issue
C. Specify, estimate & validate the Model
D. Build Decision Support Tools based on the Model
E. Distribute the Decision Support Tools for use “in the field”
Key External Resources to Leverage for
(B ) & (C) Academia
(D) & (E) Consulting Firms & Technology Vendors
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Applied Economics in Practice: Experimental Design
PROBLEM:
Unlike in the natural sciences, there really are no repeatable,
controlled experiments in the social sciences – including
economics.
In the business world, customer behavior is influenced by a host of
factors exogenous to the direct relationship of the firm with the
customer: macro-economic factors, competitive dynamics, the
“social & cultural” calendar.
SOLUTION:
One best practice is to leverage “natural experiments” of policy
changes or product introduction & use behavioral models as
“controls” for confounding factors.
Jim Manzi, founder of Applied Predictive Technologies
Dennis Campbell @ Harvard University
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Behavioral Model: Customer Attrition
PROBLEM: Customers defect from the bank, taking their current & potential
revenue stream with them.
SOLUTION: Develop a model to identify the factors associated with
retention and assess the risk that a customer will “attrite” (i.e. close all
of their accounts with the bank) in the forthcoming six months.
APPLICATION:
Tactical = Intervention & Outreach
Strategic = Motivates the development of products & services that
promote customer satisfaction & thereby maximize the “switching cost”
for the customer to defect to another bank.
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Customer Attrition Model : Key Factors
Relationship with the Bank:
What accounts do I have and how long have I had them?
Product Holding
Set of products held with the bank
Bank Tenure
length of relationship
Checking Account Activity:
How actively am I using my checking account?
Checking Account Balance …intervals, a proxy for “primary bank relationship”
Service Channel Behavior:
How do I like to do my banking?
Channel Activity
…identifies willingness of customers to transact outside of branches
Online Activity Segments
…based on “functionality” and frequency of usage
Demographics & Location:
Who am I & where do I live?
Customer Type
Geography
Retail or Mixed (i.e. owns business accounts, also)
Based on customers’ residence, strength of Branch & ATM network
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Logistic Regression Econometric Model
Objective:
Score each customer based on the likelihood to discontinue banking relationship with Wells Fargo.
Method:
Perform univariate analysis and develop segmentation on selected customer attributes.
Apply logistic procedure to estimated a binary choice model for whether or not “attrited”
Yes
Attrited by 6 month?
No
e  Zi
P( Attrited )i 
1  e  Zi
–Where Pi is the attrition probability for customer i.
–  is the intercept parameter.
– Zi is a vector of explanatory or independent variables for customer i.
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Model Performance Diagnostics
There is a well-defined methodology to assess the performance of logistic
regression models.
I. Classification Table
Over-arching consideration is the ability of the model to predict the behavior of
interest (in this case, customer attrition) in the population. The framework for
evaluation is a “truth table” originally developed in the pharmaceutical domain.
Accuracy is a key performance statistic -- the higher the better. Obviously, no model
is perfect. One goal is to balance the occurrence of “false positives” with that of
“false negatives”. This is addressed by the performance metrics of “sensitivity” and
“specificity”; the key consideration is that the two values are balanced.
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Model Performance Diagnostics
I. Classification Table
At a specific Probability Level Pcutoff , customers can be classified as ‘Attrition =1’ or
‘Retention =1’ and can be compared with the actual values whether the customer is attrited
or retained.
Predicted
Actual
Attrition=1
Attrition=0
(Retention)
Attrited
True
Positive
False
Negative
Retained
False
Positive
True
Negative
Predicted
Predicted
Positive
Negative
Actual
Positive
Actual
Negative
Accuracy:
(true positives and negatives) / (total class)
Error Rate:
(false positives and negatives) / (total class)
Sensitivity:
(true positives) / (total actual positives)
Specificity:
(true negatives) / (total actual negatives)
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Model Performance Diagnostics
II. Dispersion (Gains Chart)
A key performance consideration is the ability of the model to discriminate the
behavior of interest (in this case, customer attrition) and separate those likely to
exhibit the behavior in the background population from those who are not.
A popular way to visualize this is the Gains Chart which sorts the population into
deciles and reports the ability of the model to identify the behavior of interest.
III. Stability
How well does the model deal with new sets of input data? Is the model stable
over time? One method is to compare the Gains Chart of Forecast Validation
dataset with the Gains Chart for the Estimation dataset.
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Model Performance Diagnostics:
Gains Chart
Customer Attrition, Predicted vs. Actual
Model Score “Ventiles” grouped into Likelihood-to-Attrite Segments
Predicted
HIGH
MODERATE
Actual
LOW
VERY LOW
VERY HIGH
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3
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5
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Ventiles
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Applied Economics in Practice
Case Studies:
Customer Satisfaction Measurement: Linkage of Behavioral &
Attitudinal Data
Branch & ATM Location Site Selection
Operations: Branch Teller Staffing & Scheduling
Database Marketing: Harvesting the proliferation of internet data to
improve leads for Branch Bankers
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Linkage of Behavioral Data & Attitudinal Data: Goals
The overall goal is to identify the incremental ability of customer
attitudes, as measured by market research surveys, to enhance
the models developed from Behavioral Data.
First, we need to identify the salient question* in the customer
satisfaction survey to use in the analysis.
Next, we need to isolate the extent to which Customer Attitudes
measured by the survey are actually related to future Customer
Behaviors**.
*via Factor Analysis, a statistical data reduction technique used to explain variability among observed random
variables in terms of fewer unobserved random variables call “factors”. The observed variables are modeled as
linear combinations of the factors, plus “error” terms.
** Vikas Mittal & Wagner Kamakura
“Satisfaction, Repurchase Intent and Repurchase Behavior:
Investigating the Moderating Effects of Customer Characteristics”,
Journal of Marketing Research, Feb. 2001.
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Attitudinal Data from Customer Satisfaction Survey
A representative sample of Customers were asked, on
a monthly basis, via email:
Q1: How satisfied are you with Wells Fargo?
Five point scale, where
1=Not Satisfied
and
5=Extremely Satisfied
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Linkage of Behavioral Data & Attitudinal Data: Findings
Actual Customer Retention and self-reported Customer Satisfaction
are higher where the Predicted Risk of Customer Attrition is lower…
Mean
Response to
Q1: How
Satisfied are
you with
Wells Fargo?
Actual
Customer
Retention %
4.37
4.23
4.13
4.10
3.78
Very High
High
Moderate
Low
Very Low
Predicted Risk of Customer Attrition
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Applied Economics in Practice
Case Studies:
Customer Satisfaction Measurement: Linkage of Behavioral &
Attitudinal Data
Branch & ATM Location Site Selection
Operations: Branch Teller Staffing & Scheduling
Database Marketing: Harvesting the proliferation of internet data to
improve leads for Branch Bankers
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Wells Fargo 4th & Brannan Branch in San Francisco is
co-located with a Starbucks
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Branch & ATM Location Site Selection
PROBLEM: What is the optimal distribution network (branches, ATMs,
etc.) to cultivate existing customers & acquire new ones?
SOLUTION:
Financial Modeling
Economics of the discreet bank branch location, focused on customer
“patronage”
Strategic Marketing
What markets to serve, what street-corners to be on & what customers will
visit the location?
Real Estate
What is the marketplace value of this unique location?
Avijit Ghosh @ University of Illinois
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Branch & ATM Location Site Selection
PROBLEM: How can we dimension household banking behavioral
“spatially”
SOLUTION: Develop & describe customer branch & ATM data as a
“visitation matrix”.
This allowed assignment of customers to individual branch
locations via “patronage” and the delineation of “empirical trade
areas” for individual branches allowing a precise estimation of
local product demand.
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Customer Behavior & Branch Empirical Trade Area
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ATM Locations: How do they provide value to Wells Fargo?
the incremental customer
satisfaction stemming from
convenient cash access points.
the fee revenue that WFC earns
when other banks’ customers
pay to use WFC ATMs.
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ATM Locations: Identification of Demand
“Clump Analysis” Provides a Visual Indication of Unmet demand for ATM Withdrawals
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Custo mers at Competito r Sites
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100 - 233 sales
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234 - 488 sales
489 - 1532 sales
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Comp any S to res
13 - 12819 sales
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12820 - 26197 sales
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26198 - 68882 sales
27
Applied Economics in Practice
Case Studies:
Customer Satisfaction Measurement: Linkage of Behavioral &
Attitudinal Data
Branch & ATM Location Site Selection
Operations: Branch Teller Staffing & Scheduling
Database Marketing: Harvesting the proliferation of internet data to
improve leads for Branch Bankers
28
Operations: Branch Teller Staffing & Scheduling
PROBLEM: Mis-match between the
availability of tellers and customer
demand for teller services.
This is a “dead-weight loss” as idle tellers
waste the bank’s resources and waiting
customers waste customers’ patience.
Direct Cost of
Teller Time
Indirect Benefit of
Customer
Retention via
Quality Customer
Service
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Operations: Branch Teller Staffing & Scheduling
SOLUTION:
Forecast the customer demand for teller services for each branch
each day for each half-hour using historical data.
Leverage queue-ing models to determine the level of teller staffing
required to meet the forecast demand, with acceptable customer
wait times.
Develop software for branch managers to “drag & drop” branch
employees on their roster into a schedule sufficient to meet the
forecast demand and minimize customer wait time.
Ali Kiran founder of KCG Consulting
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Staffing & Scheduling Model Components
Forecasting: start with forecasting the three building blocks:
* arrival rates,
* service time and
* customers' (im)patience, combining the first two to create forecast of
the offered-load (or workload).
Staffing: Identify the costs constraints acceptable level of service; then
apply queue-ing models, subject to these constraints, in order to
design half-hourly staffing levels.
Shifts: Integer programs, or combinatorial optimization, is then used to
aggregate hourly demand into shifts. The output is a shift-schedule,
specifying how many agents should available to occupy each shift
Rostering: Out of the body of workers, who should come to work and
when?
Source: Avi Mandelbaum, “Service Engineering: Data-Based Science & Teaching in support of Service
Management. Technion, Haifa, Israel, 2006.
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Queue-ing Models: Erlang-A
The most widely adopted methodology for staffing models is the algorithm
developed by Erlang, which require the identification of four key
parameters.
Source: Avi Mandelbaum, “Service Engineering: Data-Based Science & Teaching in support of Service
Management. Technion, Haifa, Israel, 2006.
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Branch Teller Staffing: Impatience & Abandonment
To dimension “abandonment”, an R&D effort included experimentation
with software algorithm that converts video to anonymous branch visitor
tracking data in real-time
Ralph Crabtree & Iain Currie, founders of Brickstream
33
Applied Economics in Practice
Case Studies:
Customer Satisfaction Measurement: Linkage of Behavioral &
Attitudinal Data
Branch & ATM Location Site Selection
Operations: Branch Teller Staffing & Scheduling
Database Marketing: Harvesting the proliferation of internet data to
improve leads for Branch Bankers
34
Database Marketing: Harvesting the proliferation of
internet data to improve leads for Branch Bankers
BACKGROUND:
In the 1990s, the focus was on leveraging customer profile and account
activity to develop models to predict customers’ likelihood to purchase
another product and responsiveness to direct marketing.
PROBLEM:
The deployment of internet banking applications generated a humongous
amount of customer interaction data -- how could we make sense of it &
use it to identify leads for branch bankers?
SOLUTION:
Leverage Data warehousing technology to join Customer Profiles, Model
scores with granular “secure banking application” & “public site”
navigation.
Steve Krause, founder of Personify
Dirk van den Poel @ Ghent University
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Take the customers’ web browsing
& online banking activity
…find the customers whose
behavior indicates a sales lead
and distribute the leads to
bankers in the branch
Peter Heffring, founder of Ceres Marketing Systems
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“Drowning in Data”
PROBLEM: The emergence of the internet has contributed to a
proliferation of very complex data ripe for analysis
* Structured Data (Administrative & Accounting)
* Semi-Structured Data (system logs & weblogs)
* Unstructured Data (Text, Speech, Image)
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“Drowning in Data”, a partial solution
In order for the bank to leverage & act upon semi- or unstructured
data, it is convenient to transform it into relational data by:
Customer-ization = explicitly linking the activity to a known customer
Session-ization = creating logical groupings of the stream of interactions
by user_agent & time_stamp
Note: a special case of session-ization are banker-to-customer interactions
where the data is modeled to approximate the customer’s view of the
interaction, i.e. a “sojourn”.
This approach yields cross-sectional, longitudinal time-series data by
unique customer opening the door to the next generation of
quantitative methods in applied economics, notably Behavioral
Economics.
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What’s Hot: The emergence of Behavioral Economics
Behavioral Economics integrates economics and psychology
The discipline focuses on:
– 1. Identifying the ways in which behavior differs from the traditional
model of economics
– 2. Showing how this behavior matters in economic contexts
It seeks to explain why people don’t always make rational decisions
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What is Behavioral Finance?
Leverages Behavioral Economics
…the introduction of Psychological methods in Laboratory
Experiments of Decision Making and increased awareness of
“cognitive bias” that challenges the “rational decision maker” of
neo-classical economics
Daniel Kahneman and Amos Tversky
It is a branch of Household Finance Economics
How do individuals / families make financial decisions as
compared to Institutions?
A key insight that Household Finance decisions are innately more
complex than those in Corporate Finance
A great overview was presented in John Y. Campbell’s
presidential address to the American Finance Association on
January 7, 2006.
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What’s Hot in Marketing Science:
PROBLEM: In “contractual” settings like Cable providers or retail banks, customers hold a
portfolio of services in a very complex way.
POTENTIAL SOLUTION: Leverage “Dynamic Hidden Markov Model” to identify the latent
relationship step through which customers “evolve”, allowing a deeper understanding of
“cross-sell” & “attrition”.
David Schweidel @ University of Wisconsin
PROBLEM: The social relationships that customers of service providers have with one
another impact their relationship with the firm.
POTENTIAL SOLUTION: The author leveraged data on communication among one million
customers of a cellular company to create a large-scale social system composed of
customers' individual social networks. Analytical technique: Graph Theory
Barak Libai @ Tel Aviv University
PROBLEM: Identifying which anonymous prospects on a website are likely to progress to
“conversion” in the “shopping cart”.
POTENTIAL SOLUTION: Apply advanced statistical techniques to capture the “evolving
visit behavior in click-stream data”.
Pete Fader @ U. of Pennsylvania & Wendy Moe @ U. of Maryland
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What’s Next: Combining contextual and analytical approaches provide
a more complete picture of how customers interact with the firm
BEHAVIORAL
ANALYTICS
Ethnography
• Real people
• Real behavior
• Everyday situations
• Observation over time
• Narrative Stories
• Numeric Patterns
• Patterns / themes
• Statistical Significance
• Experiential relevance
• Ability to model and predict
Both approaches privilege observation
and understanding what people
actually do and look for opportunities to
fix, improve and innovate.
Robin Beers, founder of Business is Human
Wagner Kamakura @ Duke University
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Applied Economics in Retail Banking: Looking Forward
John Y. Campbell @ Harvard University
In 1932, John Maynard Keynes wrote that he looked forward to a
distant future when economists would be “thought of as humble,
competent people, on a level with dentists.”
Today, dentists spend much of their time delivering advice and easyto-use products that promote oral hygiene.
Economists (and bankers!) for their part can deliver, or at least
design, advice and innovations that promote financial hygiene.
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Fun with Numbers: Applied Economics in Retail Banking
Gary W. Class
1 February 2012