2001 Systems Engineering Capstone Conference • University of Virginia
AUTOMATED CALL CENTER ANALYSIS AND MODELING
Student Team: Ryan Bower, Matt Burkett, Melaina Jobs, Brian Marr
Faculty Advisor: William T. Scherer
Associate Professor, Department of Systems Engineering
Graduate Advisor: Thomas Pomroy
Department of Systems Engineering
Client Advisors: Douglas Fuller
Market Management Group, First Select Corporation
Pleasanton, CA
[email protected]
Jeff Williamson
Market Management Group, First Select Corporation
Pleasanton, CA
[email protected]
KEYWORDS: Right Party Contact, Refusal to Pay,
Promise to Pay, forecasting, prioritization
ABSTRACT
A need to maintain an advanced account
prioritization methods is recognized within the debt
collections industry. In 1990, collectors averaged
about a 10% recovery rate on charged off credit card
accounts. Since then, technological advances have
enabled collections agencies to create better
prioritization techniques enabling the average
recovery rate to increase by 6%. These new
prioritization techniques enable collectors to create a
dynamic account list based on the probability of
receiving a payment.
In order to stay competitive, First Select
Corporation (FSC) needs to improve its current call
strategy. The First Select Capstone team was created
in order to create a more efficient and effective call
strategy. In order to accomplish this task, the
Capstone team developed two models: a call
forecasting tool and an account prioritization scheme.
The Capstone team formulated the models using
current dialer data provided by the FSC call center.
INTRODUCTION
The First Select Corporation (FSC) is a
subsidiary of Providian Financial, specializing in
collecting of distressed consumer debt accounts. The
Corporation buys distressed debt, usually delinquent
credit card accounts, at a percentage of the collectable
value. FSC’s main means of collecting these debts is
through the use of an automated call center—essentially a
dialer that automatically calls numbers from a list. First
Select uses this dialer to link answered calls with
representatives to start the collections process.
As soon as the dialer recognizes that the phone on the
other end of the line is picked up, it assigns the call to a
customer service representative, who then talks to the
customer and hopefully gets the customer to agree to pay
all or part of the debt. When the representative can
confirm that they are speaking with the owner of the
account (or a spouse who is legally allowed to make
decisions concerning the spouse’s finances), this is
referred to as a Right Party Connect, or RPC. If the
account holder agrees to pay all or part of his/her debt,
this is referred to as a Promise To Pay, or PTP. If the
account holder refuses to pay all or part of his/her debt,
this is referred to as a Refusal to Pay, or RTP. The goal of
every phone call is an RPC. The goal of the customer
service reps is to get a PTP from the account holder once
an RPC is established. Many factors contribute to the
dialer not getting an RPC from the accounts it dials.
Some calls end up reaching answering machines; some
end up reaching the wrong party. Sometimes the account
holder isn’t home and sometimes the account holder has
moved.
CAPSTONE PROJECT OVERVIEW
The First Select Capstone team has analyzed two
distinct areas of call center activity in order to design an
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Automated Call Center Analysis and Modeling
effective call strategy for the First Select
Corporation—forecast the inbound call volume and
prioritize accounts that will be called in collection
efforts. The first area—inbound call forecasting—
predicts the number of inbound calls on an hourly
basis. The prioritization model arranges accounts
based on probability of contact and value of account.
The prioritization model also takes into account the
number of expected inbound calls and only makes as
many outbound calls as can be handled by the
available operators.
The First Select Capstone team has developed a
robust model to create a prioritization scheme for
First Select Corporation’s accounts. This model will
be composed of three parts: Right Party Contact
(RPC) Prediction Modeling, Call History Modeling,
and Value Prediction. These three parts will work in
conjunction with each other to prioritize the call list
for First Select’s dialer.
the account holder. The RPC Prediction Model
incorporates the significance of an account receiving a
RPC using selected variables on historical data. The
chosen variables are used to calculate the probability a
current account will receive a RPC during its time on the
dialer.
The Call History Model analysis will use the RPC
Prediction Model to generate a vector of probabilities of
reaching that account based on time of day and day of
week. This model will be important in deciding when best
to call accounts.
The Value Model will assign each account to a value
bin, which will indicate the monetary amount that account
is expected to pay over a certain time period. The value
forecasted will be used in conjunction with the probability
vector that the RPC model to rank each accounts for the
dialer during a certain time band.
These three models create an optimal system to
prioritize First Select’s accounts. Therefore, First Select
will be able debt in a more efficient manner.
INBOUND CALL FORECASTING
A method of accurately forecasting the volume of
inbound calls the call center will receive provides a
valuable piece in developing a comprehensive call
strategy. An accurate prediction of inbound call volume
allows the outbound call strategy to be structured so that
sufficient staff is available to handle inbound calls while
maintaining an acceptable level of call abandonment (an
instance when a caller hangs up before speaking with an
operator). More outbound calls are made when a low
level of inbound calls are expected, and vice versa.
The forecasting model needed to be dynamic so that
it could take data from the morning and forecast afternoon
Figure 1: In the new system, inbound calls
are predicted and outbound calls are
prioritized for discrete time intervals.
For most accounts, the debtor or account holder
must be contacted for FSC to be able to collect
money. No matter the outcome of the call, the first
contact shows the debtor that First Select means
business and will go to great lengths to collect the
money. Thus, it is a high priority to be able to reach
Figure 2: The volume of inbound calls over a
two-week period that the FSC call center received.
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2001 Systems Engineering Capstone Conference • University of Virginia
call volume. Applying this design approach would
allow the call strategy to be altered as the expected
inbound climate changes.
The call center morning of operation begins at
9:00 a.m. Each hour between 9:00 and 12:00 p.m. is
divided into 15 minute time periods creating 12 time
bands. Using the results of the morning, the
forecasting model attempts to predict the afternoon
call volume for the remainder of the business day in
one-hour time periods.
The model uses a case-based methodology as its
basis. This type of model takes historical data from
the past year and compares the historic morning
inbound call volumes with the current day’s morning
inbound volume. An error calculation similar to
MSE, mean square error, is made as a measure of
closeness between the current day and all historic
days. The model offers several methods to use the
measure of closeness to make a prediction: the
nearest neighbor, k-nearest neighbors, and neighbors
within a threshold. First, the nearest neighbor
method takes the nearest historical day to the current
day based on the measure of closeness and uses its
afternoon volume per interval as its prediction.
Second, the k-nearest neighbors method allows a user
to choose a number, k. Based on the chosen k value,
the closest k days to the current day are selected, and
the forecast value is the average of these k afternoon
values for each one-hour time period. Finally, the
threshold method allows a user to enter an error
value. All of the cases that have measures of
closeness equal or less than the error value are
selected. The average of the afternoon time band
values of the selected cases are the forecasted values
for each time period.
selected credit bureau attributes were logistically
regressed against the event of a RPC. From the
regression, those attributes that most accurately predicted
a RPC were identified and then used in the RPC
prediction model.
The RPC prediction model uses historical data on the
regressed attributes in a comparison with current data to
compute the probability of an account receiving a RPC.
The case base model enables the k nearest neighbors of an
account to be generated from which the probability the
current account will receive a RPC while on the dialer is
calculated.
CALL HISTORY MODEL
In order to analyze past calls, the time and the results
of those calls must be tracked. The first task involved
dividing the continuous time spectrum into discrete
intervals, using time bands to distinguish various times
throughout the week. The time bands represent distinct
periods during the week that had similar properties. In
addition to analyzing the time of previous call, the system
must classify and analyze the results of those calls. These
results can be grouped into two different categories—
categories are RPC and no RPC.
Individual Account Value
RPC PREDICTION MODEL
At the time of purchase, limited information is
available on accounts. FSC, thus, has no way to
determine which accounts will perform better on the
dialer and should be given a higher priority in the
account list. However, if a relationship exists
between the a priori information of an account and
its dialer performance, specifically the occurrence of
a Right Party Contact (RPC), then accounts could be
more accurately prioritized from the get go.
To determine if a relationship exists between the
static information known about the account at the
time of purchase and the occurrence of a RPC,
Figure 3: The three components that affect the
probability of a hit for each attempt on each
account.
To begin using the information attained from the call
history, three different estimations had to be developed.
First, the probability of an RPC on the very first attempt
had to be calculated. The decay rate of the probability of
an RPC also had to be estimated to indicate how much the
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Automated Call Center Analysis and Modeling
likelihood of reaching the account drops with
additional calls. Finally, the history of success in
each time band had to be used to generate knowledge
about future probabilities and create a better estimate
of exactly when to call.
VALUE MODEL
The Value Model assigns each account to a
“value” bin, which indicates the monetary amount
expected to be received from the account over the
next 6 months. The value that the model forecasts for
the account is used in conjunction with the
probability vector from the Call History model and
the nearest neighbors produced from the RPC
prediction model. The value model allows First
Select to first call the accounts from which it can
expect the highest monetary return.
Debt collection is a dynamic industry that requires
dynamic processes. The First Select Capstone team has
investigated numerous aspects of the call center’s current
strategy in an effort to correct inefficiencies and
maximize revenue. In order to validate the recommended
strategy, the Capstone group conducted a comparison of
the money received between the results of the Capstone
group’s strategy and First Select’s current collection
results. The preliminary results show that the Capstone
strategy receives money more quickly than First Select’s
current strategy. This confirms that Capstone group’s
strategy improves the identification and prioritization of
valuable and contactable accounts.
The number of bins to which an account can be
assigned has varied throughout the design phase. A
high number of bins is too specific resulting in an
unacceptably high degree of error. The model design
was refined to incorporate three value bins: Low,
Medium, and High. Most accounts fall into the Low
value bin, putting the ability to know the highest
value accounts at a premium.
The First Select Capstone team tried two
methods of forecasting to predict the value of
accounts. The first consisted of conducting linear
regression on a number of variables including
second-order variables and interactions from previous
payment data. However, this method did not produce
results accurate enough to be used. Further analysis
was conducted using population segmentation to
determine whether the accounts could be placed in
certain bins, or populations, by shared characteristics.
This analysis met with more success. The most
significant attributes were the number of Right Party
Connects to that account and the balance the account
had when it became a First Select account.
Figure 4: This graph shows the
cumulative amount collected from the
accounts, as ordered in the ways dictated
by the various models. Notice that the
final model prioritizes the accounts in
such a way as to get the most amount of
money quickly.
REFERENCES
Gibson, Kate. “Who’s Who in Credit Card Collections?”
Collections & Credit Risk. October 2000. 29
October 2000
<http://web.lexis-nexis.com/universe>
Testing the value model included measuring the
percent error of predicting each individual account’s
value, the binning accuracy, and finally an analysis of
the errors in binning accuracy. For example, if the
model incorrectly classifies a low value account, does
it classify it as a medium value or a high value
account?
Lucas, Peter. “Why Recoveries are on the Rise; Scoring
Models and Databases are Helping Collectors Boost
Recovery Rates.” Collections & Recovery. Vol 13,
No 7. October 2000. 22 October 2000.
<http://web.lexis-nexis.com/universe>.
CONCLUSION
Ryan Bower is a fourth year Systems Engineering and
Economics double major from Somerset, Pennsylvania.
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BIOGRAPHIES
2001 Systems Engineering Capstone Conference • University of Virginia
When Ryan is not creating fun templates and
graphics for the Capstone team or working on the call
history models, you can find him tearing up the
nearest ski slope. Next year, Ryan will be working
for RoyalBlue Financial in New York, NY.
Matt Burkett is a fourth year Systems Engineering
Major from Scott Depot, West Virginia. When he is
not predicting inbound call volumes or valuing
accounts, Matt can be found celebrating his Irish
heritage. Matt will begin working for Accenture
Consulting this summer in August.
Melaina Jobs is a fourth year Systems Engineering
Major concentrating in Management Information
Systems. She hails from Southington, Connecticut.
When she is not calculating the nearest neighbors for
an account, she likes to converse with the various
Michele's at Providian Financial. Melaina is moving
out west after graduation to work for Providian
Financial in sunny San Francisco, California.
Brian Marr is a fourth year Systems Engineering
Major from Virginia. Brian spent the majority of the
past year appreciating the merits of linear regression
in predicting account value. In his free time Brian
enjoys playing pool and studying airport timetables.
He will be working for Dell Computers next years.
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