Prioritizing Your Collections Pt. 2:
An Automation Case Study
Using the aging report as the sole means of prioritizing
your collections is a flawed but common approach. Too
frequently, collections professionals will base their strategy
on which customer has the most past-due balance. This
approach makes the incorrect assumption that the most
overdue collections pose the most risk. However, as
explained in Prioritizing Your Collections Pt. 1: The Do-ItYourself Approach, this is not always the case.
Instead of relying solely on the aging report, collections
managers should segment their call list in order to
approach each customer in a productive, efficient, and
appropriate manner. Collections professionals can then
analyze the different segments based on their inherent risk
to create a collections approach that matches the particular segment. The ultimate goal is to increase
cash flow, promote ongoing sales and free up the collections department’s time to continue an ongoing
analysis of the process.
The tools discussed in Prioritizing Your Collections Pt. 1: The Do-It-Yourself Approach can help collections
managers manually create a prioritized list that allows the department to focus on the most productive
collections with the most effective approach.
But what if the manual approach to prioritizing collections does not fit your company’s needs?
Why Use Automation?
There are a number of reasons why manually prioritizing collections may not suit your department.
First, you may have too many customers. Even though a company with 10,000 customers may be able
to create general portfolios, it would take far too many resources to properly segment them manually.
It may be feasible to divide them into a couple of segments, such as industry and payment history, but
the limited segments would not produce the same department-changing results an automated
solution’s fully-segmented collections list could.
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Prioritizing collections is beneficial for companies of any size, but small businesses may not have the
staff needed to manually prioritize their existing customers. A single employee may be wearing many
hats within the company, prohibiting them from putting aside the time needed to manually segment
their list. However, an automated solution can provide valuable prioritization without causing the
employee’s other responsibilities to suffer.
Businesses are often concerned with the project scope and cost of automated solutions, but with the
advent of Software-as-a-Service (SaaS), even small-businesses are able to take advantage of collections
automation. SaaS is generally subscription-based, and is accessed over the internet. SaaS offers pay-asyou-go rather than a large upfront cost. All maintenance is performed on the vendor’s side, so very little
internal IT support is required.
Ultimately, automated solutions are able to perform tasks and analyze trends that are difficult to nearly
impossible to achieve manually.
Automation Case Study: CertainTeed
It is hard to find a place that CertainTeed’s parent company, Saint-Gobain S.A.,
a French multinational corporation, has not touched. Originally a mirror
manufacturer, it now produces a variety of construction and highperformance materials. CertainTeed specializes in manufacturing building
materials including roofing, vinyl siding, gypsum, ceilings and insulation.
CertainTeed’s corporate office, located in Valley Forge, Pennsylvania, hosts the
company’s credit shared services department, led by Director of Credit Susan
Delloiacono. The credit shared services department encompasses
CertainTeed’s 65 facilities and serves its North American customers.
Delloiacono’s job is particularly difficult, as during a rough economy, building manufacturers are hit hard
with late payments and delinquent customers. “You’re busy as a credit professional,” Delloiacono says.
“There is no Maytag repairman sitting in my office.”
When Delloiacono took the director of credit position in 2010, CertainTeed’s credit shared services
department ran on five independent ERP systems, each dedicated to a specific product line. In addition
to the unique ERPs, each product line had its own credit manager. While the credit managers were wellversed in their specific product line, “… we could sell to the same customer in all five ERP systems with
no way to manage the overall risk,” Delloiacono says.
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The segregated credit shared services department was created with the customer-first mentality that
the more you know a product, the better you can assist your customer. However, each credit manager
within the department established their own parameters for what they thought the appropriate risk was
for their particular product, causing issues for customers that purchased multiple product lines.
“We were considered a shared service, but the only thing we shared was a coffee pot and a rug,”
Delloiacono says.
While the credit managers had a solid grasp on their own product, the department lacked congruity
across the board. Consequently, analyzing their overall customer base and creating a prioritized
collections list was a logistical nightmare.
The Implementation — SunGard’s AvantGard
CertainTeed’s CFO saw a product demo of SunGard’s AR solution, AvantGard GETPAID, and liked how
the automated system put collections, deductions and credit under one umbrella.
Shortly after the demo, Susan Delloiacono was hired to oversee the AvantGard GETPAID implementation
because of her extensive experience with the service. Delloiacono had overseen two AvantGard
GETPAID implementations — one with Dialogic Corporation — an Intel company in the ’90s — and the
other with Brother International Corporation. CertainTeed would be her third.
“Our first challenge was basically getting the linkage correct in terms of all of the children (product
lines), so I could see what was happening at the parent level,” Delloiacono says.
AvantGard GETPAID, like many collections automation solutions, offers a variety of services within a
single system. The collections service was turned on first as a means to link the product lines and put
the customers under the single umbrella.
Delloiacono and the credit shared services department were required to have AvantGard GETPAID live
within five months, despite the scope of the project. The deadline was met, but not without substantial
change within the department.
“When an employee is a soup-to-nuts guy — they write up the credit line reviews, release orders from
credit hold, make a of couple phone calls, visit customers — they kind of have their own shop,”
Delloiacono explains. “I changed it to a segregation of duties shop, where people who were better at
relationships did collections, and the more analytical people did the credit write-ups and analysis.”
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With a newly focused department and SunGard’s freshly implemented collections system, Delloiacono
and her team were ready to take advantage of the predictive metrics that AvantGard GETPAID provides.
How It Works
Once implemented, AvantGard GETPAID evaluates the customer base and provides the user with critical
data that allows the department to prioritize their collections using a risk-based strategy.
It does so by analyzing the customer master file, invoices, credit bureau scores and any other
information requested by the user to create a score that accurately assesses an account’s real risk.
Unlike an aging report that gives you a snapshot of your current collections, AvantGard GETPAID
assesses risk based on both current and paid invoices to provide users with an accurate picture of the
likelihood a customer will pay late or become delinquent. Essentially, it allows collections to be
proactive instead of reactive.
After AvantGard GETPAID evaluates a customer and calibrates them to the department’s portfolio, the
user will be provided with five key predictive metrics:
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Risk Score – Customers are given a score on a scale from 0 to 100, where 100 is the poses the
least amount of risk and 0.01 poses the most.
Probability of Bad – The system will provide the probability of a good-standing company
becoming delinquent in the following six months.
Risk Class – Customers are put into one of six classes, ranging from very low risk to extreme risk.
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Cash at Risk – Using the other outputs, the cash at risk output can help determine the amount of
a customer’s outstanding balance that is actually at risk.
Adverse Reason Codes – The Adverse Reason Codes let the user know why the customer scored
the way they did.
Risk Class is perhaps the most valuable predictive metric provided, as it uses all available customer data
to place accounts in a category based on their risk. An aging report will tell you which accounts are the
most past-due, but the risk class will take all collected data — such as account age, credit bureau scores,
payment history — and assign a true risk of delinquency.
In CertainTeed’s case, AvantGard GETPAID combined its predictive metrics with CertainTeed’s own
metrics to create an internal score based on CertainTeed’s risk acceptance. Any customer who changes
more than ten points creates an automatic alert in the credit module.
“This month’s alert list just came out. We had 200 customers to analyze further,” Delloiacono says.
“Two hundred is a lot easier to look at than a list with over 4,000 customers on it. It puts your nose on
the right track.”
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The list of high-risk customers effectively prioritizes your customers, significantly reducing the amount
of time spent on unproductive calls.
“You’re going to unleash your collections team to work on meaningful tasks,” C.J. Wimley, chief
operating officer of SunGard’s AvantGard Receivables says. “AvantGard GETPAID gives them at least
twice as much time in the day to work on collections. Usually it’s more than that.”
The Results
CertainTeed’s implementation of SunGard’s AvantGard credit and collections solution resulted in a more
efficient, more valuable credit shared services department. The metrics provided by SunGard’s
automated solution allow Delloiacono and her team to determine who is truly at risk for not paying.
“We are learning more about our customers,” says Delloiacono. “With the scores, I can be mindful of
high-risk accounts. They’re going to be in the rotation frequently versus maybe the customer who is
very-low risk.”
Since AvantGard GETPAID went live, Delloiacono has seen a six point drop in Days Sales Outstanding
(DSO). Additionally, bad debt has decreased dramatically.
The product-centered micro-departments have been replaced with employees who are specialized in a
credit department function. Customer service has increased because the predictive measures allow the
staff to resolve potential issues long before they become problems. The value the staff now brings to
the company increases engagement and leaves employees with a sense of accomplishment.
“It has really paid off,” says Delloiacono.
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