This is the presentation that I gave at the March 2010 CRF Meeting with Mike Elliott from RRD and John Fahey from Edward Don

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Cashing In With Statistical Portfolio Scoring
Edward Don & Company and RR Donnelly
Case Study Summary
Maximizing results by understanding risk

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Edward Don & Company
Case Study

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About Edward Don & Company
Distributor of equipment & supply to foodservice industry. “Everything But The
Food”
National distribution network – some international
Approximately 35,000 active customers with an average balance less than $2500
Centralized credit office with 5 direct reports with a staff of 40 credit and collection
analysts
Sell to restaurants, lodging, healthcare and institutions through a sales staff of 350
Most profitable accounts are the Independent operators (mom & pop’s), but are
also the most challenging from credit and collection perspective
Owned and operated by the Don family since 1921, Edward Don & Company
(Don) is the world's leading distributor of foodservice equipment and supplies

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Why Did Don Embrace Risk-Based Collections?
Don averages close to 1 collector for every 1,500 customers – it is difficult for the
collector to determine which customers they should focus on, when to focus on them
and what treatment to apply using just aging information.
Don found that using only aging to prioritize collections, many of our low dollar, high
risk customers were being ignored.
Don also reviewed many orders that were placed on hold, but those that stopped
were often contacted too late and many that were put on hold were low risk and the
orders were being released anyway.
Collection activity was managed and prioritized solely on the aging and highest
dollar customer accounts.
Was looking to gain collection efficiencies due to the size of the customer
portfolio, the number of customers going on hold and the limited number of
collectors

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Don – Collection & Portfolio Management Process
Prior to March 2007
Portfolio Risk Analysis was determined by a judgmental scorecard based on a combination of
bureau and internal data.
Obtained statistical credit scores from credit bureau (Experian) on a quarterly basis and
created a a “blended score” with an A to E matrix, based on 70% payment to Don measured
by DBT (Days Beyond Terms) and 30% based on the credit bureau score.
Collection activity was prioritized based on the customer’s risk score.
System generated credit holds were based on Risk Class Score, C & D accounts would go on
hold if a violation occurs in any one of 9 credit rules. The process resulted in excessive holds
which credit analyst had to frequently review resulting in 75% of holds being released without
contacting sales and 98% of accounts eventually being released from credit hold.
In addition since only 10% of accounts were ranked A - low risk, Don thought they may be
missing opportunities to increase sales with that customer segment.
Don implemented a risk-based collections methodology more than 6 years
ago based on bureau scores

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Don – Collection & Portfolio Management Process
Prior March 2007
Bureau based model produced the following customer segments
13.2% of Accounts are Ranked A (9.4% of $)
21.4% Ranked B (17.2% of $)
32% Ranked C (32.6% of $)
33.4% Ranked D (40.8% of $)
Don was using a bureau based model to assess risk and accounts could not
be rolled up to the parent level and had to be scored at the ship-to level

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Don – Collection & Portfolio Management Process
Implemented March 2007
Model focused on using only Don’s internal A/R and performance data, data which
has proven to be more predictive for small and mid-sized customers.
After several tests, final model validated based on Don’s customized “BAD”
definition - more than 50% of monthly outstanding balance goes 61 or more days
past due at sometime during the 6 month period after the scoring date.
For Don it increased the number of A&B (low risk) accounts and reduced the
number of C & D (high risk) accounts allowing collectors to truly focus on problems
and collection opportunities based on risk assessment that was proven to be
accurate.
End result is that Don’s collectors are spending more time collecting and less time
releasing orders.
After having PMI conduct a Net30Score Validation Analysis and being satisfied
with the predictive results Don made the decision to use Net30Score and
ScoreMiner, PMI’s hosted web-based reporting and querying application

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Don Customer Segmentation
Prior vs. Post Statistical Portfolio Scoring
Statistical based collection / portfolio score with roll-up to parent level
25% Risk Class A
39% Risk Class B
21% Risk Class C
15% Risk Class D
36% of customers rank risk class C and D
Bureau based score that could not score at the parent level
13.2% Risk Class A
21.4% Risk Class B
32% Risk Class C
33.4% Risk Class D
65.4% of customers rank risk class C and D

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Summary of Benefits Gained From
Statistical Portfolio Scoring
Significantly improved collection department performance by prioritizing collection resources
based: first upon inherent risk, and second upon account aging
25% increase in phone contacts, which drove cash flow improvement and lower DSO.
DSO reduction of 8% during first 24 months
Significant reduction in the number of accounts going on credit hold, increase customer
satisfaction
Reduction in credit bureau costs, no longer purchase the quarterly scoring updates
Using ScoreMiner for customer reporting needs, including filtering and querying, segmentation,
comparative and rate of change analysis
Most important we saw a productivity gain as collectors spent less time reviewing and releasing
orders and more time productively contacting customers.
During the worst economic downturn it has ever seen, Don has been able to
reduce the overall level of risk in it’s customer portfolio

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RR Donnelley
Case Study

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About RR Donnelley (RRD)
Provides print and related services, including business process outsourcing, print fulfillment,
transactional print-and-mail, print management, online services, digital photography, color
services, and content & database management.
Products and related service offerings include magazines, catalogs, retail inserts, books,
directories, commercial print, financial print, direct mail, forms, labels, office products, pre-media,
and logistics services.
Client Financial Services are centralized domestically in Chicago and Reno with a six person
management team and 51 credit and collection analysts, 3 credit coordinators and 6 collectors.
Have an average 65k open bill-too’s each month, 75% of which have an average balance of less
than $2,500
Use SAP and have complex invoicing solutions and multiple systems feeding target platforms.
RRD was founded in 1864, based in Chicago and is a public company with
2008 revenue of 11.5B

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Why Did RRD Embrace Risk-Based
Collections and Portfolio Management?
RRD was using systemic rules, aging analysis, and dunning response, which seemed
reactionary, to drive day-to-day collection activity
Our team composition varies greatly in collector to account responsibility ratio and it was
very difficult for our team to maximize the time allotted to proactively collect
Outbound collection activity was being diverted to handling: inbound calls from
unnecessary dunning, accounts where relationships were ‘known’ as well as customers
where risk of loss was low
As our ERP uses a formula to put account on hold that didn’t use risk, we were
reactionary as we were trying to collect the last order before producing the next. This
was time consuming, and not within the partnering spirit we aim to forge with sales and
clients
We were looking for a proactive solution to compliment to our system driven
methodologies for collections

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RRD – Collection & Portfolio Management
Process Prior to July 2008
The target platform relied on aging based segmented dunning, systemic holds and
our collections software
The on-boarding platforms relied on relationships and highly skilled personnel
Implemented collection software to manage and automate collector work flow.
Collection software used a rules based scorecard to drive daily work lists that was
based on aging metrics, customers segmentation and collector workload expectation
The collection software was never fully embraced
Our target A/R platform (SAP) was undergoing an upgrade to a newer version
Partially integrated strategies existed due to acquisitions.

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RRD– Portfolio & Collection Management
Process After July 2008
After Net30Score go-live and before our SAP upgrade
PMI validated and deployed Net30Score quicker than our systems upgrade and we were able to start to take
‘advantage of’ and ‘experiment with’ the definitions, expectations and scoring outputs
PMI provided an on-line management tool (ScoreMiner)
PMI allowed for ad hoc reporting capabilities right away
PMI was able to customize fields to enable easier reporting
PMI created search fields useful for us
PMI included all provided data which allowed for comprehensive and detailed usability
PMI provided training for our staff
After having PMI conduct a Net30Score Validation Analysis and being satisfied
with the predictive results, RRD made the decision to use Net30Score and
ScoreMiner to help manage the collection process

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How RRD Leveraged Statistical Collection &
Portfolio Score In The Collection Process
RRD improved our new worklist tools by using the PMI scoring into our
workflow model
RRD was able to work with our partners at D&B to provide additional eRAM
scorecard data that fit our needs. The Net30Score outputs are now weighted
variables in our D&B eRAM scorecards.
RRD senior management uses PMI’s ScoreMiner for ad-hoc reporting and
querying. Analysts and collectors who use the scoring day to day now access
the data through SAP and eRAM but also have access to ScoreMiner
After the SAP upgrade Net30SCore was embedded into our systems and
solutions

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Summary of Benefits Gained From
Statistical Portfolio Scoring
For the portion of our portfolio using Net30Score from July 08 until the SAP upgrade in Aug 09, RRD saw the
following:
Total past dues (TPD) improved from $138.1M to $110.1M
120+ days dues went from $20.2M to $5.5M
Since Aug the trends continued TPD are $101.6M & $1.1M is over 120 days
Major improvements in the 1-30 area and aged 120 areas attributable to focusing those hours of the day on the
right customers based on risk
Reduction in reliance on systemic holds for past dues and dunning to drive collections (shift from reactionary to
proactive management – working smarter not harder)
Final Comment: Getting in early allowed us the time to work out kinks, get the team familiarized with the product,
and include the model into other processes and system solutions. It also allowed us to start to see results even
before the SAP upgrade and full effect took root, which helped with organizational buy-in with management and
the functional user community.
RRD has been able to integrate the PMI scoring into our dunning and collection
work list processes (SAP FSCM), as well as into eRAM and has helped us
improve each of those areas.