International Distributor Gains 50% Efficiency IncreaseonCreditReviews

International Distributor Gains 50% Efficiency Increase on Credit Reviews
Automates Credit and Collection Quickly and Efficiently

Uline markets its shipping and packaging materials to every identifiable business in North America, and currently has more than 300,000 customers. Speed and operational excellence are crucial to its business model. With its double-digit yearly growth over the last few years, the company faced a dilemma with its credit processing. Uline was physically stopping too many orders for further credit review, and was worried that this could impact its continued growth, force the company to hire more resources to keep up, and damage its longstanding focus on exceptional customer service.

Since its founding in 1980 by Liz and Dick Uihlein, the company has grown quickly from a single product to several billion dollars in revenue, 35,000+ product SKUs, and has expanded product offerings beyond shipping supplies to include warehouse handling equipment, safety supplies, janitorial products, and manufacturing and retail offerings. With eight warehouses throughout the U.S., two in Canada, and two in Mexico, Uline has established itself as the leading distributor of shipping, industrial, and packaging materials to businesses throughout North America.

Recognizing the role that greater data automation could play with both its credit and collections, Uline turned to long- time data partner Dun & Bradstreet to help.

THE CHALLENGE:
Streamlining the Credit and Collection Process


According to Kurt Albright, Director, Credit & Collections at Uline,“When Ifirst got here eight years ago, order volume was very high andmy credit analysts were physically touching 15%ofthose orders tocheckfor creditlimits,pastduenotices,orsuspicious items.Within a fewyears,wewere processing over 2x as many orders, and it became clear to me that we would have to expand staffing to keep up with the growth or become more efficient. My goal was to get the number of orders that had to be touched by a person in my department below 10%.

” Lookingback,Albrightknewthat reducingthenumberof orders thatwouldhave tobe stoppedin the queue for further creditreviewwouldsaveconsiderabletimeandresources.At thattime,Uline’s credit andcollections process was already 85%automated, and it was usingDun&Bradstreet products but wanted to deepen its relationship and expand the role of data. “For us, we knew it would be an efficiency play that would allow us to approve more orders and touch fewer of them, and continue our fast growth,” Albright states.

On the collections side, Albright said that the challenge was to gain more customer insights that would shed light on where to best focus resources and prioritize calls for bad-debt and pastdue account management.
  THE SOLUTION:
Supporting Complex Credit and Collection Management Needs

With a strong focus on the role of data at the company, Uline depends on the Dun & Bradstreet DNBi® Risk Management solution and the Data Integration Toolkit functionality that feeds real-time Dun & Bradstreet data into Uline’s credit process. DNB provides extensive data for Uline’s credit teams looking to further automate and systematize the new credit application review and account management processes.

Here’s how Dun & Bradstreet’s products work for Uline’s credit process: When a customer hits the “Buy” button on Uline’s website, the Uline system instantaneously pings Dun & Bradstreet’s servers. If there’s a company match, the Dun & Bradstreet toolkit pushes Dun & Bradstreet data and adjusts information in Uline’s system. Essentially, the order is fed through an algorithm that reviews Dun & Bradstreet data such as company size by number of employees, the risk class, and PAYDEX® scores. Based on those data elements, a credit line is automatically assigned to that account, and the order is shipped and invoiced without ever being touched by an analyst.The whole process takes just minutes.

The only time an order is stopped is when there isn’t high confidence on a Dun & Bradstreet match for that business, or when an order exceeds certain past-due parameters. But even these orders are evaluated quickly, often within five minutes of the order stopping in the queue. The credit analysts are each handling upwards of 200+ credit reviews a day. Albright says, “Their job is like that of an air traffic controller — they have to review multiple screens at once, and make the right decisions for Uline’s fast-paced business.


” When an order stops and analysts want to take a closer look, they pull up a DNBi report with all its rich detail to help in the decisionmaking process. “With Dun & Bradstreet, I’m pleased with the automation and the speed with which our credit analysts handle orders,” says Albright.

COLLECTIONS INSIGHTS:
Customers Extending Their Own Terms

On the collections side of the business, Albright says, “Each collector was making over 150 calls a day on accounts, many of which we weren’t sure we needed to make.” To establish priorities on collection calls, Uline leveraged Dun & Bradstreet data and advanced analytics, and tapped one of its expert analytical team members for a special project that would dig deeper into this situation. After running sophisticated regression analysis and testing of different customer attributes in three different territories, Uline developed what it called the Collection Queue Model. This offered compelling insight into the changing risk complexion of the portfolio. Based on the results, Uline now better determines how to establish priorities for collection calls.

“After digging deep into the data, we found the customer portfolio was becoming a little less likely to pay slow, or less likely to be written off,” explains Albright. “That was one of those a-ha moments because we think a pretty significant portion of our past-due portfolio is with customers who are unilaterally extending their own terms — for working capital purposes. We’re seeing it all over the place.”

Albright admits that to continue to support the company’s growth, it would have limited ability to impact those customers driving the past-due numbers. However, what Uline discovered instead was that bad debt write-offs declined significantly as a percentage of sales over the last few years.

“The conclusion I draw from that is that we are getting to more of the high-risk accounts at the right time, getting them to pay before they become a severely delinquent account,” says Albright.

He also admits that as a “credit guy,” he hates to see bad debt. “We’re not afraid to take risk, but I hate to see bad debt write-offs increase. We’re leaving money on the table when that happens. I’m much more comfortable today than I was a few years ago,” says Albright.

THE RESULTS:
Boosting Efficiency 50%, Capturing Insights

“On the credit side, we’ve seen the percentage of orders we have to touch fall from 15% to 7%. That is a 50% pick up in efficiency, which enabled us to avoid having to add new staff at the pace sales were increasing,” says Albright.

While the credit side of the business is all about getting orders processed quickly and efficiently, the collections side focuses on call prioritization. “We’re now calling the right people at the right time with the right strategy, thanks in part to Dun & Bradstreet’s advanced analytics,” says Albright.

With the role of data so paramount at Uline, Albright says it’s imperative to have team members and partners like Dun & Bradstreet with “strong analytical chops.”

ULINE USE CASE
At-a-Glance
– A multi-billion-dollar-in-revenue distributor of shipping, industrial, and packaging materials to businesses throughout North America — family owned and operated

– Wanted to further streamline credit and collections processes handling a very large number of orders each week

– Credit results: Increased efficiency by 50% on credit checks required by analysts, dropped from 15% to 7%

– Collection results: Developed Collections Queue Analysisthat is providing insights into the risk of the portfolio,helping Uline decrease bad-debt write-offs significantly