Smarter Better Faster – Debt Collection Story

 In the book Smarter Faster Better, there is a story of a debt collection team that was collecting a million dollars more per month then the other teams. And, they some of highest employee satisfaction scores. How did they do it? Have a look an excerpt from the NY Times best seller! 


In 1997, executives running the debt collection division of Chase Manhatten Bank began wondering why a particular group of employees in Tampa, Florida were so much more successful than their peers at convincing people to pay their credit card bills. Chase, at the time, was also one of the largest credit card issuers in the nation. As a result, it was one of the largest debt collectors. It employed thousands of people in offices all over the country, who sat in cubicles all day and called debtor after debtor, to harass them about overdue credit card bills.

Chase knew from internal surveys that debt collectors didn’t especially like their jobs, and executives had grown accustomed to lackluster performance. The company had tried to make the work easier by giving the collectors tools to help convince debtors to pay. As each call occurred, for instance, the computer in front of the debt collector served up information that would assist in tailoring their pitch: it told the debtor’s age, how frequently he or she had paid off their balances, how many other credit cards they owned, what conversational tactics had proven successful in the past. Employees where sent to training sessions and given daily memos with charts and graphs showing the success of various collection tactics.

But, almost none of the employees, chase found, paid much attention to the information they received. No matter how many classes Chase provided or memos they sent, collection rates never seemed to improve much. So executives were pleasantly surprised when one team in Tampa started collecting larger than usual amounts.

That group was overseen by a manager named Charlotte Fludd, a evangelical minister in training with a passion for long skirts and Hooters chicken wings, who started out as a debt collector herself and worked her way through the ranks until she was overseeing a group responsible for the hardest accounts, debtors who were 120 to 150 days overdue. Cardholders that far in arrears almost never paid off their balances. However, Fludd’s group was collecting 1 million more per month than any other collection team. What’s more, Fludd’s group reported some of Chase’s highest employee satisfaction scores. Even the debtors they collected from, in follow up surveys, said they appreciated how they were treated.

Chase executives hoped Fludd might share her tactics with other managers, and so they asked her to speak at the company’s regional meeting at the Innisbrook Resort near Tampa. The title of her talk was “Optimizing the Mosaix/Voicelink Autodialer System.” The room was packed.

Can you tell us how you schedule your autodialer? One man asked.

“Carefully” Fluff said. From 9:15am to 11:50am, she explained, the collectors called peoples home numbers because they were more likely to reach a wife taking care of the kids. Women were more likely to send a cheque, Fludd said.

“Then, from noon to one thirty, we call debtors’ work numbers,” Fludd explained. “and we get a lot more men, but you can start the conversation by saying, “Oh, I’m glad I caught you on your way to lunch, Like he’s real important and his schedule is busy, because that way, he’ll want to live up to your expectations and he’ll promise to pay.

“Then at dinnertime, we call people we think are unmarried because they’re more likely to be lonely and will want to talk, and then right after dinner, we call people whose balances have ballooned up and down, because if they’ve already had a glass of wine they’re relaxed, we can remind them how good it feels to start paying the card off.”

Fludd has dozens of tips like these. She had advise on when to use a comforting tone (if you hear soap operas in the background), when collectors should reveal personal details (if the debtor mentions kids), and when to deploy a stern approach (to anyone invoking religion)

The other managers didn’t know what to make of these suggestions. All of them sounded perfectly logical- but they didn’t think their employees would be able to use any of them. The average debt collector had just a high school education. For many collectors, this this was their first full time job. Manager mostly spent their time reminding employees to avoid sounding wooden on the phone. Their debt collectors weren’t going to be able to pay attention to what television show was playing in the background or listen for religious references. No one was adept enough at analyzing debtors’ records to figure out how to reach a housewife versus her husband. They just talked to whoever picked up the phone. Chase might send the collectors memos each morning, the company might give them computer screens of information and provide them with classes- but managers knew almost no one actually read those memos or looked at screens of information or used what they learned in class. Simply having a phone conversation with a stranger about a sensitive like an overdue bill was overwhelming enough on its own. The average collector couldn’t process additional information while conducting a call.

But when fludd was asked why her employees were so efficient at processing more information than the average collector, she didn’t have any great answers. She couldn’t explain why her workers seemed to absorb so much more. So after the conference, Chase hired a consulting firm from Mitchell Madison Group to examine her methods.

“How did you figure out that it’s better to call women in the morning?” a consultant named Traci Entel asked her when Fludd was back in the office.

“Do you want me to show you my calendar?” said Fludd. The consultants weren’t certain why she needed a calendar to explain her methods, but sure they said, let’s see the calendar. They expected Fludd to pull out a date book or journal. Instead, she dropped a binder onto the table. Then she wheeled over a cart containing several more binders just like it.

“Okay” Fludd said, leafing through the pages filled with numbers and scribbled notes. She found the sheet she was looking for. “One day, I came up with this idea that it would be easier to collect from younger people, because I figured they are more eager to keep a good credit score,” she said.

Fludd explained that coming up with such theories was common on her team. Employees would gather during lunch breaks or after work to kick around ideas. Typically, these ideas didn’t make sense – at least not at first. In fact, the ideas were often somewhat nonsensical, such as the suggestion that an irresponsible young person who is already behind on her debts, for some reason, would suddenly care deeply about improving her credit score. But that was ok. The point wasn’t to suggest a good idea. It was to generate an idea, any idea at all, and then test it.

Fludd looked at her calendar. “So, the next day, we started calling people between the ages of twenty one and thirty seven.” At the end of the shift, employees reported no noticeable change in how much they convinced people to pay. So the following morning, she told her employees to call people between the ages of twenty six and thirty one. The collection rates improved slightly. The next day, they called a subset of that group, cardholders between twenty six and thirty one with balances between 3000 and 6000. Collection rates declined. The next day: Cardholders with balances between $5000 and $8000. That led to the highest collection rates of the week. In the evening before everyone left, managers gathered to review the day’s results and speculate on why certain efforts had succeeded or failed. They printed out logs and circled which calls had gone well. That was Fludd’s “calendar”: the printout from each day with annotations and employees comments as well as notes suggesting why certain tactics worked so well.

With further testing, Fludd determined that her original theory regarding young people was a dud. That, in itself, wasn’t surprizing. Most of the theories were duds initially. Employees had all kinds of hunches that didn’t bear up under testing. But as each experiment unfolded, workers became increasingly sensitive to patterns they hadn’t noticed before. They listened more closely. They tracked how debtors would respond to various questions. And eventually, a valuable insight would emerge- like, say, it’s better to call people’s homes between 9:15 and 11:50 in the morning because the wife will pick up and women are more likely to pay a family debt. Sometimes, the debt collectors would develop instincts they couldn’t exactly put into words but learned to heed nonetheless.

Then someone would propose a new theory or experiment and the process would start all over again. “When you track every call and keep notes and talk about what just happened with person in cubicle beside you, you start paying attention differently,”Fludd told me. “You learn to pick up on things.”

To the consultants, this was an example of someone using the scientific method to isolate and test variables. Fludd’s team was heightening their sensitivity to the information flowing past. In a sense, they were adding an element of disfluency to their work, performing operations on the “data” generated during each conversation until lessons were easier to absorb. The spreadsheets and memos that they received each morning, the data that appeared on their screens, the noises they heard in the background of phone calls- that became material for coming up with new theories and running various experiments. Each phone call contained tons of information that most collectors never registered. But, Fludd’s employees noticed it, because they were looking for clues to prove or disprove theories. They were interacting with the data embodied in each conversation, turning it into something they could use.

This is how learning occurs. Information gets absorbed almost without our noticing because we are so engrossed with it. Fludd took the torrent of data arriving each day and gave her team a method for placing it into folders that made it easier to understand. She helped her employees do something with all those memos they received and conversations they were having- and, as a result, it was easier for them to learn.


About the Author Ken Matthews

Ken Matthews is a veteran Canadian entrepreneur and business executive with over 30 years experience.

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