“Eighty-five percent of statistics are made up!”
I love that quote. I’ve used it so long, I don’t remember if I saw it somewhere or may it up myself. But, it’s my favorite example of bad information—funny, totally unverifiable, and somehow still quoted with confidence.
It’s also a surprisingly good reminder of how easy it is to accept flawed data without thinking twice.
In the collections world, where numbers drive decisions, data has a kind of mystical power. But when the underlying data is incomplete, biased, or misinterpreted, we risk building our vendor performance evaluations—and our resulting strategies—on shaky ground.
We Rely on Bad Information Because It Feels Better Than No Information
Let’s be honest: it’s uncomfortable to admit we don’t have all the facts. So instead, we latch onto whatever data we do have, hoping it’ll give us enough clarity to make decisions. Even when that data comes with caveats—or should be tossed altogether—we convince ourselves that “something” is better than “nothing.”
But misleading information isn’t just unhelpful. It can actively drive poor decisions. You think you’re optimizing vendor performance. In reality, you may be penalizing the wrong agency, rewarding the wrong strategy, or creating blind spots in your operation.
When Good Data Goes Bad
Sometimes the numbers look great—clean tables, sharp visuals, month-over-month comparisons—but what’s hiding underneath?
Take a simple batch track report by placement month. On the surface, it seems like a solid metric: How are my accounts placed in January performing vs. February or March?
But what if your underwriting department made a subtle shift in February, approving more borderline applicants? Suddenly, your February placements are less collectable—but the report doesn’t know that. You’re now comparing apples to bruised apples, and holding your agencies accountable for performance they could never have delivered.
I like to see spreadsheets with a ton of associated clarifying points. For example, if you’re using a batch track of collection results by month, then list on that same report, that factors that distort the information. You might indicate: February – underwriting change to lower acceptable credit scores. Or maybe: January batch included large inventory of older accounts. Or, maybe even: Internal staffing issues in March resulted in less pre-charge-off internal efforts. These are just a few examples of trying to include historical knowledge and management experiences into data. It’s easy to remember when it’s still fairly new, but a year later, when you’re trying to compare year-over-year results, you’ll be happy to have a bit more explanation with the numbers.
The Self-Fulfilling Prophecy of Placement Strategy
It’s not uncommon to steer “easier” accounts to the agency you think will do the best job. After all, if Agency A is great with small balance debt, why give them complex skip files?
The problem is, when you do this, you’re creating the very results you’re trying to measure. If you feed Agency A all the easier accounts, of course their numbers will look great. Then you use those numbers to say they’re the best—and send them even more of the easiest work. Meanwhile, other agencies are saddled with the leftovers and look progressively worse. It’s a self-fulfilling prophecy fueled by well-intentioned—but biased—placement logic.
Operational Shifts You May Be Ignoring
Even when you think you’re comparing fairly, there are all sorts of changes that can silently distort your agency performance data:
- Changes in balance mix (higher/lower average placement amounts)
- Differences in account age or days-delinquent-at-placement
- Updated scoring models or segmentation criteria
- Seasonal volume spikes
- Legal holds, documentation delays, or temporary freezes
- Internal staffing shifts at the agency level (e.g., their best collector just left)
If you’re not documenting these changes—or asking about them—you’re flying blind.
The Danger of “Data Confidence”
Sometimes people fall into the trap of trusting the data too much just because it’s organized, numerically precise, or presented in a slick dashboard. But clarity in presentation does not equal clarity in meaning. The most dangerous reports are often the ones that look perfect but fail to disclose what changed behind the scenes.
What You Can Do Instead
Bad information is unavoidable sometimes. But you don’t have to let it control your decisions. Here are some ways to take control back on your agency performance measurements:
- Normalize the data: Control for balance bands, delinquency age, and placement volume when comparing agencies.
- Document placement strategy: If you change your logic—even for good reasons—make sure it’s recorded and visible when you are evaluating performance.
- Track upstream changes: Work with your internal teams to understand credit policy, operational, or portfolio changes over time.
- Use randomization or A/B testing: If feasible, randomly assign similar accounts across agencies to establish a performance baseline.
- Talk to your agencies: They often see trends or shifts in account quality before your reports can catch up.
The Missing Piece: Numbers Need Narratives
Before you close the spreadsheet or finalize your performance scorecard, remember this: narratives shape how we interpret the numbers—and sometimes, they save us from misinterpreting them entirely.
When information is incomplete or skewed, observation fills the gap. Get out and listen. If you manage internal collectors, spend time on the floor. Hear the talk-offs. Pay attention to tone, energy, and how they handle objections. If the numbers say performance is down but the calls are sharp, maybe there’s a story the data isn’t telling yet.
If you manage agencies, don’t rely solely on reports. Conduct audits. Listen to calls. Ask questions. Context matters—are they working harder for the same results? Is it a training or expertise issue? Does their activity match their results?
Narratives don’t replace numbers—but they help explain them.
When the metrics are fuzzy, context becomes your compass. Use what you observe to reinforce, refine, or sometimes outright challenge what the reports say. That’s how you go from making assumptions to making informed decisions.
Closing Thought
Data is a powerful ally when it’s accurate, consistent, and understood in context.
But when good data is not available, or it’s flawed, it becomes a liability.
As funny as the “85% of statistics” quote is, it’s also a cautionary tale.
Don’t let bad information dress itself up as good intelligence.
Trust your data— but enhance and verify it with narrative, observation, and common sense.
Author: Judy Hammond
judy.hammond@resourcemanagement.com
Judy Hammond is founder and President of Resource Management Services, Inc. The corporation was founded in 1986 and specializes in auditing and consulting, serving the collection and recovery industry. As President of Resource Management Services, Inc., she has more than 35 years of experience with an emphasis on operational reviews for compliance and operational effectiveness of collection operations, both for creditors’ internal collection and recovery operations as well as collection agencies and attorneys. She has worked with top banks and financial institutions, utilities, credit unions and telcoms, (and their vendors) and has conducted many Best Practices projects. She is author of various industry publications: “Comprehensive Agency/Attorney Usage Study,” “Comprehensive Agency/ Attorney Usage Study II” and “Collect More From Collection Agencies”. Her work with creditors who were looking to sell debt for the first time, and subsequent Buyer/Seller research was the foundation for the second corporation, The Debt Marketplace, Inc. She worked with Dennis Hammond as co-founders of the Debt Buyers’ Association, (now RMAi), building the foundations for industry standards, as well as the original code of ethics. She developed and produced two industry conferences, Collection and Recovery Solutions and Debt Connection Symposium & Expo, from their inception in 2002 and 2006, respectively, to 2022. Prior to starting her own company, she worked with two large collection agencies.
