The use of data and analysis in collections has come a long way. The best collectors not so long ago would rely on the magical powers of knowledge and instinct to determine the accounts that were the most apt to pay and, if so, how to best secure payment.
The days of believing that collecting is purely an “art” are long gone. Of course, many collection floors still have one or two old-school legends lurking in cubicles, forgoing the best-planned treatment strategies to don blue tights and a red cape with a giant “C” – for “Captain Collector” - patched on. These collectors are wasting time and money with no return on investment – and it’s your dime.
It’s a new era. If your organization leverages data effectively and crafts the appropriate segmentation and treatment strategies based on empirical data, then your caped collections hero is leading you astray.
Shifting Data Challenges
In the past, there was a shortage of data to effectively collect on a portfolio. For collection agencies, the creditor payment historical data often was weak and did not neatly fit into the standard new business layouts.
Credit data was bulky and expensive. Non-credit data was spotty and rarely led to paid dollars.
Public record data was fragmented and not easy to integrate into the collection process; aggregated or summarized data had never been used in the space; and the Internet had not yet fully realized its value to collectors.
Creditors were and still are repurposing data used early in the account acquisition and management cycle, thus they were not far ahead of the agencies in managing the data challenges.
In the past, actionable data was sparse. When it was available, it must have seemed like magic as collectors chased the rainbow trying to find the secret sauce - the data about a consumer’s ability and willingness to pay. A plethora of data accumulators, pretexting organizations and others flocked to the industry and, over time with the help of regulators, left.
It should be noted that illegally obtained data such as that secured by pretexting or “tricking” the telephone billing system is still available in the market. This sort of data is illegal. Be wary.
Today’s data challenge is much different. The industry has migrated quickly from a period of very little data to data overload. A confluence of factors drove this migration, including: maturation of the Internet; consolidation of non-credit data vendors; technology enhancements; credit-reporting agencies focusing on collections as a growth industry; the emergence of debt buying; the credit economy and, lastly; the ongoing complexity of the collections regulatory landscape.
All of these things combined for the rapid acceleration of data availability and quality analysis to drive predictive results and portfolio best practices.
How does a collections organization sort through today’s data landscape to build a high-quality data-driven practice? To answer this, we first have to look at the different types of data available in the market.
Types Of Data Collectors Use Today
Performance data. Performance data is the information preened from the history of an account. For example, if I pay my credit card on the eighth of every month but the account is due on the fifth, this is pertinent performance information.
You can infer quite a bit about the paying and spending patterns of a consumer through performance data. Although performance data is important, some collections organizations overemphasize the data source and build collections and recovery models exclusively on this data set. This practice is dangerous in shifting economic times and does not paint the full financial picture of the consumer.
Credit data. Whereas performance data demonstrates how a consumer performs on a particular account, credit data demonstrates how that consumer performs on all of their credit accounts. Credit data is important because it can highlight leading indicators before they are obvious within an individual portfolio. Credit data generally also offers a high degree of phone and address quality. Credit data gives a good snapshot into a person’s ability to pay and can help model the best treatment strategies.
Public record data. Public record data is generally less predictable than credit data but contains niche information that is helpful for deeper, investigative skip work. Judgment and court case data can be leading predictive elements and give insight into the right course for an account. Public record data is a broad set of varying data elements that are available ad-hoc on the Internet or through other data sources, emissaries and consolidators.
Summarized data. Summarized data can be aggregated at different levels, but one typical method is to aggregate data at a ZIP + 4 level. Summarized data is a broad set of data attributes. Whereas performance data tells a collector how a consumer is doing with his or her account and credit data tells a collector how a consumer is doing within all of their accounts, summarized data extends the field of vision for the collector and tells them how the consumer is doing in the context of his or her neighbors.
Geodata, or geographic data, is a subset of summarized data and can easily call out the performance history and potential of differing geographic regions.
Although you cannot use geographic or summarized data to grant credit or make credit decisions, it is commonly used for collections segmentation and strategy builds. Would it be important to note that there are neighborhoods in Minneapolis that have credit scores that are nearly 6% higher than the national average?
Is it important that there are ZIP + 4 ranges in Indiana cities with twice the average number of accounts in collections than the national average? Would you treat those accounts differently if you knew this information? If there is an abnormally high rate of foreclosures in a neighborhood and a high number of houses on the market in that same neighborhood, would you move to a foreclosure or would a loan modification be more appealing?
These are the sort of questions that can be asked and answered with the usage of summarized or aggregated geo-based data.
The Great Recession, as some are calling it, is a national issue but it’s more of a local problem. For all the banter from politicians about Wall Street versus Main Street, there is something inherently true about the economy: It affects everyone differently. Yet, I have visited dozens of collections organizations over the last few years and every one of them treats an account in Elkhart, Ind., the same way they treat an account in Omaha, Neb.
Proud of their robust segmentation and treatment strategies, collectors do not seem to care that unemployment in some areas is nearly 16%, while it is only 5.5% in others - those accounts are called the same and treated the same but with different results.
If a collector could know in advance that consumers in a certain area face high unemployment, significant equity loss and high foreclosures, would they treat that account differently than they would treat an account in an area of high employment, steady home prices and rising credit scores? Absolutely.
Leveraging geographic-based summarized data gives insight into which accounts to prioritize, contact methodologies, settlement and payment arrangement terms, and much more, but it is a data variable that for unknown reasons simply is not being used today en masse.
Optimizing Data Utilization
There are several ways to address the confluence of environmental factors that dictate how well or poorly an organization manages data.
If your organization is rich with analysts who can help create models that drive the operational workflow, then explore the utilization of a variety of sources of data. One thing that is evident is that combining a variety of predictive data sources provides significant lift over single-source data. If your models are based on performance data, leveraging credit data and public record data can bring incremental lift.
If you do not have the luxury of a rich bank of modelers helping to drive your operational strategies and instead utilize off-the-shelf collections or recovery models, choose a model that not only validates well historically, but also is built off of a variety of data sets instead of a single-source type of model. Not only will dual source data models perform better today, but they will also adapt to microtrends nicely in the future.
Explore the utilization of summarized geographic data, as this information can help define successful campaigns and give much greater insight into how to segment and treat accounts in different regions; national-only strategies leave a lot of money on the table.
Lastly, what is the most important rule in data management and collections in general? Know when to stop. There is a diminishing return in everything. The seventh vendor in your robust skip-trace waterfall is overkill. So is your third, for that matter.
Effective collections and recovery systems offer a lot of flexibility with workflow management functionality. Leverage this flexibility so that you can extract the complexities of today’s high-volume data world away from your collector through automation.
This allows collectors to focus on what they should be focused on: executing against defined strategies and collecting. Sure, it’s great to reminisce about the old days. But if your organization is managing and leveraging data effectively, the results will speak volumes, and Captain Collector can remain in retirement.
Dan Buell is vice president of Credit Services Marketing at Experian.
