Debt collection industry can improve success rate through data analysis
Increasingly, decision-makers are turning to data analytics to inform their business decisions. The benefits are well-known throughout most industries and one of the major impediments to implementing it is the difficulty associated with improving the local IT infrastructure. For small- to medium-sized businesses, the cost or ability to manage it may be beyond its reach.
However, for businesses that do implement data analytics, the results can be impressive. Forbes recently reported on the effect that big data analytics can have on a business's success. It found that organizations utilizing the technology were typically 5 to 6 percent more productive and profitable than their competitors. The source also noted a McKinsey study on the subject. The report discovered that data-focused marketing and sales decisions could improve an enterprise's marketing return on investment by 15 to 20 percent.
The benefit to debt collectors
Although the needs of the debt collection industry differ from many other sectors, the same is true within many of these types of organizations. Any business can benefit from a greater degree of productivity, and this will inevitably lead to increased profit as a worker's time use grows that much more efficient and effective. The vast amount of consumer credit data available to some firms can provide a considerable degree of insight if analyzed, particularly when it is coupled with the information debt collectors can provide about their own practices.
Additionally, the ability to track patterns and predict successful operating procedures through a firm's data does not require a company to overhaul its current IT infrastructure, nor enhance it. Third-party data mining solutions can provide the services and tools for agents to improve their collection methods.
Debt collection agencies hoping to enhance their success rate should keep a couple of things in mind. Computer World recently reported on a number of errors businesses make with predictive analytics, including a few mistakes debt collectors made.
Make the data available
The most problematic issue was in the case of a debt collection firm that refused to share its data for analysis, the source noted. After it spent hundreds of thousands of dollars on the project, the management delayed using the program for three years - after the data was rendered useless.
"The models were developed but never used because the political hoops weren't connected," Dean Abbott, president of Abbott Analytics, told the source.
Recognizing patterns requires differing methodologies
The other issue Computer World reported on was on a debt collection business that wanted to discover the best method possible for collecting from borrowers. The firm hoped to know which procedures worked best based on factors such as income, ZIP code, the way the debt was incurred and all the other details that might help or detract from an agent's collection efforts.
Unfortunately, the agency used the same set of collection methods every time. Without the ability to compare a soft or hard approach, or whether sending a letter early or making a phone call early, among other procedures, the data analytics firm had difficulty predicting how the firm should behave in the future.
These are notably rare occurrences. However, debt collection agencies can see considerable benefits from adding predictive analytics into their business model. Small methodology changes can improve an agent's ability to manage borrowers, while even minor productivity benefits can provide long term advantages. These can be employed at an at-need basis, rather than devoting an in-house IT department to the effort.