10 Biases In Skip Trace Tests

Skip trace testing is biased. As collection organizations we do our best to conduct fair and accurate third-party data tests free of bias that skew results. No such test exists.

Even the best-conducted tests won’t completely eliminate bias. That doesn’t mean it isn’t worth doing. Identifying bias in performance data is valuable, and if addressed, can result in better: tests, decisions, strategies and results.

Right party contact rate (RPC= right part contacts / phone data hits) is one of the most important and most widely used metrics in phone data tests for collections, but it’s not free of bias. Having been a part of data testing, on both sides of the desk (data supplier and client), I’ve come to accept that bias exists. Understanding bias that can be overcome or bias that just needs to be accepted are key steps in achieving better performance outcomes.

Here are a just a few biases that influence RPC rates.

  1. Account Selection: In some testing scenarios data suppliers will receive a different set of accounts with their own history and characteristics.   Depending on the purpose of the test this may or may not be ideal.
  2. Number of Phones Returned: RPC rates generally favor products that return a higher number of phone numbers with each account hit. There is a linear relationship between the average number of phones returned, with each hit, and RPC rates.
  3. Phones Not Dialed: Wide variances in the percentage of phones dialed between products will skew RPC rates. This is more common than what one might expect, even on purchased phones.
  4. Number of Dial Attempts: Equally, important are the number of dial attempts made on each phone number. Pre-existing dialer strategies may determine if, how and how often numbers will be dialed.  If the average number of dial attempts varies by product and isn’t accounted for, there will be bias in the results.
  5. Phone Relationship to Debtor: One-supplier returns phone numbers directly associated to the debtor. A second supplier returns phone numbers for the debtor, their: relatives, roommates, associates and employer.
  6. Timing.  Bias from timing occurs when there are variances in when test records are sent or received to/from third-party data providers or when phone numbers are dialed.
  7. Definitions of Right and Wrong Party Contacts. Management, collectors and staff may have different definitions of a right or wrong party contact. Is an answering machine for Jane Doe a right party, wrong party or unknown call result? How about a spouse, attorney or roommate? How about a payment received that results in the last phone number called being marked as a right party contact?
  8. Collector Bias. A collector may have a perception of data from a particular data supplier or see account characteristics that cause him or her to prematurely or intentionally mark the phone as bad that may otherwise be good.
  9. Technology Issue see it here. Mapping or programming errors, formula mistakes, file issues and code translations are a few of the more common technology issues that commonly skew results.
  10. Assuming un-Dialed Phones or Good or Bad. Some collection organizations will assume all phones returned from the data supplier are good or bad unless a call is made and the call outcome is updated to a good or bad phone number.

Every skip trace phone test has bias, but bias alone isn’t a reason to dismiss the test or the value of the performance data. Uncovering, overcoming and sometimes accepting bias are what make good strategies great strategies.


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