How Better Data Can Improve Collection Performance |Will Turner, TEC Services Group | Ep. 297

There's always room to further optimize it.

Whether or not you can further optimize to twenty percent each and every time, I think is a, you know, that, that's a-- it's a big goal, but definitely a doable thing because the impact that data can have ripples, and when you figure out that process and you've got-- Right? It's like finding the right recipe.

You're gonna make the best pasta here, and when you, when you know which ingredients have to go in at which measurements and in which order, it changes the way that that dish is gonna turn out.

And I think if you can continue to tweak and understand that, what kind of testing programs do you build for that to clean out the bad data and append with more powerful, accurate information and data?

Yeah.

So it kinda depends on the data type.

Um, you know, we're doing a lot right now with debt buyers and law firms around, you know, address waterfalls, address hygiene, um, appending good addresses, just ensuring that they have a good address-

Mm-hmm

... to reduce return mail and increase process serving rates.

And so, you know, for, for that, we look at, you know, how well a company is cleansing and validating their addresses, how accurate is the information, what does their append strategy look like if they have one.

Typically, we introduce some sort of address scoring component, very similar, you know, for, for phones.

I guess the, the methodology is pretty similar.

Overall, how we evaluate how well somebody's current strategy is for whatever problem they're trying to solve.

And then the methodology we use for testing, um, kinda depends a little bit about-- Some of it i-i-is how well they can maintain control, you know, of the test.

I mean, we do all-- The, the testing me-methodologies are pretty broad, Adam, so, you know, we do everything from, you know, sending the same accounts to multiple third-party data providers.

Let every-- all the data providers get the same accounts.

We do a lot of champion challenger production testing where we'll take a portion of a client's inventory, and we'll always have them running through sorta challenger strategies, mul-multiple data providers, you know, in, in, in a sequence.

Sometimes we're testing with, you know, the, the, the bad data, the, the information that the customer knows is wrong, and then we'll demonstrate using third-party data providers the lift that we can provide, um, with information that, that they know to be bad.

We'll take fresh portfolios.

We'll take aged portfolios.

One of the, one of the things that I find interesting is that when it comes to bad data, when we think about debt collections, the older that account gets, in some ways the value of that bad data amplifies.

And what I mean by that is, you know, you take a, take an account that went from a creditor a-a-to a first party agency, and it, and it now becomes, you know, a tertiary or quad account.

The, the, the bucket of bad data increases as those accounts age, which is, you know, if that problem is resolved, it represents an opportunity for somebody to collect that money.

So, so I find, you know, the, the strategies that we de-deploy can sometimes depend on how bad the, the data problem is and, you know, the age of the account sometimes, you know, it often amplifies as the, as the account ages.