Why do some lists perform way better than others?

Question

I’ve been digging into outbound workflows lately and something I keep seeing is that not all lists perform anywhere close to the same—even when they look similar on paper.

You can have two lists with:

• similar filters (absentee, high equity, etc.)
• same skip tracer
• same outreach channel

And one gets solid responses while the other barely connects.

From what I’ve seen, a lot of it comes down to what happens after the list is built—specifically how it’s prioritized before outreach.

Most teams seem to do a good job stacking lists, but once they start calling or texting, they’re still just working top-down or oldest-first.

Curious how you guys are handling that part—

Are you seeing consistent performance across lists, or do some just hit way better than others?

Travis Goodwin · Answer

Some lists absolutely perform better than others, even with the same filters, because the data only tells part of the story. Two absentee/high-equity lists can look identical on paper, but one may have owners with real life change happening now—inheritance, tired tenants, tax pressure, relocation—while the other is just stable long-term owners with no reason to move. That’s why motivation timing matters more than filters alone. We’ve also seen recency, ownership length, equity growth, distress stacking, and even call order make a difference. A good list is important, but prioritizing who is most likely to have a reason to act right now is where a lot of the results come from.

Carson Nordmann · Answer

List performance usually comes down to the trigger event behind the list, not the filter stack. Absentee + high equity is a static condition. A filing or life event (probate, pre-foreclosure, divorce) is a reason to answer the phone this month. Same skip tracer, totally different conversation.

Chris Arnold · Answer

@Dominic Mazzarella

That’s exactly what I’ve been seeing too. On paper the lists check all the boxes, but once you actually start working them, a lot of it comes down to whether there’s a real reason behind the situation.

And like you said, that’s where the time drain happens—you can burn through a full day on technically “good” leads that just don’t have any urgency behind them.

The challenge I’ve been focused on is how to surface more of that upfront so you’re not having to discover it manually after the fact.

Not perfect, but trying to get closer to identifying those “something else is going on here” signals before the list ever gets worked.

Would be interesting to compare how you’re spotting those now vs what shows up once you actually start calling.

Dominic Mazzarella · Answer

I’ve seen the same thing. Two lists can look almost identical on paper and perform totally differently once you actually start calling.

For me, the difference usually comes down to whether there’s a real reason behind the lead. Absentee + equity by itself doesn’t tell you much anymore. If there’s something else going on with the property or the owner, those are the ones I want near the top. Otherwise it’s easy to spend a whole day working a list that technically matches the filters but has no real urgency behind it. And that's fine for a lot of folks, but it's also time consuming.

Chris Arnold · Answer

@Dominic Mazzarella

That’s a great point—especially around the subtle signals. Those are usually where the real difference shows up, but they’re not always obvious from the surface-level filters.

I like that you mentioned code violations too. That kind of municipal data seems to surface situations that a lot of standard lists miss.

The challenge I’ve seen is exactly what you’re describing—being able to identify and weigh those signals consistently before outreach without having to manually dig into every lead.

That’s the piece I’ve been focused on—trying to take those same “read between the lines” indicators and turn them into a more structured way to prioritize lists upfront.

Curious—when you’re looking for those signals now, is that something you’re reviewing lead-by-lead, or do you have any kind of system to help surface them?

Chris Arnold · Answer

@Travis Goodwin

That’s a great way to put it—especially the part about two lists looking identical on paper but performing completely differently based on what’s actually happening in the owner’s situation.

The timing piece seems to be where a lot of the variance comes from, like you mentioned. Filters get you close, but they don’t always capture who’s actually ready to act right now.

I also like that you brought up call order—feels like that’s one of the more overlooked factors even when the list itself is solid.

That’s really the piece I’ve been digging into—how to take those same signals (recency, distress stacking, ownership patterns, etc.) and translate them into a more structured prioritization instead of just working through the list.

Curious—have you guys found certain signals consistently outweigh others when it comes to actual conversations or deals, or does it shift quite a bit depending on the market?

Why do some lists perform way better than others?

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