A multi-state wholesaling operation moved its outbound texting to DealMachine data and saw SMS reply rates about double what peers report, at roughly a dollar per lead, with "wrong person" replies all but gone.
The challenge
This operator runs a high-volume, multi-state acquisitions business built on SMS outreach: text property owners at scale, qualify the responses, and put the motivated sellers under contract. In that model, everything downstream depends on the quality of the data behind the texts. Wrong numbers, dead records, and mismatched owners do more than lower response; they burn the time of the lead managers working every reply.
The team had tried texting years earlier with other data and moved on. Coming back to the channel, the question was simple: could the underlying data actually produce motivated, correctly matched sellers, or would it be the same noise as before?
Blasting more texts means nothing if the replies are wrong numbers and mismatched owners.
Every junk reply costs a human qualifier’s time, the scarcest resource in the operation.
An earlier run at SMS on other data didn’t justify staying in the channel.
Success hinges on reaching the right seller profiles: senior owners, real equity, tired landlords.
The difference
The clearest signal that the data was better: response. Against the 10 to 13% reply rate the operator says peers report on cold SMS, DealMachine-sourced lists landed at 20 to 23%, roughly twice the engagement from the same channel.
What peers report vs. this operator on DealMachine data
“Everybody I’ve talked to is getting like 10, maybe 13% at best. We’re getting about 20 to 23% reply rates on our messages. The data is very accurate.”
Why it worked
The strongest proof of quality wasn’t a stat. It was the absence of a problem. Outside of standard opt-outs, the operator reported essentially never seeing a "no, that’s not me" response. The people replying were the people the data said they’d be, which is exactly what keeps qualifiers focused on real conversations.
Filtering a typical market list of about 20,000 owners down to mobile numbers still left roughly 15,000 to 16,000 contactable records. That is the difference between a list you can actually text and one that evaporates when you apply real filters.
At about a dollar per lead, the economics of the channel work before a single deal closes. The first 23,000 or so texts produced around 350 leads: cheap, high-intent top of funnel that the team can scale as fast as it can staff the follow-up.
Counting tech costs, the operator had spent only about $250 on texting when the first assignment (about $6,000) came under contract, with another contract worth roughly $15,000 in progress and more expected. Early days, but a telling return on a few hundred dollars of outreach.
The playbook
Beyond accuracy, the filters let the operator build lists around the seller profiles that convert best and skip the ones that don’t.
Owners past 70 are often solving a problem, not chasing top dollar. Easier, faster conversations than owners still optimizing for price.
Layering equity filters focuses spend on owners with the room to actually transact.
Rental owners ready to offload a portfolio already know their numbers. They are among the most motivated sellers on the list.
The same high-intent filter set reapplies market to market, so the playbook scales across states without rebuilding each list.
“The data is very accurate. Anybody that’s replied, other than stop or opt out, I haven’t seen them say "no, it’s not me."”
Metrics and quotes drawn from a recorded customer strategy call and reflect the customer’s own reported results. Customer name withheld at their request; industry disclosed with permission. Early-stage results; individual outcomes vary.
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