When lead data went bad, Tony Cisneros’s roofing operation watched its weekly booked appointments collapse by more than half. Switching to DealMachine reversed it almost overnight, with no extra headcount.
The challenge
Tony Cisneros runs a high-volume outbound operation that books roofing and storm-restoration appointments across Texas and the Sun Belt. For a business like his, the list is the engine: 115 callers dialing all day are only as productive as the accuracy of the records in front of them.
Earlier this year, appointment volume cratered. A team that had reliably booked roughly 1,300 to 1,400 appointments a week fell to about 600. The culprit was not the callers. It was the data. Even the pricey list providers were serving wrong numbers and inaccurate records, and every bad record meant wasted dials, wasted payroll, and lost time.
Weekly appointments dropped from about 1,400 to about 600 as list accuracy deteriorated.
Premium providers still delivered wrong numbers and inaccurate owner records.
Callers spent hours grinding through numbers that went nowhere. Pure labor cost, no return.
Making up the lost output the old way would have meant adding 30 to 40 more people.
“Before, we were averaging about 1,300 to 1,400 appointments a week. The data got so bad we dropped to 600. We got on with your data and now we’re back to about 1,400 a week. It’s been immediate.”
The results
Nothing changed about the size of the team or the hours they worked. The only variable was the quality of the data feeding the dials, and switching to DealMachine took weekly appointments straight back to peak, immediately.
Before DealMachine vs. after. Same 115-person team.
Why it worked
“With inaccurate information, I’d have to hire an extra 30 to 40 people just to make up for the lost time. With this, it’s made us so much more efficient.” Better records meant the existing team recovered the lost output on its own, turning a payroll problem into a data decision.
DealMachine’s filters let Tony’s team zero in on specific hail-damage swaths and property characteristics, so dialers work the homes most likely to need a roof instead of spraying an entire metro. Right list, right street, right conversation.
Tony had been paying an offshore team of list scrubbers to build his commercial data by hand. DealMachine’s commercial coverage and skip tracing does that work automatically, cutting roughly five payrolls of manual effort and freeing that budget to buy more data.
Across his team accounts, Tony pulls up to 450,000 fresh leads a month, with de-duplication so teams working nearby markets never burn the same records twice. Results were strong enough that he immediately maxed out and added more accounts.
“I’m seeing a huge ROI already. The data’s so good, it eliminates payroll and makes my team more efficient, so we’ll just keep rolling it.”
Figures and quotes drawn from Tony Cisneros’s recorded testimonial and DealMachine onboarding call, and reflect the customer’s own reported results. Shared with permission.
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