CR Deals Cincinnati is a real estate wholesaling operation — they find distressed properties, put them under contract, and sell the contracts to cash buyers. The business runs on lead volume and speed. The problem was that their back-office was almost entirely manual, and it was costing them more than they realized.
The Problem
When we audited the operation, we found four separate manual processes that were consuming significant time every week:
- Lead intake: Inbound seller leads from the website, direct mail, and driving-for-dollars apps were being manually copied into a spreadsheet. One person spent about 2 hours a day just transferring contact information.
- Follow-up: After initial contact, follow-ups were tracked in the same spreadsheet with color coding. Leads regularly fell through the cracks when the spreadsheet got messy or the person managing it was busy.
- Buyer list management: Their cash buyer list was in a separate spreadsheet. Matching new deals to relevant buyers required manually scrolling through hundreds of rows.
- Contract and document routing: When a deal was accepted, documents were sent manually by email. Tracking who signed what required checking email threads.
We added up the time and costed it at the loaded rate of the two people handling these tasks. The total came to approximately $52,000 per year in labor allocated to work that shouldn't require a human at all.
What We Built
Automated lead intake: Every lead source — the website form, the direct mail landing page, and the driving-for-dollars app via Zapier — now feeds directly into GoHighLevel. No manual entry. Each lead is automatically tagged with its source, assigned a pipeline stage, and triggers a follow-up sequence.
Follow-up sequences by lead status: We built four automated sequences: initial contact (immediate text + email), no response follow-up (days 2, 5, 10, 21), appointment confirmation, and deal-in-progress. The right sequence fires based on what stage the lead is in. The team's job is now to respond to interested leads — not to remember to follow up.
Buyer matching automation: When a new deal is added to the system, it automatically identifies buyers on the list whose criteria match the property (geography, price range, property type) and sends a deal alert to that subset. No more manual matching.
Document automation: Accepted deals now trigger an automatic document workflow via PandaDoc — the contract is generated with the property details pre-filled and sent for signature automatically. Signed documents trigger a notification to the relevant team members.
The result: The two employees who were spending the majority of their time on manual data work now spend that time on outreach, relationship management, and deal negotiation. The operation handles 40% more leads with the same headcount.
What We'd Do Differently
A few things we'd change if we started this project today:
Start with the follow-up sequences, not the intake. We built intake first because it felt like the logical starting point, but the follow-up system generated more immediate ROI. Leads were already in the system — they just weren't being followed up consistently. We'd sequence the build differently next time.
Build the reporting dashboard earlier. We added a reporting layer — lead source performance, conversion rates by stage, average time-to-close — in week three. Having that data from week one would have let us optimize the sequences faster.
Invest more in the buyer side. The seller-side automation was the primary focus, but the buyer matching system could go further — personalized deal alerts based on past purchases, automated relationship nurture for high-value buyers. There's more to build there.
The Takeaway
This project cost $4,200 to build and paid for itself in about six weeks. The ongoing cost is roughly $180/month in software. The annual return, conservatively, is over $50,000 in reclaimed labor — not counting the revenue impact of higher lead conversion from consistent follow-up.
The best automation projects aren't technically impressive. They're just solving a clearly defined, expensive problem with a reliable system. This was one of those.