AI sorts your past jobs by equipment age, repair history and unsold quotes, and hands you a short list of homeowners most likely to need a new system soon.
Also written up for Plumbing: Find aging water heaters in your customer list.
You have a few years of job history with equipment ages in it and you're spending on ads to find replacement leads.
Your records don't include equipment ages or install dates. Fix that first; the list is only as good as the data.
You export your customer and job history from your field software, and AI scores each household: equipment age, repairs in the last two years, repeat calls, open estimates and plan status. You get a ranked list and a few message drafts for each group. A person still reviews the list before anyone gets a text.
Nobody has published results for this one yet, so this use case rests on my judgment. If you’ve done it, tell me how it went.
Start small: the households you'd actually reach out to in a week.
You already know who these people are. They're in your job history: the 15-year-old furnace you've repaired twice, the AC you quoted last summer, the plan member whose system is past its life. Nobody has time to dig them out, so the money goes to ads aimed at strangers.
1 to 2 hrs a week: Building the list by hand takes a half day to a day each month (4 to 8 hours). AI scoring of an export cuts that to under an hour. Rounded down. The real value is the replacements, not the hours.
Use a paid business plan that doesn't train on your data, strip anything you don't need (like payment details), and keep the file on your own computer. That's part of why I mark this one as needing help.
It's aimed. Instead of emailing everyone, you reach the 20 or 30 households whose systems are old and already giving them trouble, with a message that fits their situation.
All HVAC use cases · AIMention Toolbox · How to get your HVAC company found by ChatGPT and AI search
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