Storm restoration
Storm marketing that knows where the storms were.
Storm work rewards the company that knows first where the damage is, and punishes the one that spends everywhere and hopes. I've built the systems that tell you where to go, and where not to, before the budget moves.
Why storm work is hard to read.
Roofs, siding and gutters after hail or wind don't follow a marketing calendar. Demand arrives all at once, in the places the weather chose, with an insurance claim in the middle of every sale. Canvassing, door hangers, lead vendors and search all spike together, and every one of them looks like it worked.
The real questions are harder. Was this lead inside a storm footprint? When was the date of loss? Is this a market worth staying in once the storm crews leave, or one to step back from?
The date of loss is the detail most companies are missing. When the CRM carries it, reps walk in knowing which storm damaged the roof, managers can see how much time is left on a claim, and marketing can tell a storm-driven lead from a retail one. Without it, every storm season looks like a marketing win, and every quiet season looks like a loss. Storm revenue is real revenue, but it belongs on its own line, with its own cost to win and its own margin.
What storm companies need to know
- Where the storms actually hitBy ZIP code and by date, not by what the news covered.
- Which leads sit inside a stormSo a claim-driven lead is read differently from a retail one.
- Where not to goThe markets that look busy and never pay back the canvass.
Each of these can be answered before the money moves.
What I've built
Storm data where the decisions get made.
Storm history on every lead
An integration that geocodes every lead, pulls regional storm history, works out a date of loss by a rule the team can tune, and writes it back to the CRM record. It shipped with a kill switch, a dry-run mode and a limit on what it could write before it ever touched live data. Reps and managers see the storm context without leaving the CRM.
Every ZIP scored before you spend
A demand model that scores every residential ZIP in a company's markets on housing data and eleven years of storm history, through two lenses: retail and storm. It produces a territory view for sales, a clean target list for outside lead vendors with none of the internal scoring exposed, a summary for leadership, and a list of ZIPs to keep out of the service area. That last list is usually the one that saves the most money.
Where I help storm companies.
Storm restoration companies usually come to me at one of three moments: a new market that isn't paying back, a season where spend jumped and signed contracts didn't, or an owner who wants to know which of the storm-season channels actually produced contracts.
Each one starts with the same work. Every lead source brought onto one read, retail and storm separated, and signed contract value traced back to where the lead came from. Then a decision you can defend: which markets to feed, which to starve, and why.
Deciding where to send crews and budget next season? Bring the map to a working conversation.
Book a working conversation →Thirty minutes, and you leave with a straight read on where to look first.