Lead planning model · Residential landscape
Start from the revenue goal, work backward to the qualified leads required, then build a channel plan that produces them. Every channel rate is a published benchmark or a flagged assumption, replaced by the client's own numbers after one season.
Dashed fields are benchmarks. Switching scenario resets them. Grades: A large multi-account dataset · B vendor or agency data · C assumption, calibrate first.
A qualified lead is inside the service area, has a project that fits the company's services, a budget at or above the minimum job, a start date within 6 months, and the decision-maker on the call. Anything less is a raw lead.
Keeps organic social, SEO, AI search, shows, referrals and partners as entered, since you can't buy more of those on demand. Then sizes Google, Meta, promoted posts and door hangers to close the remaining gap, cheapest cost per qualified lead first, up to each cap. Channels switched out of the plan are skipped. Everything else on the page recalculates live as you edit.
| Channel | Raw leads | Qualified | Spend (CAD) | Cost / qualified | Evidence |
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Saves the current plan as a client-ready report: goal math, verdict, channel plan with every rate and its evidence grade, the follow-up conditions, and sources, as a letter-size PDF. The CSV holds the channel table for a spreadsheet.
The model is only as good as the follow-up behind it. These are the conditions the numbers assume. Without them, cut every channel's output in half.
Most benchmarks are US data in USD; paid-media costs are converted with the CAD/USD field, which is an approximation since Canadian auctions price differently. Treat everything as a starting point until the client's own data replaces it.