Missed-call recovery economics for home services
Turn unanswered calls into arithmetic you can defend — without inventing ROI.
If your shop tracks missed inbound calls, you already have the start of an investment decision. Most teams skip the arithmetic and jump to a vendor demo. That is how "automation will save us" becomes an unfalsifiable story.
Bound the workflow first. Missed-call recovery starts at the unanswered inbound call and ends at first successful re-contact or a defined close. Live reception and long nurture after first contact are different problems — do not blend them into one "phones AI" project.
Pull aggregated last-90-day counts you can defend: eligible missed events, attempts, successful re-contacts, outcomes booked from recovery, and never-attempted falls. Pair them with gross profit per successful outcome and the fully loaded labor on this queue — not revenue alone.
A useful screen asks whether annual economic exposure on the unattempted (or unworked) slice is material on your P&L relative to the full cost of change (tooling, setup, oversight, rework) — before you treat recovery spend as decision-ready. Exposure is not recoverable value. It only answers: is this workflow worth deciding carefully, or are you arguing about spare change?
attempt_to_book = booked_from_attempts / attempts
implied_jobs_unattempted_90 = unattempted × attempt_to_book (assumption — label it)
GP_unattempted_90 = implied_jobs_unattempted_90 × gross_profit_per_outcome
GP_unattempted_year ≈ GP_unattempted_90 × (365/90)
Fake ROI usually looks like this: take every miss, multiply by average ticket, ignore attempt rates, ignore after-hours message-only coverage, ignore adoption, and call the product "savings." Reject that model. Label every material number measured, estimate, external benchmark, or assumption — and allow the honest answer to be: do nothing, or measure first.
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