Pro Pixel Labs Team
4 min read
This is a hypothetical worked example, not a client result. Every number below is an assumption chosen to show how to do the math. Replace them with your own.
The business: an imagined HVAC and plumbing company with six technicians, roughly 60/40 HVAC and plumbing work. After-hours contact is handled the way most similar businesses handle it: an answering service takes messages, the owner checks them in the morning, and the team calls back when they can.
The goal of this exercise is to show how you would measure the gap, model the change, and decide whether it is worth acting on.
Step 1: Baseline Your Own Numbers
Before any build, pull 60 days of contact data across every channel: phone, web form, and Google Business Profile messages. Then answer four questions.
- How many contacts per month? Our example: 42.
- How many arrive after hours (before 7am, after 7pm, weekends)? Example: 37%, or about 15 per month.
- How many of those become booked jobs today? Example: 3 of 15 (20%). The rest leave voicemails, submit forms nobody sees until morning, or message the business profile and move on.
- How long until someone responds? Example: about 11 hours on average.
Step 2: Size the Gap
Assume an average job value of $437 (a blended figure between HVAC and plumbing tickets) and that 60% of after-hours contacts would book with a same-day response.
- Missed after-hours contacts: 15 − 3 = 12 per month
- Jobs lost: 12 × 60% ≈ 7 per month
- Revenue lost: 7.2 × $437 ≈ $3,100 per month
Your assumptions will differ. The point is that the gap is calculable before you spend anything.
Step 3: Model the Change
Suppose AI intake lifts after-hours capture from 20% to about 85%, with emergency keywords (flooding, no heat, gas smell, burst pipe) triggering an immediate alert to the on-call technician.
| Metric (per month) | Before | Modeled after |
|---|---|---|
| After-hours contacts | 15 | 15 |
| After-hours contacts captured | 3 (20%) | 13 (85%) |
| Jobs booked at a 60% conversion | ~2 | ~8 |
| Average response time | ~11 hours | under a minute |
The difference is roughly 6 additional jobs a month, about $2,600 in revenue at $437 per job. Compare that with the monthly cost of the system and the result is a simple payback calculation.
Notice what the model does not claim. It does not assume more contacts overall, a higher close rate, or any boost from reviews. Those can happen, but they are upside, not the base case.
What Won’t Change
Conversion among captured leads stays where it is. AI intake gets more people into a conversation. It doesn’t make them more likely to buy. Close rate is still a human skill.
Phone calls still need a human. If a large share of your inbound contacts are direct calls, an intake system for web, chat and messaging won’t cover them. Plan for phone coverage separately.
The on-call technician still has to close the emergency. The alert is fast, but if the technician can’t call back within minutes, the customer books someone else. Clear on-call protocols matter regardless of how fast intake is. For what that alert looks like, see the burst pipe scenario.
What to Track Over 90 Days
Pick two or three of these before you start, and record the baseline:
- After-hours contacts captured, as a percentage of all after-hours contacts
- Average after-hours response time
- Jobs booked from AI-initiated conversations
- Emergency alerts fired, and how many became booked jobs
- Staff hours spent on morning callbacks
Review them at 30, 60 and 90 days. If the numbers aren’t moving, find out why before adding more.
The Starting Point
The AI Readiness Audit does the baseline in Steps 1 and 2 for your business: your after-hours contact volume, capture rate, and estimated revenue gap, before any build commitment. For fuller month-one numbers, see the HVAC example with 31 after-hours leads and the plumbing example with 22 jobs.