Industries · Home services
Value-Based Bidding for Home Services: Optimise Around the Value of the Job
A kitchen remodel inquiry and a small repair both count as one lead, yet one could be worth sixty times the other. Use your own job history to estimate what each new inquiry is likely to bring in, so the ad platforms can learn which jobs to go after.
Why lead counts fall short
Why a count of inquiries misses what your jobs are worth
Every inquiry looks the same in a lead report. In your job history they are nothing alike: a full roof replacement, a kitchen remodel and a leaking gutter carry different close rates, different ticket sizes and, often, different margins.
Bidding on inquiry volume pushes spend towards whatever produces the most forms at the lowest cost, which in many trades means small, quick jobs. That can be the right mix, but it should be a choice made from your numbers rather than a side effect of counting.
Close rate and ticket size pull in different directions
Small repairs often close quickly and often; big projects close less often at many times the revenue. Expected value weighs both.
Revenue is not profit
If some job types carry much better margins, revenue alone over-rewards the rest. The product can price on profit with one margin share for the whole business; it does not model a separate margin per job type.
Service area and timing matter
An inquiry outside your core area, or one with no start date in sight, may convert very differently. The model tests those fields when your form captures them.
The funnel
From inquiry to job won
- Inquiry
- Estimate booked
- Quote issued
- Job won
Known when the lead arrives
What a new lead can be priced from
- Requested service
- Project scope
- Service area or postcode
- Desired start date
Learned later
What the history is trained on
- Whether an estimate was booked
- Whether a quote went out
- Whether the job was won, and for how much
The job type and scope on the inquiry form are what price a new lead. The estimate, the quote and the final invoice are what the history is trained on.
How it works
From your sales history to what the ad platforms see
- 1
Bring in your history
A CRM export or a HubSpot connection: leads with their outcomes, won, lost or still open.
- 2
Find what relates to value
Each field known when a lead arrives is tested against your closed deals. Only the ones that clearly moved outcomes are kept.
- 3
Estimate each new lead
Its expected value: how likely leads like it are to become customers, times what those customers were worth.
- 4
Send the value
To Google Ads or Meta, as a conversion value on the lead. Sending changes no bid by itself.
- 5
Judge it on real outcomes
Compare closed deals before and after your campaigns start optimising on the values, never the values we sent.
Worked example
Three leads, three different values
Expected lead value = the estimated chance of becoming a customer × the expected value of that customer.
Value basis: Job revenue. The amount recorded on the won job. Set a margin share in the product and the same estimate is expressed as profit instead, using one margin for the whole business.
Kitchen remodel
- Scope: full remodel
- Start: within three to six months
12% × $38,000
$4,560
estimated value of this lead
Roof replacement
- Scope: full replacement
- Start: within a month
30% × $14,000
$4,200
estimated value of this lead
Gutter repair
- Scope: single section
- Start: as soon as possible
45% × $600
$270
estimated value of this lead
Illustrative example, not customer results.
The remodel and the roof end up close in expected value by different routes: one is a big job that rarely closes, the other a mid-sized job that closes often. The repair is worth a small fraction of either, however quickly it books.
How the product actually estimates this
It does not score each lead on its own. It starts from your overall close rate times your average won deal, with any deal above three times the median counted at that cap, then applies a multiplier for each field that clearly moved outcomes in your history, and rescales so the average estimate matches what your leads were actually worth. Fields that do not clear the evidence thresholds are left out and listed. The formula above is the same idea in one line.
What data you need
Your CRM already has most of it
- Inquiry dateWhen the lead came in, not when the job was invoiced.
- Won, lost or openJobs quoted and lost are as useful as jobs won.
- Job value on won jobsThe invoiced or contracted amount. Decide whether you want revenue or profit before you start.
- Fields from the inquiry formRequested service, scope, area and desired start date, as the customer gave them.
- Matching informationGoogle's click ID or the customer's email for Google; email or phone, plus Meta's cookies from your site, for Meta.
What matters most is reliable outcomes and enough of them. The report checks your own file and names anything it could not price, rather than applying a one-size minimum.
How activation works
Estimating, sending and optimising are three steps
The product estimates each lead's value and sends it. Your campaigns use it only once you set them to optimise for value, a change you make in the ad platform, and the platform decides when an account qualifies.
Google Ads
Sends each inquiry's estimated value to a conversion action in Google Ads, matched on the click ID or on email. Your campaigns use it only once they bid on value, for example with Maximize conversion value.
Switching a live campaign, step by stepMeta
Sends a ValuedLead event with the value through the Conversions API. Website-form ad sets bid on values once they qualify for value optimisation; instant-form ad sets can learn from your pipeline stages through Conversion Leads.
How the Meta route worksThe full method, from pricing a lead to measuring the result, is in the complete guide. Leads that reach your CRM without a click ID are covered in the click ID guide.
How to judge success
Measured in outcomes, not in the values we sent
Revenue per lead
Won job value divided by the inquiries that produced it, before and after the switch.
Cost per won job
Ad spend against jobs won, not against form fills.
Job mix
Whether the share of larger jobs moved, which shows within weeks, long before the revenue does.
Reported conversion value going up is not the result. The result is what your job book shows: the product compares won jobs before and after the switch, with leads from other sources as a control.
Questions
Home services, specifically
Most of our revenue comes from a few big jobs. Won't they skew everything?
They would without a cap. The product caps any deal above three times your median won job at that level, shows the cap and lists every job it clipped, so one exceptional project cannot teach the ad platforms to chase its lookalikes.
We are seasonal. Does a winter model price summer leads badly?
It prices from the history you give it, so include at least a full year if you have one. A model fitted only on the slow season will not know the busy season; refitting after a season changes is a deliberate step you take, never something it does silently.
Should we optimise for revenue or profit?
Whichever matches how you run the business. The product prices on revenue by default and can express the same estimates as profit using one margin share you set. If margins differ sharply by job type, that single share is a simplification and worth knowing about.
Our quotes live in one tool and jobs in another. Which export do we use?
The record that follows an inquiry to its outcome: the one with the inquiry date, the job type and the won or lost result with its amount. If that spans two tools, join them on the customer before exporting; the product reads one file.
We added a new service this year. Can it be priced?
Only once enough of those jobs have resolved to measure. Until then the model prices those inquiries from what is known in general, and the report names any service it could not price on its own.
Check whether your lead data is ready
Upload a CRM export or connect HubSpot. In about five minutes you see which of your fields relate to value, what new leads would be estimated at, and whether your history is enough to send. The file is read in your browser; you are asked for a name and work email before the full report.