Because Local Services Ads charge per lead, and because a meaningful share of leads turn out to be off-target, credits are how you avoid paying for opportunities that were never real. But the way credits work changed substantially in 2024. If your understanding of "disputing a bad lead" is a few years old, it's out of date. Here's how the system works now.
The end of manual disputes
For years, advertisers recovered spend by manually flagging individual leads as invalid — logging in, finding the lead, submitting a dispute, and waiting for a decision. That manual dispute process was retired around July–August 2024. You can no longer hand-dispute each lead the old way.
In its place, Google introduced a machine-learning auto-credit system paired with a "Rate this lead" survey. Rather than you arguing each case, Google's model assesses leads and issues credits automatically, while your ratings feed the model that makes those decisions.
How the auto-credit system works
The mechanics, as commonly understood, run like this:
- Google's model evaluates leads, typically within about 72 hours.
- Credits it determines are warranted are generally posted within about 30 days.
- Alongside this, you're asked to "Rate this lead" — feedback that both helps recover the immediate lead where appropriate and trains the system over time.
The shift matters for how you operate. Under the old system, diligence meant working a dispute queue. Under the new one, diligence means rating your leads promptly and honestly, because that's now your input into a mostly automated process. Consistent, accurate rating is the lever you still hold.
What qualifies — and what doesn't
Not every disappointing lead earns a credit. Genuinely invalid leads — those that don't represent a real, serviceable opportunity — are the target of the system. But some reasons explicitly don't qualify:
- Job-type mismatch and geographic mismatch are examples of what is not creditable in the way you might hope — the system treats these categories specifically.
- A customer who was a real prospect but simply chose a competitor is not a bad lead — that's the auction working, and it doesn't earn a credit.
Certain verticals are also excluded from the credit system entirely — notably healthcare and tax. If you operate in an excluded vertical, credits aren't part of your economics, which raises the importance of tight targeting and fast qualification.
| Situation | Typically creditable? |
|---|---|
| Spam / clearly fake contact | Often yes |
| Service you don't offer (per system rules) | Handled by the model — not guaranteed |
| Customer chose a competitor | No |
| Real prospect you failed to book | No |
| Any lead in healthcare / tax verticals | Excluded from credits |
How much is actually recoverable
It's important to set realistic expectations. Third-party analyses tend to estimate recoverable spend at roughly 6–7% of total LSA spend. That's meaningful — on a healthy budget it adds up — but it's not a mechanism that makes bad targeting free. Credits are a backstop for genuinely invalid leads, not a rebate on every lead that didn't book.
This framing keeps priorities straight: credit recovery is worth doing diligently, but it will never rescue an account that's fundamentally paying for the wrong leads. The bigger wins come upstream — a tighter service area, accurate service selection, and fast qualification — which reduce how many bad leads you receive in the first place.
Why honest rating matters beyond your own account
The "Rate this lead" survey isn't just paperwork — it's training data. Rating leads accurately helps the model get better at recognizing invalid leads, which benefits you over time. It also intersects with compliance: because the system rewards honest feedback, gaming it (rating good leads as bad to chase credits) is both against the spirit of the program and unlikely to work against an ML system watching for patterns. Straightforward, honest rating is the sustainable approach.
Building credits into your routine
Practically, effective credit management now looks like this:
- Rate every lead promptly, ideally within the window where the assessment is happening.
- Be honest and specific — flag genuinely invalid leads, leave real prospects alone.
- Track your credits over time so you know your real net cost per lead, not just gross.
- Treat persistent bad-lead patterns as a targeting problem to fix upstream, not just a credit queue to work.
The move to auto-credit took the busywork out of disputes but put a premium on consistency. The advertisers who benefit most are the ones who rate every lead, every time, and who use the patterns they see to tighten their targeting — turning credit data into a feedback loop rather than just a refund.
Frequently asked questions
Can I still manually dispute LSA leads?
No. Google retired the manual lead-dispute process around July to August 2024 and replaced it with a machine-learning auto-credit system paired with a Rate this lead survey.
How long do LSA lead credits take?
Google's model typically assesses leads within about 72 hours, and credits it determines are warranted are generally posted within about 30 days.
Which LSA leads do not qualify for a credit?
Job-type and geographic mismatches are treated specifically, and a customer who simply chose a competitor is not creditable; the healthcare and tax verticals are excluded from the credit system entirely. Third-party estimates put recoverable spend around 6 to 7 percent.