€7 Per Lead on Meta Ads. Here's What Actually Changed.
Most Meta accounts don’t have a strategy problem. They have a homework problem.
Nobody’s auditing the stale audiences from 2021. The retargeting campaign eating 40% of budget at 3x average CPL stays alive because nobody looked. The lookalike audiences built on outdated pixel data keep running because stopping them would require admitting they haven’t worked in two years.
January 2026. An Italian furniture brand I’ve worked with for years. 101 leads. €7.65 average CPL. Best adset at €4.38. Here’s what the homework found.
Key Takeaway: Killing lookalike audiences and switching to Broad + Interest targeting on Meta Ads dropped CPL from double digits to €7.30 — with the best adset hitting €4.38. The change worked because Meta’s algorithm performs better with fewer constraints, especially on smaller budgets with thin pixel data. AI-assisted weekly audits caught hidden budget drains (stale audiences, zombie retargeting) that manual management missed.
The Account Was Stuck in Its Own History
When I sat down to dig in, the first thing I did was pull two years of data. Not to analyze it — just to understand the shape of it.
What I found was an account that had been run by gut feel and inertia. Lookalike audiences built off a pixel that had seen better days. The same campaigns running month after month because nobody had a reason to stop them. CPL bouncing around — some months decent, some months you’re paying way too much for leads that go nowhere.
The account wasn’t broken. It was just… frozen. Optimizing inside a logic that had stopped working.
The instinct when that happens is to add things. New creatives, new copy angles, new audiences. I did the opposite.
The Switch: Broad + Interest Over Lookalike
This is the part that sounds too simple to be real, but it’s what moved the needle.
Lookalike audiences are built on who already converted. If your pixel data is old or thin, your lookalikes are built on a distorted picture. You’re cloning the wrong person, or cloning someone from 2021 who doesn’t reflect today’s buyer.
We killed the lookalikes. Moved to Broad + Interest targeting.
Broad means you let Meta’s algorithm figure out who to show the ad to, with minimal constraints. Interest means you layer in relevant signals — in this case, design and home improvement communities on platforms like Houzz.
The “Houzz + Design” adset ended up at €4.38 CPL. That’s the best result in the account’s history.
Why does this work? A few reasons. Meta’s algorithm has gotten very good at finding buyers when you give it room. Lookalikes add a constraint that can actually narrow the algorithm’s search in unhelpful ways, especially on smaller budgets. And interest targeting, done right, gets you in front of people actively thinking about the category.
What AI Actually Does Here (And What It Doesn’t)
I want to be specific about this because there’s a lot of noise around “AI for ads.”
I use Claude Code connected to the Meta Ads API and GA4 directly. Not screenshots. Not CSV exports I analyze manually. The actual raw data — adsets, spend, CPL, frequency, audience overlap, everything.
Think of it like this. Before, managing an account meant doing your own homework and then making decisions. Now the homework is done before I sit down.
Every week, the system pulls the data, runs analysis, and gives me a report: what’s working, what’s not, where budget is being wasted, what to test next. I read it. I decide what to act on.
Every decision is still mine. The AI doesn’t touch the account. It reads, it reports, it suggests. I make the calls.
What the AI Actually Caught
Three things the AI surfaced faster than manual review would have.
Stale audiences from 2022. There were custom audiences in the account that hadn’t been refreshed in years. Still active, still eating impressions, still affecting lookalike quality. Nobody had flagged them because nobody was looking at that level of detail systematically. Gone.
Two underperforming adsets with no path to improvement. Not just “low ROAS right now” underperformers — adsets that had been given enough time and budget to prove themselves and hadn’t. The data made it obvious. Killed them. Budget redistributed.
A retargeting campaign eating 40% of the retargeting budget at 3x average cost per lead. One ad. Forty percent. Three times the cost. This is the kind of thing that hides in plain sight when you’re managing an account by looking at top-line numbers. The granular pull surfaced it immediately.
That last one alone probably funded the difference.
The Numbers
January 2026:
| Metric | Result |
|---|---|
| Total leads | 101 (target was 70) |
| Average CPL | €7.65 — best month on record |
| Best adset CPL | €4.38 (“Houzz + Design”) |
Same budget. Different approach. That’s it.
The Honest Part
Q1 is seasonally strong for furniture. January converts well in this category. Some of this result is the season doing its job — not the audit.
Two months is also a small sample. I can’t isolate the AI effect from the targeting switch cleanly. The two changes happened together. I’ll know more in June.
What I can say with confidence: the combination of killing stale audiences and switching to Broad + Interest produced results I wasn’t seeing before. Whether it holds through summer is a different question.
The Actual Takeaway
Most accounts don’t fail because of bad creative or wrong audiences. They fail because the homework doesn’t get done consistently.
The AI layer doesn’t replace judgment. It makes the boring homework automatic — so when you sit down to make decisions, you’re working with complete information instead of guessing.
I still make every call. But I walk in with all the data.
That’s the shift. Not AI ads. AI-assisted prep.
This client is an Italian furniture brand — not named by agreement. All numbers are real. Q1 seasonality caveat stands.
Frequently Asked Questions
Does switching from Lookalike to Broad targeting always lower CPL on Meta Ads?
Not always. It depends on pixel data quality and budget size. If your pixel has strong, recent conversion data, lookalikes can still work. In this case, the pixel was built on thin and outdated data — the lookalikes were cloning the wrong buyer. Broad + Interest gave Meta’s algorithm more room to find actual prospects. The smaller your budget, the more this matters.
How does AI help manage Meta Ads campaigns?
In this setup, Claude Code connects to the Meta Ads API and GA4 directly. It pulls raw data weekly — adset performance, audience overlap, frequency, CPL by segment — and generates a report with specific recommendations. The AI reads and reports. Every decision (kill an adset, shift budget, change targeting) is made by a human. It replaces the manual homework, not the judgment.
What’s the difference between Broad and Interest targeting on Meta?
Broad targeting gives Meta maximum freedom to find your buyer with almost no constraints. Interest targeting layers in signals — like users who follow design communities or home renovation topics. You can combine them. In this account, the best results came from Interest targeting around Houzz and interior design audiences, not pure Broad.
How often should you audit Meta Ads audiences?
Weekly, minimum. Stale custom audiences, zombie retargeting campaigns, and budget concentration in underperforming adsets are problems that compound silently. This account had audiences from 2022 still running. A systematic weekly audit — automated or manual — catches these before they drain budget.