🎙️ He Kills Most of His Ads in Three Days. One Has Run for Two Years.

Most publishers buying Facebook ads think the job is finding a winning ad.

Manny Reyes thinks the job is killing losers fast enough that the winners can pay for them.

Of every ad his team launches, 10–20% survive. He calls 25% "crushing it."

This week on Audience Bridge Insights, I sat down with Manny Reyes, founder and CEO of Boletín Growth, a paid growth agency for newsletters.

He's helped acquire more than 5 million subscribers over six years of media buying, including time at Morning Brew.

His team runs Meta campaigns for B2C authors and for B2B newsletters as niche as Home Pros, which goes to HVAC owners and managers.

You can watch the entire podcast now or scroll down to get the full breakdown.

Here's the thing about a $1,000 test. It almost always looks great.

The CPAs come in low, the data looks like it's crushing, and you decide it's time to scale.

So month two you spend $5K. Month three, $10K. And the numbers look nothing like what you tested.

That's why Manny won't take on a client spending less than $2,000–3,000 in month one.

It isn't about his fee.

A thousand dollars just doesn't buy enough data to tell you what happens at real spend.

What he wants is $3,000–5,000 in month one.

At that sample size, he's confident the CPA he finds will hold when spend moves to $8K–10K a month, and from there he scales budgets 20–30% at a time.

If you only have $1,000, his advice is to skip the agency and run it yourself.

Just don't treat what you learn as a forecast.

A cheap test isn't a small version of a real campaign. It's a different campaign.

Start with the win rate, because it changes how you think about everything else.

So if you're launching two ads a week and hoping both work, the math is against you before you start.

When neither hits, you've burned a week or two of spend and learned almost nothing.

Manny's answer is volume.

His team produces dozens, sometimes hundreds, of new ads a week across clients, and more than 80% of the spend goes to video.

That includes text over a moving background, since it runs in the Reels placement.

Statics win in bursts, but he's never seen one hold the top spot for longer than three to six months.

And the winners are often ugly. He doesn't fight it.

If his team drifts away from what works just to try something prettier, the client is the one paying for the experiment.

Volume is also why his UGC has moved to AI.

Think about the trade.

$250 buys you one creator, one video, and your fingers crossed.

The same script across a dozen AI actors buys you a dozen shots at a winner.

He asks clients first, and some say no, which is fine.

Founder ads still work, just differently.

The writer records each line a few times, the team stitches the best takes together, and nobody has to memorize a script or read it into the lens.

Then every ad gets the same short leash.

Sometimes he can tell within 48 hours.

The hard max is three to five days, judged on where Meta is putting the spend, click-through rate, and what that works out to in CPA.

When 80% of what you make fails, making more ads is the only way the math works.

Once an ad wins, the question is how fast you can push it. Manny's answer is slower than you want.

Go faster and you reset Meta's learning phase. The algorithm starts over every day, and the CPA you were scaling disappears.

So he stacks four to six of those increases, roughly doubling spend over two weeks, then lets it settle and watches whether the CPA holds.

If it keeps climbing, the extra budget goes to other winners instead.

The structure behind it is simple.

Every account runs two campaigns: a main campaign with 80–90% of the budget on proven winners, and a testing campaign with the rest, at $40–50 a day per test.

Go lower than that and you still won't have enough conversions to call a winner by day three.

Here's the part most people miss.

When a winner "graduates" into the main campaign, Meta treats it like a new ad, and the CPA you saw in testing may not carry over.

So Manny leaves the test version running, watches both for three to five days, and backs whichever one holds.

Usually that's the main campaign.

Not always.

While that's happening, his team is already building a V2 of the winner with the same format and hook, so something is ready when the original fades.

Scale slower than you want to. Push too hard and the algorithm starts over./

His longest-running winner has been live for more than two years.

That sounds like a creative miracle. It isn't.

What happened is the ad collected thousands of likes and comments along the way.

At this point it looks like a popular post, not an ad, and that social proof keeps feeding its performance.

That only works with a huge audience, though.

On B2B, Manny has never had an ad last past four to six months.

The audience is small enough that the same people see it over and over, and returns drop.

B2C can run 6–12 months or longer, and once an ad makes it past six to eight months, it will likely run another six.

Think about the difference in scale.

An author like James Clear has a potential audience in the tens of millions.

Home Pros has maybe 40,000–60,000 ideal readers in the US.

Same platform, completely different expectations.

Your audience size decides how long an ad lasts before you write a word of copy.

Here's a question most publishers can't answer: of the people Facebook brought you last month, how many are actually your reader?

CPA can't tell you.

It tells you what a subscriber cost, not who you bought.

That's why Manny treats first-party data as the bare minimum on B2B: a survey right after sign-up, tied to the UTM that says which ad brought each subscriber in.

Jacob Donnelly has been making that point for years.

It matters on B2C too. "60% of my list earns over $150,000" is one data point, and it changes a sponsor conversation.

What's changed is how fast you can use it.

Manny's team plugs AI into beehiiv's MCP and asks things like: of the subscribers who said they're HR C-suite, which came from Facebook, and how does each ad set perform?

They score every ad set on clicks, opens, engagement and survey answers together.

Fourth or fifth place gets its budget cut. A strong one gets 15–20% more for two weeks, and then they ask again.

That analysis used to take half a day to a full day per client.

Now it's about ten minutes, every client, every week.

I asked him about a case I'd been thinking through.

You're targeting senior employees, and one ad is pulling mostly juniors at a cheap CPA.

He'd just run into exactly that.

Within two to three days, the survey data showed more than half the spend going to the wrong ad and ad set.

He cut it, even though the ads left running cost more per subscriber.

The cheap one was never cheap.

It was just buying the wrong people.

CPA tells you what a subscriber cost. First-party data tells you whether you bought the right one.

I asked Manny for a straight yes or no on Facebook lead forms. No.

The problem is the autofill. The form drops in whatever email the person used to open their Facebook or Instagram account.

There's no friction, so the conversion rate looks incredible:

70–80%, against 40–60% for a landing page. Your CPA drops.

And you're paying for addresses nobody checks.

He'll admit some well-run media companies use them, maybe 20–25% of established operators, and it may be working for them.

The one exception he'd make is a call center or SMS team working the leads, where the phone number is probably real.

I saw this play out last month.

A publisher's welcome emails were opening at around 5%, and I kept digging into deliverability until I found the lead forms.

I told him to stop and send the same Facebook traffic to a landing page instead.

New sign-ups started opening around 45%.

Now, you know how I feel about open rates, so read that jump for what it is.

It isn't proof of engagement. It's proof the emails started getting through.

A welcome email opening at 5% is landing somewhere nobody looks, because the auto-filled addresses were abandoned inboxes dragging down sender reputation for everyone who signed up alongside them.

The number that proves those new subscribers are people is the click.

Manny has 8–10 versions of the same story from the past year.

They switch to a landing page, opens go up, and CPAs barely move.

The creative was never the problem. The email addresses were.

A 70% conversion rate is a cheap CPA on addresses nobody reads.

Ask Manny for the biggest myth in paid newsletter growth and he doesn't hesitate: that any newsletter can get under $1 or $2 a subscriber.

Efficient depends on what a subscriber is worth to you, and Manny works that backwards from the one or two income streams that actually pay.

Most newsletters running three or four get just 10–20% of revenue from the rest.

For a subscriber who engages and converts, efficient might mean $2–4, or $4–5 and up.

Local newsletters can hit $0.35, for about two to three months, until the market runs out.

Payback runs on the same logic.

He's never believed anyone claiming 24 hours.

Thirty days is realistic if you're as dialed in as Matt Paulson, and that took years of infrastructure.

About 20–25% of his clients sell an offer between sign-up and the first newsletter.

At 0.5–2% conversion, that recoups 30–40% of ad costs in the first month, with the rest coming in over three to six months.

Channel choice is the same math again.

LinkedIn runs $10–30 a subscriber because it's priced for SaaS companies happy to pay $300 a lead.

Unless each subscriber is worth $50–100 to you over a year or two, it doesn't work.

Ads in other newsletters are the opposite.

At Morning Brew they brought in the most engaged subscribers of any channel, but it's a grind, every placement is a bet, and there are only so many newsletters to buy from.

Stop asking how low your CPA can go. Ask how high your income per subscriber lets you pay.

So what should you fix before spending a dollar?

Manny's answer was three pages: a sign-up survey if you're B2B, a thank-you page, and a welcome email.

Having them isn't enough, though.

Think about the chain.

A weak welcome email gets fewer clicks and replies.

Fewer clicks means fewer engaged readers. And that shows up in your deliverability long before it shows up in revenue.

So here's Monday morning.

Pull last month's paid sign-ups by UTM source and answer two questions: how many clicked the welcome email, and how many fit your target reader?

If you can't answer either one, that's the fix, before the next dollar goes out.

1. Every lever Manny pulls depends on knowing who's real.

Look at what actually moved his numbers: the survey, the UTM cohorts, killing lead forms, cutting the cheap ad set that was buying juniors.

None of it was about creative. All of it was about whether the subscriber on the other end was the person he paid for.

If I were building his scoring rubric, clicks would carry the most weight and opens the least.

2. Two guests, two verdicts on LinkedIn, same math.

Andy Mackensen told me LinkedIn costs more per subscriber and less per engaged user.

Manny says $10–30 a subscriber doesn't work unless each one is worth $50–100.

They can both be right, because the answer depends on cost per engaged subscriber against what an engaged subscriber earns you.

If you're only tracking CPL, you can't tell which one applies to you.

3. Patience is the strategy.

Kill in three to five days. Scale 15–20% a day.

Keep the test version running and build the V2 before the original fades.

I've blown up ad campaigns myself, and it was always impatience: scaling too fast, or not having creative ready when a winner died.

Manny has a rule for both.

📺 Watch the full conversation

🎧 Listen to the Podcast

See you next week,

Chris Miquel

P.S. Manny's lead form story is the one I'd tape to the monitor. The cheap CPA was buying addresses that never became readers.

That's the problem Smart Lead is built to avoid. Leads come from real click behavior across our own newsletter network, matched against your most engaged subscribers, delivered by ISP group, with full event data flowing back so you can see which ones engaged.

If you want to know what your paid sign-ups really cost per engaged subscriber, book a call.