Consciously raising “four kids”: my two sons and two startups | Follow me for valuable insights on how to lead your marketing strategy and team effectively

We're a senior paid media team. We help B2B companies generate qualified leads and DTC stores drive profitable sales through Google & Meta Ads.

How we work:

- One clear plan: keep, stop, test next.
- Creative pods: 3–4 angles, weekly trims. Winners get budget.
- Audience targeting that converts: remarketing, lookalikes, and cold prospecting.
- Spend guardrails: start small, scale only when performance holds.
- Simple reporting: 2-minute weekly notes tied to results.

Proof:

- B2B SaaS: Cost per SQL dropped 62% in 6 weeks. Lead volume up 3x.
- Fashion brand: ROAS 3.09→7.33 in 8 weeks. Purchases 111→216. Similar spend.

18 years in paid media. Expert-vetted. Case studies + client videos https://marketingo.com/testimonials

Offer:
Free Paid Ads Strategy Plan in 48h: short audit → 3 quick wins → optional 20-min call.

DM "LEADS" for B2B or "SALES" for eCommerce.

Andrei Haret

If you're comparing Google ROAS to Meta ROAS, you might be misleading your client. Here's how to align metrics.

Most agencies treat all ROAS the same.

That's a mistake.

Google and Meta serve different roles in the funnel. Comparing their ROAS 1:1 is like comparing a closer to a lead generator.

Here's why direct comparison breaks down:

1. Intent vs. interruption

Google captures demand. Meta creates it.
You can't expect the same conversion rate from someone searching "buy CRM software" vs. scrolling Instagram.

2. Attribution windows differ

Google defaults to 30-day click. Meta uses 7-day click, 1-day view.

Same conversion, different credit. Your "winner" might just have a longer window.

3. Conversion types aren't equal

Google drives bottom-funnel actions: demos, purchases, sign-ups.

Meta often captures top-funnel: content downloads, webinar regs, email opt-ins.

A $50 CPL on Google isn't the same as a $50 CPL on Meta if one converts at 30% and the other at 8%.

4. Incrementality is invisible

Meta might assist Google conversions without getting credit.
Someone sees your Meta ad, searches your brand on Google, and converts.

Google gets the ROAS. Meta gets blamed for "not working."

5. Blended metrics tell the real story

Track MER (marketing efficiency ratio): total revenue divided by total ad spend.

If Google ROAS drops but Meta scales and MER holds, you're winning.

What's your go-to metric when comparing channels?

We turn Google & Meta Ads into B2B meetings and eCommerce revenue | CEO, MarketinGO

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I saw this post about office life:

"Top 3 things you hear:
1. Did the money come in?
2. Anyone want something from the cafe?
3. Can you turn the music down?"

Made me smile.

Because my entire team works remotely.

From home. From coworking spaces. From cafes.

Sometimes I don't even know what country they're in.

So here's my version for remote teams:

1. You're on mute.
2. Can you see my screen?
3. The Wi-Fi just died.

I've heard all three in the same call before.

What's the most common phrase you hear on your team calls?

Google rolled out Analytics Advisor AI in GA4.

It's supposed to answer data questions in plain English.

Ask "What's my bounce rate trend?" and it pulls charts.
Ask "Why did traffic drop?" and it runs diagnostics.
Ask "Which channels convert best?" and it visualizes the answer.

Sounds useful on paper.

The real question: does it save time or just add another layer between you and the data?

Does it catch what manual analysis would miss?

Or does it give surface-level answers that still need validation?

I'm curious about the practitioner view here.

If you've used Analytics Advisor AI in GA4, what's your take?

Did it actually help you spot something faster?

Or did you end up going back to custom reports anyway?

Last week, we audited a B2B Google Ad account. Prospect asked: "Why do my remarketing campaigns have zero conversions?"

We pulled the Content Suitability report.

Their Display remarketing was running on YouTube kids channels. Cocomelon. Baby Shark. Toddler nursery rhymes.

The audience: 2-year-olds watching cartoons on their parents' devices.

I hear this a lot: "Don't exclude placements in remarketing. The user already visited your site. They know you."

True. But intent matters.

A site visitor watching Peppa Pig isn't in buying mode. They're not even the decision maker. They're a toddler.

Remarketing works when you layer intent with context.

Check your placements. Run the Content Suitability report. Exclude the noise.

Your remarketing budget should chase pipeline, not pacify toddlers.

What's the weirdest placement you've found eating your remarketing budget?

90% of GTM setups I audit have at least one of these issues. Quick checklist inside.

After 8 years managing paid media budgets up to $1M/month, I've seen the same GTM [Google Tag Manager] mistakes kill attribution over and over.

Here are the 3 that cost you the most:

𝗠𝗶𝘀𝘁𝗮𝗸𝗲 𝟭: 𝗙𝗶𝗿𝗶𝗻𝗴 𝘁𝗮𝗴𝘀 𝗼𝗻 𝘁𝗵𝗲 𝘄𝗿𝗼𝗻𝗴 𝘁𝗿𝗶𝗴𝗴𝗲𝗿

Your conversion tag fires on page load instead of form submit. You count visits as conversions. Your CPA looks great until you check the CRM.

𝗠𝗶𝘀𝘁𝗮𝗸𝗲 𝟮: 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻𝘀 𝘀𝗲𝘁𝘂𝗽

You installed the tag but skipped the user data variables. Your reporting becomes unreliable. Your optimization runs on incomplete data.

𝗠𝗶𝘀𝘁𝗮𝗸𝗲 𝟯: 𝗡𝗼 𝗤𝗔 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗶𝗻𝗴

You push changes live without preview mode testing. Tags break. Data gaps appear. You don't notice until the client asks why leads dropped.

𝗤𝘂𝗶𝗰𝗸 𝟭𝟬-𝗽𝗼𝗶𝗻𝘁 𝗚𝗧𝗠 𝗮𝘂𝗱𝗶𝘁 𝗰𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁:

1. All tags fire on correct triggers
2. Enhanced Conversions variables mapped
3. Consent mode tag fires first
4. Conversion linker tag active
5. No duplicate tags firing
6. UTM parameters captured
7. Form submissions tracked properly
8. Phone clicks tagged
9. Preview mode tested before publish
10. Version notes documented

Clean tracking beats creative genius every time.

What's the worst GTM mistake you've inherited from a previous agency?

Everyone wants to exit Meta's learning phase fast.
I used to think the same.

Until I realized the learning phase isn't always the enemy.

Here's what changed my mind:

The learning phase forces Meta to test delivery patterns before locking in.

For stable accounts with proven creative, yes, exit fast.

But for new accounts or fresh angles? Let it run.

I've seen campaigns hit their stride on day 6 of learning because Meta needed time to find the right audience pockets.

Here's my rule:

If you have conversion history and proven creative, optimize to exit learning fast. Stable budgets, clear signals, tight targeting.

If you're testing new offers or entering cold markets, give learning phase 7-10 days with steady budget. Let Meta map the terrain.

The mistake is treating every campaign the same.

Some need speed. Some need patience.

I've cut learning time to 3 days with proper setup.

I've also let it run 10 days and watched CPAs drop 40% after exit.

Context matters more than the phase itself.

What's your take on Meta's learning phase? Waste of budget or necessary evil?

graphical user interface, text, application

15 types of agency chaos that would be hilarious to imagine (if they weren't so real).

It's Friday, another week survived 😎

Here's a lighthearted break to laugh at scenarios we can all picture happening somewhere:

1. Pixel Panic: that heart-stopping moment when you realize the tracking pixel hasn't fired since 2022.

2. ROAS Delusion: when you keep refreshing the dashboard hoping the return magically doubles.

3. Campaign Amnesia: when you forget why you launched that ad set, but you're too deep in optimization to delete it now.

4. Attribution Anxiety: the existential crisis of trying to explain to a client why Google says 50 conversions and Meta says 4.

5. Creative Fatigue: when every new ad looks like the last one, just with a different emoji and slightly more authentic stock photo.

6. Audit Therapy: the strangely calming process of tearing apart someone else's ad account to remind yourself you're not that messy.

7. CPC Depression: that quiet sadness when your click cost triples overnight and no one knows why.

8. Landing Page Limbo: where conversions go to die slowly while everyone blames UX, copy, or the weather.

9. Learning Phase Denial: pretending everything's fine while Meta still hasn't exited learning for 3 weeks.

10. Budget Roulette: the art of moving $200 from one campaign to another like it's going to change your life.

11. Tracking Existentialism: realizing that in the cookieless future, maybe we never truly knew our users.

12. UTM Chaos: the spiritual pain of seeing one link tagged utm_source=fb and another utm_source=Facebook_ads_v3_finalFINAL.

13. Retargeting Déjà Vu: when you see your own ad for the 100th time and start to believe you should buy the product.

14. Conversion Therapy (Agency Edition): that moment when the only thing you can still optimize is your expectations.

15. Client Dashboard Zen: accepting that no matter how perfect your results are, the client will still say, so can we scale this?

Which one hit closest to home for you? 😅

LinkedIn Ads Expert | Ex-LinkedIn | Digital Account Manager | B2B Marketing

I help B2B teams turn LinkedIn Ads into a reliable growth channel.

After years at LinkedIn as a technician - and later leading strategy for 100+ clients at the world’s largest LinkedIn Ads agency - I’ve seen the same patterns repeat: 

Businesses treating LinkedIn like Google. Skipping the buyer journey. Expecting conversions from people who barely know them. 

Now, I run a Boutique LinkedIn Ads Consultancy for companies that want performance - and know it takes clarity, consistency, and coordination across marketing and sales. 

I work best with businesses that: 

– Know their audience 
– Have a clear offer and real differentiators 
– Value long-term growth over shortcuts 
– Create content their ICP actually cares about 
– Want strategy and execution working hand-in-hand 

I help clients build LinkedIn engines that support how people actually buy - with awareness, trust, relevance, and timing. 

If you’re navigating long sales cycles, complex buying committees, or just trying to make LinkedIn finally work - I’d be glad to talk. 

Message me to learn more!

Sheila Coque

I didn’t start my own LinkedIn Ads practice because I felt confident.
I started it mid-2025, working 10+ hours a day, 7 days a week,
including the months when I had 0 clients.

I did things I never imagined myself doing.
Sales, for one.
Accounting was worse (Brazilian bureaucracy is something else).

2025 wasn’t easy. Not even close.

I don’t think I could have done this
if I had kids.
If I had to carry the house alone.
If I didn’t have 15+ years of prior business experience to lean on.
If I wasn't working out as much. 

That context matters.

Still, here’s what the last 6 months look like on paper:

– 93 long-form posts written in 2025 (no AI)
– 100% inbound leads
– 100% lead → booked call
– 97% booked call → closed won
– 14 long-term agreements signed
– 01 client fired
– 09 one-time projects sold (audits and strategy work)
– 78% profit margin

I built this mostly on my own.
With some support in operations.
And one account management partnership I deeply trust.

Truthfully, a big part of this wasn’t about growth.
It was about proving something to myself.

Confidence has never been my default setting.
So I did what I know how to do: I built evidence.

At the same time, I wanted to learn what running a business actually means.
Not the social media version. The real one.

There’s still a lot I don’t know.
But I do feel a quiet sense of accomplishment.
And clarity.

Because 2026 will look different.

I’ll bring on more people.
More tools.
And I’ll step up my pricing.

I’m pretty sure those sky-high conversion rates have more to do with underpricing than magic. 

I have big plans for 2026.
And this time, I’m building them at the right altitude.

LinkedIn Ads Expert | Ex-Linkedin | Managed 110+ LI Ad Accounts | Lessons from Real LinkedIn Ads Campaigns

I recently took on a short-term project I normally would’ve declined.

• $1k/month ad budget
• Cold LinkedIn campaigns
• ~80k TAM in the US
• Expectation: ~200 leads via a contest-style subscription

Before starting, I was clear:
I’ve never seen that outcome happen under those constraints.

Still, I like being proven wrong. That’s how you learn.
So I made an exception and ran a short-term engagement to see how this would actually play out.

We did the work properly:

• Scrutinized the full audience
• Narrowed to the segment most likely to convert using the client’s own data
• Chose the most cost-efficient objectives and ad formats available
• Built content briefs based on proven winners from my own accounts (some with 30%+ CTR averages)
• Launched, monitored, and adjusted constantly

Once the data was statistically usable, we did what you’re supposed to do:

• Paused underperformers
• Refined demographics
• Launched retargeting from the small audience we’d built
• Optimized everything that could realistically be optimized

Performance tracked exactly where industry benchmarks said it would.
After ~4 weeks, CPL landed around $240.

That’s when the feedback came:
That the campaign was passively accepting underperformance.
That the failure was entirely on execution.

And look... LinkedIn Ads is humbling. I can take honest feedback.

But I can’t compress market education, trust building, and signal clarity into a few weeks and a very low budget.

I can’t fully eliminate cold-audience waste without a pre-qualified list.
That’s how cold media works.

This project didn’t teach me anything new about LinkedIn Ads.

But it reinforced a few things I keep seeing over and over:

• LinkedIn Ads is not a lead vending machine
• LinkedIn Ads doesn’t create demand out of thin air
• No amount of expertise can fix expectations that were never grounded in reality.

Over the past 7 months, a client invested $17,600 in LinkedIn Ads.
Rough math: ~$300K in revenue
That’s ~17x ROAS.

The main goal wasn’t lead gen.
It was simply to test the channel.

Here’s what came out of it:
• 39 sales-qualified conversations
• ~40% SQL → closed-won
• ~15 deals
• ~$20K average LTV per deal

There were:
• No lists
• No videos
• No lead magnets
• No CTAs on posts
• No distinctive brand assets
• No conversion-optimized website

What was in place:
• Native LinkedIn targeting
• Thought Leader Ads
• Strong operational discipline

But IMO, the real drivers sat outside the ad account:
• Clear product-market fit
• Existing, high category demand
• Competitors unsure who they actually serve
• Deep understanding of who buys and in which buying situations
• A very simple, opinionated offer
• Consistent founder-led content building memory over time

They were already winning before Linkedin Ads. 

LinkedIn Ads didn’t create that success.
But it did accelerate it.

I’ve never seen a massively successful LinkedIn Ads account that was struggling to acquire customers everywhere else.

Have you?

So when I hear:
“LinkedIn Ads are too expensive”
“LinkedIn Ads don’t work”

I can’t help but think:

Maybe LinkedIn Ads isn’t the main problem.
Maybe LinkedIn Ads is being asked to solve something that isn’t theirs to fix.

If ads feel too hard, it’s often because the business is.

One thing becomes painfully clear when you run LinkedIn Ads:
often the hardest part isn’t performance.
It’s proving it.

Seven months in, working with my own first clients, that reality hits hard.

Marketing science is pretty clear on one thing:
new paid channels don’t reveal efficiency first.
They reveal patterns.

But businesses don’t want patterns.
They want to know their Return on Ad Spend... 

The convo is always the same:
“[whatever you say here], but what does our ROAS look like?”

And if you’ve worked in B2B, you know things get messier the moment attribution enters the room.

LinkedIn Ads shows a booked call.
HubSpot says “Direct traffic”.
Sales says “They already knew us”.

No one is lying.
This is just how buying actually works.

That’s one reason I’ve been suggesting clients test Fibbler.

Not to manufacture ROAS.
But to give ad exposure the credit it usually loses in this fight.

Sometimes, that’s all you need.
More context.

One recent example surprised a lot of people.

Even after seeing screenshots of impressions served to high-value accounts, doubt remained.

As we say in Brazil: “Santo de casa não faz milagre.”
Roughly, a prophet is never believed in their own land.

Only after connecting Fibbler did the conversation change.
$573,259 in LinkedIn Ads–influenced revenue tends to do that...

It doesn’t end the debate forever.
But it gives you leverage in an attribution fight many of us are soooo tired of having.

And after that?
For now, it’s just back to work.

A lot of LinkedIn ABM motions break down even when best practices are followed.

Not because of creative, campaign objectives, or other tactics.
But because uneven impression distribution leaves large parts of the account list effectively invisible.

This post is about that invisibility.
The accounts that never see the brand enough to be remembered when buyers are actually in-market.

Leo Torres and I are redesigning a ABM strategy for a high-ACV, low-volume B2B business, and early data forced a rethink.

Not because results were bad.
But because once we joined paid impression history with deal outcomes, the story stopped matching how ABM is often executed.

What we found was uncomfortable enough that I repeated the same analysis across my five largest clients, just to see if the pattern held.

It did.

This work was grounded in:
- CRM deal history
- Client feedback
- LinkedIn company-level delivery data
- Buyer-journey analysis (via Fibbler), connecting exposure patterns to real decision timing

First, none of this is a new discovery.

Decades of research in advertising and behavioral science show that memory and trust build through repeated, spaced exposure, while returns flatten as repetition increases.

Second, this is still a small dataset inside an auction system, so we’re careful about causality.

What we've found: 

When paid impression history was joined with deal outcomes, a consistent directional pattern emerged across all of them:

1. A small subset of target accounts absorbed a disproportionate share of paid impressions

2. A large portion of the ABM list remained materially underexposed

3. The largest share of closed-won deals did not come from the most-impressed accounts

4. The high-impression tail skewed toward losses or stalled deals
(association, not causation)

5. Wins did not require extreme paid exposure

6. The “sweet spot” consistently sat in a lower-to-moderate exposure range
(it varied by client, but generally landed around ~300–800 impressions per account)

This is not proof that high-impression accounts were “over-sold.”
And this is not an argument against frequency.

What it does show is something more structural:
In the analyzed cases, a large portion of the TAM never received enough exposure to fairly evaluate the offer at all.

The data supports:
- Diminishing returns at high concentration
- Real concentration risk from oversaturation
- No evidence that forcing accounts into high-frequency bands improves outcomes

For these accounts, performance was constrained by something  basic:
uneven exposure across the account universe.

If most of your TAM is systematically invisible, continuing to optimize everything around that invisibility is unlikely to change outcomes in a meaningful way.

Tomorrow, I’ll share how this insight forced us to rethink how cold ABM campaigns are structured in the first place, not just how they’re optimized.

Do you often do this exposure audit across your ABM accounts yet?

Joining LinkedIn paid impression data with deal outcomes broke our 2025 ABM playbook.

We stopped asking:
“Should we increase TAM, ad frequency, content diversity, formats?”

And started with something more basic first:

- Who has actually seen us enough to evaluate the offer?
- And what does “enough” mean for this business?

(This post builds directly on the exposure post I shared yesterday).

Those questions changed how we structure cold LinkedIn Ads ABM.

Impressions stopped being treated as a volume metric.
They became a coverage signal, analyzed at the company level, over rolling time windows.

From there, we stopped optimizing campaigns in isolation and started segmenting accounts by real paid exposure.

We're keeping the structure intentionally simple:

- Cluster 01 - Underexposed
Not enough repetition to reasonably evaluate the offer.
No shared familiarity. No internal justification yet.

- Cluster 02 - Decision band (anchored in closed-won CRM data)
Sufficient exposure for recognition, confidence-building, and buying-group alignment.
This is where most closed-won deals showed up.

- Cluster 03 - Saturated
Diminishing marginal value unless new signals appear
(e.g. a new buying trigger).

One important clarification:

This doesn’t mean exposure cause the outcome.
It means insufficient exposure makes meaningful evaluation unlikely.

Execution doesn’t need to be complex:

– We're using dynamic company audiences in LinkedIn Ads Manager
– Let exposure tiers (through the paid impression filter) drive inclusion and exclusion logic

What does change is how success in the cold layer is evaluated:

– Account coverage becomes a primary health signal
– Over-delivery is actively suppressed where marginal value drops (based on your sales data)
– Spend is more concentrated during plausible decision windows, not evenly over time

Now, this isn’t a copy-paste framework.

Your market, ACV, audience, sales motion, buying cycle, sales data will differ.

But if you’ve tried multiple tactics and your ABM motion still doesn’t take off, audit your company list exposure distribution.

If most target accounts haven’t seen you enough to form a view, no amount of downstream optimization will fix that.

That lens alone is worth carrying forward.

We’re still testing this model.
If it holds, we’ll share the performance comparison.
If it doesn’t, we’ll keep following the data and adjusting. 

Do you already run ABM like this, how are the results showing up for you?

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The Battle

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Sheila Coque

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